school of biomedical sciences charles sturt university ......factors, such as mannose binding lectin...
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School of Biomedical Sciences
Charles Sturt University
Rheumatoid Arthritis and Risk of Infection: The Role of
Disease-Modifying Anti-inflammatory Drugs
Hamid Reza Ravanbod
MBBS, M.Sc. (Public Health), M.Sc. (Podiatric Surgery)
Submitted in partial fulfilment of the requirements for the degree of
Doctor of Philosophy
First submitted August 2019
Revised March 2020
i
CERTIFICATE OF AUTHORSHIP
Hamidreza Ravanbod
ii
PUBLICATIONS FROM THIS WORK
1. Hamid Reza Ravanbod, H.R., Jazayeri, J.A., Russell, K.G., and Carroll, G.J. (2017).
Serious infections in rheumatoid arthritis and strategies for their prevention - A review
and discussion of implications for clinical practice. Journal of Immunology, Infection
& Inflammatory Diseases, 2(3). https://scientonline.org/open-access/serious-infections-
in-rheumatoid-arthritis-and-strategies-for-their-prevention-a-review-and-discussion-
of-implications-for-clinical-practice.pdf
2. Serious and non-serious infections in recipients of conventional synthetic and biologic
DMARDs in rheumatoid arthritis; an examination of self-reported data from the ARAD
registry (in preparation).
iii
ETHICS APPROVAL
This research was approved by the Human Research Ethics Committee (HREC), Charles Sturt
University. Protocol number: 2014/080
(Appendix L).
iv
ACKNOWLEDGEMENTS
I would like to express my sincere thanks and gratitude to my supervisors, Dr. Jalal Jazayeri
(CSU, principal supervisor) and Dr. Graeme Carroll (UWA) for their guidance and supports
during this research. I also wish to express my gratitude to late Professor Kenneth Russell for
his expert advice and contributions to the statistics in the fifth chapter. While this PhD research
has never been easy, it has always been a privilege to undertake it. Thank you, both, for helping
me along the way.
I would also like to thank Dr Sandra Savocchia, Dr Christopher Scott, Ms. Vibhasha Chand,
and Mr Abishek Santhakumar for their various advice or assistance with technical matters.
I would like to acknowledge Kara Gilbert for proofreading this thesis, in accordance with the
ethical standards for editing and proofreading contained in the Australian Standards for
Editing Practices (2nd ed.) (2013) as set out by the Institute of Professional Editors (IPEd) in
relation to editing and proofreading research these.
Special thanks to my family, parents, and my children, who supported me and gave me time to
finish this project, and to my employers, who provided me with an ongoing income to support
my family during my university studies.
v
LIST OF ABBREVIATIONS
ABT: Abatacept
ACPA: Anti-citrullinated peptide antibody
ACR: American College of Rheumatology
AD: Anno Domini
ADA: Adalimumab
AIC: Akaike information criterion
AIRR: Annualised internal rate of return
ANC: Absolute neutrophil count
APC: Antigen-presenting cells
APRIL: A proliferation-inducing ligand
ARAD: Australian Rheumatology Association Database;
bDMARDs: Biologic disease-modifying anti-rheumatic drugs
BJM: Bone, joint, muscle
BMI: Body mass index
BSRBR: British Society for Rheumatology Biologics Register
CABG: Coronary artery bypass grafting
CCP: Cyclic citrullinated peptide,
CHD: Coronary heart disease
CMV: Cytomegalovirus
CNS: Central nervous system
COPD: Chronic obstructive pulmonary disease
CoQ10: Coenzyme Q 10
CRP: C-reactive protein
CS: Corticosteroid
csDMARDs: Conventional synthetic biologic disease-modifying anti-rheumatic drugs
CSF: Colony-stimulating factor
CSU: Charles Sturt University
CV: Cardiovascular
CVID: Common variable immunodeficiency
CYA: Cyclosporine A
DF: Degree of freedom
DM: Diabetes mellitus
vi
DMARD: Disease-modifying anti-rheumatic drugs
DREAM: Dutch Rheumatoid Arthritis Monitoring Registry (Netherlands)
EENT: Eye, ear, nose, throat,
EOW: Every other week
ESR: Erythrocyte sedimentation rate
ETN: Etanercept
GAG: Glycosaminoglycans
GCONV: Global convergence variable
GDR: German RABBIT Registry Review
GISEA: Registry (Italian Group for the Study of Early Arthritis)
GIT: Gastrointestinal tract (GIT)
GM CSF: granulocyte-macrophage colony-stimulating factor
HAQ Score: Health assessment questionnaire score
HB: Hepatitis B
HCQ: Hydroxychloroquine
HLA: Human leukocyte antigen
HREC: Human Research Ethics Committee
T1DM: Insulin-dependent diabetes mellitus
IHD: Ischemic heart disease
ILD: Interstitial lung disease
IM: Intramuscular
IMIDs: Immune-mediated inflammatory diseases
INX: Infliximab
IR: Incidence rate
IRR: Incidence rate ratio
IUIS: International Union of Immunological Societies
IV: Intravenous
JAK: Janus kinase inhibitors
-2 Log L: Deviance in the model
LDA: Low disease activity
LEF: Leflunomide
lr: Likelihood ratio
LRTI: Lower respiratory tract infection
MBDA: Multi biomarker disease activity
vii
MBL: Mannose binding lectin (MBL)
MCP: Metacarpophalangeal
MCSF: Macrophage colony-stimulating factor,
MHDA: Moderate to high disease activity
MI: Myocardial infarction
MMPs: Matrix metalloproteinases
MSK: Musculoskeletal
MTP: Metatarsophalangeal
MTX: Methotrexate
NF-KB: Nuclear factor kappa-Β,
T2DM: Non-insulin dependent diabetes mellitus
NK: Natural killer cell
NSAIDs: Non-steroidal, anti-inflammatory drug
NTM: Non-tuberculous mycobacterial
OCP: Oral contraceptive pill
OIs: Opportunistic infections
PG: Proteoglycan
PIP: Proximal interphalangeal
PML: Leukoencephalopathy
PRISMA: Preferred reporting items for systematic reviews
PYs: Person-years
RA: Rheumatic arthritis
RABBIT: Rheumatoid arthritis (RA) observation of biologic therapy
RANKL: Receptor activator of nuclear factor kappa-Β ligand
RCT: Randomised control (or controlled) trial
RF: Rheumatic factor
RTX: Rituximab
RX: A medical prescription
SAS software: Statistical Analysis System software
SC: Schwarz criterion
SD: Standard deviation
SERENE: Study evaluating rituximab’s efficacy in MTX iNadequate rEsponders
SI: Serious infection
SIE: Serious infection event
viii
SSTIs: Skin and soft tissue infections
TB: Tuberculosis
TCZ: Tocilizumab
TKI: Tyrosine kinase inhibitor
TNF-α: Tumour necrosis factor-α
TNFI: TNF inhibitor
TOF: Tofacitinib
UK: United Kingdom
US: Ultrasound
USA: United States of America
UTI: Urinary tract infection
UWA: University of Western Australia
ix
WHOLE THESIS ABSTRACT
The development of infection is far more common in rheumatoid arthritis (RA) patients than in
the general population. It is probably one of the most important consequences of RA. It is shown
that RA can also increase the rate of serious infection (SI), from less than one per hundred
patient years (100PYs) in the normal population to around five per 100PYs in the RA
population. The risk of infection in RA increases due to several factors. Some of these include
(i) the nature of RA disease and the pathophysiological changes in the immune system, (ii) RA
medications, a number of which suppress the immune system, and (iii) coexisting genetic
factors, such as Mannose binding lectin (MBL) deficiency, which increases the risk of
immunodeficiency through well-known or unknown mechanism(s).
In this project, data were collected from the Australian Rheumatology Association Database
(ARAD), in which a cohort of 3569 RA patients (960 males and 2609 females), who had
completed related questionnaires 28176 times (during 200 to 2014) were investigated for the
development of infections. Among the 3569 patients, 459 patients were eliminated because they
had filled out the questionnaire only once, after which 3110 patients remained. Eight duplicates
were eliminated, leaving 27709 visits from 3110 patients. All these visits were examined, to
capture self-reported infections in different organs and the medications that were being taken
at the time. ARAD reports were statistically analysed using the Chi-square test, Fisher’s exact
test and logistic or multinomial logistical regression modelling. The thesis is divided into five
chapters:
Chapter 1 provides a detailed overview of the entire thesis, including a comprehensive
background of the topic and the project hypothesis, goals, objectives, and strategies.
Chapter 2 outlines a comprehensive systematic review in which the implications of the
development of infection in RA patients and strategies for the prevention of infection are
discussed. This chapter was published as a review article in 2017. This chapter provides a
background on the subject of this thesis and provides a comprehensive review of the relevant
studies that have been undertaken in this area.
Chapter 3 outlines a descriptive analysis of the infection status of RA patients, in which the
role of disease modifying anti-inflammatory drugs (DMARDs) are investigated. ARAD
x
reports are examined with respect to demographic and treatment categories. Observed
differences were then subjected to descriptive statistical appraisal. This chapter is an
introduction to the more complex inferential analysis outlined in chapter 4.
Chapter 4 outlines an inferential analysis of the association between the risk of infection and
each anti-RA medication. The analysis provides valuable information concerning the
relative frequency of self-reported infections in users of diverse anti-rheumatic therapies.
Various organs, including eyes, ears, nose, throat, lungs, urinary tract, heart, gastrointestinal
tract, and the central nervous system (CNS) are examined, as well as systemic infections of
a viral and pyogenic nature (sepsis /septicaemia). This provides an introduction to the use of
adjusted equations for predicting the risk of infection, presented in the next chapter.
Chapter 5 presents more complex assessments around the incidence of serious infection, its
demographic characteristics, and potential risk factors. Patient reports taken from 27709
visits by 3110 patients during 2001 to 2014 were searched for evidence of hospitalisation or
intravenous (IV) infusion for infection. Resultant data were tested using inferential and
descriptive analyses, and odds ratios for potential risk factors were calculated. A few studies
indicate that RA disease and anti RA medication can specifically increase the risk of serious
infections. Serious infection (SI) is still the number one cause of death in RA, globally, and
so investigating the basis for SIs is important because of the risk of immediate mortality,
ongoing morbidity, and health economic burdens. Moreover, an increased understanding of
SIs may lead to the development of improved strategies for the prevention of infection. In
Chapter 5, serious infection, with all its potential risk factors, is discussed and analysed in
detail.
Based on the systematic literature research, we have found that SI is far more common in RA
than in the general population. In addition, anti RA medications have different impacts on
serious infections, with corticosteroids demonstrating a huge impact on infection followed by
bDMARDs and csDMARDs. The time of prescribing bDMARDs in the first year or after,
higher dosage of bDMARDs, and combination therapy with bDMARDs all increase the risk
of infection. Although it seems that, in the Australian database, csDMARDs alone, during
prescription, can evoke higher rates of infection than bDMARDs alone; this difference is
statistically significant in self-reports of heart infection, lung infection (p-value = 0.0156),
urinary system infection (p-value = 0.0002), and GIT infection. Both csDMARDs and
xi
bDMARDs are associated with a higher risk of infection in RA. All in all, without isolating
the first year of taking bDMARDs, it seems that bDMARDs causes less infection but more
serious infection. The impact of various medications on infection depends on the type and
severity of infection.
xii
TABLE OF CONTENTS
Certificate of authorship ................................................................................................................................. i
Publications from this work ........................................................................................................................... ii
Ethics approval ............................................................................................................................................. iii
List of Abbreviations ...................................................................................................................................... v
Whole Thesis Abstract .................................................................................................................................. ix
Table of Contents ....................................................................................................................................... xiii
chapter 1 ....................................................................................................................................................... 1
Abstract ................................................................................................................................. 2
1. Introduction ...................................................................................................................... 3
1.1 Overview .................................................................................................................................................. 3 1.2 Overview of the thesis rationale ............................................................................................................. 3 1.3. Background information ......................................................................................................................... 3
1.3.1. Rheumatoid arthritis ......................................................................................................... 3
1.3.2. Diagnosis and prevalence in RA ....................................................................................... 4
1.3.3. Consequences and medication in RA ............................................................................... 5
1.3.4. Pathophysiology in RA ..................................................................................................... 5
1.4. Molecular pathogenesis ......................................................................................................................... 6 1.4.1. Mechanism of actions of bDMARDs and csDMARDs .................................................... 7
1.4.2. Mechanism of action of bDMARDs ................................................................................. 8
1.4.3. TNFα ................................................................................................................................ 9
1.4.4. TNFα inhibitors ................................................................................................................ 9
1.5. Major risk factors .................................................................................................................................. 11 1.6. Signs and symptoms and laboratory tests ............................................................................................ 11 1.7. Complications ....................................................................................................................................... 13 1.8. Moderate and serious infections .......................................................................................................... 14 1.9. Medical treatment ................................................................................................................................ 15
1.9.1. Medication and risk of infection in the literature ............................................................ 17
1.10 Discussion ............................................................................................................................................ 19 1.11 Organisation of this thesis ................................................................................................................... 23 1.12 Hypotheses to be examined in this thesis ........................................................................................... 24 1.13 Significance of undertaking this review ............................................................................................... 25
2. Methods ........................................................................................................................... 25
3. Summary of the Results ................................................................................................. 26
3.1. Strengths of this research ..................................................................................................................... 27
xiv
3.2. Limitations ............................................................................................................................................ 28
4. Conclusion ....................................................................................................................... 28
References ........................................................................................................................... 30
Chapter 2 .................................................................................................................................................... 37
Abstract ............................................................................................................................... 38
1. Introduction .................................................................................................................... 39
2. Methods ........................................................................................................................... 40
2.1. Search strategy and selection criteria .................................................................................................. 40
3. Results and discussions .................................................................................................. 40
3.1. Study selection ..................................................................................................................................... 40 3.3. Risk factor categories ........................................................................................................................... 44 3.4. The impact of medications (non‐biologics) .......................................................................................... 45 3.5. Corticosteroids ..................................................................................................................................... 46 3.6 Synthetic DMARDS ................................................................................................................................ 46 3.7 The impact of medications (biologics) ................................................................................................... 48 3.8. TNF‐α Inhibitors .................................................................................................................................... 48 3.9. Abatacept (ABT), Rituximab, Anakinra, Tofacitinib and Tocilizumab ................................................... 49 3.10. Risks associated with combination therapies ..................................................................................... 51 3.11. Tuberculosis (TB) and non‐tuberculous mycobacterial (NTM) infections .......................................... 52 3.12. Serological and other laboratory parameters that influence SI risk ................................................... 52 3.13. Mannose Binding Lectin (MBL) and other immune deficiencies ........................................................ 52 3.14. Implications for Clinical Practice ......................................................................................................... 54
3.14.1. Age ............................................................................................................................... 54
3.14.2. Corticosteroid (CS) Use and Dosage ............................................................................ 54
3.14.3. Doses of biologic agents ............................................................................................... 54
3.14.4. Vaccination ................................................................................................................... 55
3.14.5. Comorbidities related and unrelated to RA .................................................................. 55
4. Conclusion ....................................................................................................................... 55
References ........................................................................................................................... 57
Chapter 3 .................................................................................................................................................... 63
Abstract ............................................................................................................................... 64
1. Introduction .................................................................................................................... 66
1.1. DMARDs ................................................................................................................................................ 68 1.2. bDMARDs .............................................................................................................................................. 68 1.3. Aims and Objectives ............................................................................................................................. 69
2. Methods ........................................................................................................................... 70
2.1. Data Collection ..................................................................................................................................... 70 2.2. Statistical Analysis ................................................................................................................................ 70
3. Results and discussions .................................................................................................. 70
xv
3.1. Demography of whole RA population .................................................................................................. 70 3.2. Demography of patients taking purely bDMARDs ................................................................................ 71 3.3. Demography of patients receiving csDMARDs alone ........................................................................... 74 3.4. Comparison of patients receiving bDMARDs and patients on csDMARDs alone ................................. 77
3.4.1. Prednisolone comparison ................................................................................................ 78
3.4.2. Alcohol comparison ........................................................................................................ 79
3.4.3. Smoking comparison ...................................................................................................... 80
3.4.4. Sex distribution comparison ........................................................................................... 82
3.4.5. T2DM comparison .......................................................................................................... 85
3.4.6. T1DM comparison .......................................................................................................... 86
3.4.7. Skin and nail infections comparison ............................................................................... 87
3.4.8. Eyes, Ears, nose, Throat (EENT) Infections – a comparison ......................................... 89
3.4.9. Heart infections comparison ........................................................................................... 91
3.4.10. Lung infections comparison ......................................................................................... 92
3.4.11. Gasterointestinal tract (GIT) infections ........................................................................ 95
3.4.12. Urinary tract infections (UTI) ........................................................................................................... 97 3.4.13. Musculoskeletal infections (MSK) ................................................................................................... 99 3.4.14. Artificial joint infections ................................................................................................................ 102 3.4.15. Nervous system infections ............................................................................................................ 103 3.4.16. Tuberculosis (TB) infection ............................................................................................................ 103 3.3.17. Blood infections ............................................................................................................................. 104 3.4.18. Viral Infections ............................................................................................................................... 106 3.5. Chapter discussion and conclusion ..................................................................................................... 108
References: ........................................................................................................................ 117
Chapter 4 .................................................................................................................................................. 125
Abstract ............................................................................................................................. 126
1. Introduction .................................................................................................................. 128
1.1. Aims .................................................................................................................................................... 131 1.2. Hypothesis .......................................................................................................................................... 131
2. Methods ......................................................................................................................... 132
2.1 Data Collection .................................................................................................................................... 132 2.2. Statistical Analysis .............................................................................................................................. 132
3. Results and Discussions ............................................................................................... 132
3.1. Different organ infections .................................................................................................................. 135 3.2. Eye, Ears, Nose and Throat (EENT) infection ‐ analysis of Anti‐RA medicines .................................... 136
3.2.1. Wald Chi-square, Likelihood ratio test and Score test to test significance of differences
................................................................................................................................................ 137
3.2.2. Effects of medications on Eye Ear Nose and Throat (EENT) infection ....................... 137
3.3. Chest or lung infection ‐ analysis of anti‐RA medicines ...................................................................... 142 3.3.1. Wald Chi-square, likelihood ratio test and score test to test significance of differences
................................................................................................................................................ 143
3.3.2. Effects of different medications on lung infection ....................................................... 143
xvi
3.4. Skin and Nail infection ‐ analysis of Anti‐RA medicines ...................................................................... 150 3.4.1. Effects of different medications on skin and nail infection .......................................... 151
3.5. Artificial (Prosthetic) Joint infection ‐ analysis of Anti‐RA medicines ................................................. 156 3.5.1. Wald Chi-square, Likelihood ratio test and Score test to test significance of differences
................................................................................................................................................ 158
3.5.2. Effects of different medications on artificial (prosthetic) joint infection ..................... 158
3.6. Bone, joint and muscle (BJM) infection ‐ analysis of anti‐RA medicines ............................................ 162 3.6.1. Wald Chi-squared, Likelihood ratio test and Score test to test significance of differences
................................................................................................................................................ 163
3.6.2. Effects of different medications on bone, joint and muscle infection ........................... 163
3.7. Blood infection ‐ analysis of Anti‐RA medicines ................................................................................. 170 3.7.1. Wald Chi-square, Likelihood ratio test and Score test to test the significance of
differences .............................................................................................................................. 171
3.7.2. Effects of different medications on blood infection ..................................................... 171
3.8. Gastro‐intestinal tract infection ‐ analysis of medication confounders ............................................. 176 3.8.1. Wald Chi-square, Likelihood ratio test and Score test ................................................. 178
3.8.2. Effects of different medications on GIT infections ...................................................... 178
3.9. Nervous System infection ‐ analysis of medication confounders ....................................................... 182 3.10. TB infection ‐ analysis of medication confounders ........................................................................... 183 3.11. Urinary tract infection ‐ analysis of medication confounders .......................................................... 184
3.11.1. Wald Chi-square, Likelihood ratio test and Score test to test significance of differences
................................................................................................................................................ 186
3.11.2. Effects of medications on Urinary tract infection ....................................................... 186
3.12. Viral infection ‐ analysis of medication confounders ....................................................................... 193
3.12.3 Chapter Conclusion ............................................................................................... 198
References: ........................................................................................................................ 203
Chapter 5 .................................................................................................................................................. 206
Abstract ............................................................................................................................. 207
1. Introduction .................................................................................................................. 208
1.1. Aims .................................................................................................................................................... 209 1.2. Hypothesis .......................................................................................................................................... 210
2. Methods ......................................................................................................................... 210
2.1. Data Collection ................................................................................................................................... 210 2.2. Statistical Analysis .............................................................................................................................. 211
3.0 Results and discussion ................................................................................................ 212
3.1. Analysis of Rheumatoid Arthritis (RA) and Serious Infections (SIs) in Australia ................................. 212 3.2. Age and gender ................................................................................................................................... 215 3.3. Length of time in the program ........................................................................................................... 217 3.4. Time in the program as a function of Gender .................................................................................... 218 3.5. Distribution of age groups .................................................................................................................. 218 3.6. Incidence and rate of SIs. .................................................................................................................... 219 3.7. Incidence of SIs ................................................................................................................................... 220
xvii
3.7.1. Rates of serious infections ............................................................................................ 221
3.7.2. Predictor variables ........................................................................................................ 222
3.8. Prediction of Serious infection ........................................................................................................... 224
4. Discussion ...................................................................................................................... 227
5. Chapter conclusion ....................................................................................................... 230
Thesis summary and Remarks .................................................................................................................... 232
Summary of main findings ......................................................................................................................... 233 Concluding remarks ................................................................................................................................... 236
References: ........................................................................................................................ 238
Appendices ............................................................................................................................................... 241
Description of data in appendix ...................................................................................... 243
Taking different medication levels ............................................................................................................ 243 Response levels ......................................................................................................................................... 243
Appendix A: Output of SAS for EENT Infection ........................................................................................... 244
Appendix B: OUTPUT of SAS for Lung Infection .......................................................................................... 268
Appendix C: Output of SAS for Nail and skin infection ................................................................................ 301
Appendix D: Output of SAS for artificial joint infection ............................................................................... 328
Appendix E: Output of SAS for bone muscle joint infection ........................................................................ 351
Appendix F: Output of SAS for blood infection ........................................................................................... 385
Appendix G: Output of SAS for GIT Infection .............................................................................................. 411
Appendix H: Output of SAS for Nervous system infection ........................................................................... 433
Appendix I: Output of SAS for TB infection ................................................................................................. 461
Appendix J: Output of SAS for Urinary Tract Infection ................................................................................ 485
Appendix K: Output of SAS for viral infection ............................................................................................. 509
Appendix L: Ethical approval for the thesis ................................................................................................ 535
APPENDIX M: Sample of ARAD questionnaire ............................................................................................ 536
1
CHAPTER 1
Introduction and overview
2
Abstract
Objective: To provide a comprehensive background to the project and to summarise the goals
and approaches of this thesis.
Methods: After a systematic review, ARAD patients’ records from 2001to 2014 were tested
using a series of descriptive and inferential statistical analysis. Initially the data was once
divided to (i) those with serious infection and those with non-serious infection, Then the
development of serious infection was evaluated in patients taking bDMARDs and compared
with those who were taking csDMARDs. Afterward in each section, these groups were
compared for their features and risk of infection.
Results: In the systematic review 31 articles met the criteria for further analysis and showed
increased association of serious infection with taking prednisolone, bDMARDs and to a lesser
extent csDMARDs. The risk of infection is reported to be higher in the first year of taking
bDMARDs compared to the following years.
ARAD data is analyzed by a series of descriptive and inferential analyses. In the descriptive
analysis the mean age for RA patients was found to be 61.47; for the group taking csDMARDs
it was 59.24 and for those taking bDMARDs it was 62.62 years respectively. ENT infections,
with a frequency of 14.75%, were the most common infection type in RA. Heart infection, lung
infection, urinary tract infection, and GIT infection were statistically more frequent in users of
csDMARDs compared to bDMARDs. Cyclosporine and Prednisolone were almost associated
with all types of infections in RA. Age, gender, alcohol consumption, etc. are potentially
associated with increased risk of SIs.
Conclusion: Based on the systematic research, SI is far more common in RA than in the
general population. Based on ARAD data, for most types of infection, csDMARDs alone are
associated with higher rates of diverse infection, whereas bDMARDs alone are more strongly
associated with serious infections.
According to the ARAD analysis, the most common infection in RA in Australia is EENT
infection (14.75%). The risk of any serious infection is almost 2.92% in ARAD and for females
this risk starts at younger ages. Among various risk factors, smoking is linked to serious
infections.
3
1. Introduction
1.1 Overview
This chapter presents a summary of key information important to the rationale for the thesis.
Information about rheumatoid arthritis (RA) is presented, followed by background information
about infection in RA. Biologic DMARDs and csDMARDs are defined and their role in the
treatment of RA, together with their capacity to predispose to infection, is outlined. The role of
different risk factors in increasing the risk of infection is briefly reviewed, followed by
background information about medications. The aims, hypotheses, and significance of the study
conclude the introduction.
1.2 Overview of the thesis rationale
Linkages between RA and serious infection have been hypothesised and continue to be refined
as our understanding of RA, its pathogenesis and methods of treatment continue to evolve. A
growing body of research indicates that sometimes using effective treatments, such as
bDMARDs, is associated with unwanted effects, including minor and major infections, some
of which are serious and can be life-threatening or fatal[1].
1.3. Background information
1.3.1. Rheumatoid arthritis
According to Arthritis Australia, RA is an autoimmune disease which causes swelling and pain
of the joints. RA disease causes inflammation and joint damage in the smaller joints in the
hands and feet, through damage to the lining of the joints. Rarely, in RA, larger joints, such as
the knees and hip joints, can also be affected, too [1]. Symptoms vary from person to person
and may include symmetric joint pain, swelling and tenderness, with morning stiffness [2].
RA is usually diagnosed from its symptoms, a physical examination and testsm such as blood
tests for inflammatory factors and antibodies (anti-CCP), including rheumatoid factor. X-rays
can also help to see if joints are damaged[3].
4
1.3.2. Diagnosis and prevalence in RA
Historically, the precise time at which RA emerged is difficult to determine and is mainly based
on both assumptions and empirical analysis; however, it seems that RA is mainly a disease of
the modern world. Probably the earliest evidence of RA start from portraits by artists of the
Flemish school, during the mid-15th to early 16th centuries. These depictions hint at the
existence of rheumatoid-like deformities in the European models used by these artists[4].
According to Australian guidelines, the diagnosis of RA is made on the basis of clinical
presentation, in association with autoantibodies and evidence of systemic inflammation.
Common features of RA are discussed in the following sections.
Common features of rheumatoid arthritis [5]:
Early morning stiffness for longer than one hour
Family history of inflammatory arthritis
Joint swelling in more than five joints and symmetry of the affected area
Rheumatoid factor positivity, compression tenderness in hands and feet
Anti CCP positivity, chronicity of symptoms for more than 6 weeks
Bony erosion apparent in X-rays of the hands, wrists and feet
Presence of rheumatoid nodules and raised inflammatory markers (ESR and CRP)
Systemic features, such as fatigue and weight loss, are relatively common
The incidence of RA is generally variable and, overall, the number of people affected by RA is
not large. Among all countries, Japan and France have the lowest incidence rates of 8 per
100,000 and 8.8 per 100,000, respectively, while the highest incidence rate is reported to occur
in the United States, with 44.6 per 100,000. Rheumatoid arthritis incidence rates may alter
marginally, as they are exaggerated by time of reporting and the gap between symptom onset
and report to a population-based registry[6].
The prevalence of RA among blacks is lower compared to whites. Although the prevalence of
RA among the black population is lower, there is no evidence that the disease manifestations
differ. However, there is even some evidence to suggest that RA in blacks is less severe, in
5
terms of extra-articular manifestations and disability. Rheumatoid arthritis is also more
common in women and is relatively uncommon in young men (<35 years). The French Afro-
Caribbean population have specific manifestations of the disease, such as high female
predominance, high immune seropositivity and low tobacco use. As these data are extracted
from France, which has a mixed population, it seems that geographical varieties play a small
role in these differences[7]. In another study in North America, the prevalence of RA among
Eskimos has been also reported as 0.8 % (the method of diagnosing RA in this study was based
on clinical signs and symptoms plus serology, without the benefit of x-rays)[8]. Also, the
prevalence rate of RA among Chinese people is different. In a study by Zeng et. al. (2002) the
prevalence rate of RA in China is almost 0.2-0.3% of the population[9].
1.3.3. Consequences and medication in RA
Rheumatoid arthritis (RA) has been a major cause of disability and loss of productivity among
different populations, including Australians[10]. Multiple comorbidities and extra-articular
disease manifestations may accompany rheumatoid arthritis (RA) [10].
Prior to 1990 or thereabouts, a conventional multi-disciplinary management approach to this
chronic disease was usually followed. Increasingly, in the last three decades, the emphasis has
shifted to the use of multiple and increasingly sophisticated pharmacological measures, with
early and aggressive management strategies advocated. One of the disadvantages of this
approach has been a rise in the rates of non-serious and also serious infections in a not
inconsiderable subset of RA patients receiving these new treatments [10].
1.3.4. Pathophysiology in RA
In rheumatoid arthritis (RA), the site of the initial inflammatory process is the synovial lining
of diarthrodial joints. In these joints, usually synovial fluid is the source of food for the articular
cartilage and lubricates the cartilage matrix. During the inflammatory process, the synovial
tissue undergoes increased vascularization and infiltration by activated macrophages,
lymphocytes, and plasma cells. As the disease advances, a pannus forms from the progressive
overgrowth of this tissue, which then threatens the adjacent cartilage and bone (6).
Although the aetiology and pathogenesis of RA have yet to be completely elucidated, several
factors have been identified that contribute importantly to the disease process. These factors
6
include genetic contributors, environmental factors, the inter- action of genes and environment,
and cellular abnormalities. A series of immune system factors such as tumour necrosis factor
(TNF), have been identified which, when induced, is elaborated and then interacts with target
cells, and appears to drive inflammation and tissue damage. Accordingly, medications, such as
infliximab (Remicade), adalimumab (Humira), etanercept (Etanercept), certolizumab pegol
(Cimzia), and golimumab (Simponi), have been produced which can prevent the interaction of
TNF with its endogenous receptors or bind and neutralize their activity in the extracellular
environment [12].
1.4. Molecular pathogenesis
The mechanism of action by which progressive inflammation and damage occurs is a complex
cellular interplay between several key cell types and processes. Usually RA initiates with
abnormal presentation of self-antigen by antigen-presenting cells (APC), such as B cells,
dendritic cells, or macrophages, which leads, in turn, to the activation of autoreactive T
lymphocytes[13]. As the disease progresses, the sub lining of the synovium is infiltrated by T
cells, B cells, macrophages, and plasma cells. T cells, once activated, build up in the affected
joint and secrete lymphokines such as interleukin-2 and interferon, and other pro-inflammatory
cytokines. In addition to acting as APC, B cells produce RF and other autoantibodies, secrete
pro-inflammatory cytokines, such as tumour necrosis factor (TNF)-α, and activate T cells. In
addition, macrophages secret cytokines and stimulate synoviocytes to release enzymes, which
may damage cartilage and bone[14].
Several other cell types accumulate and stimulate in the synovial membrane of RA patients via
activated endothelial cells, including synovial fibroblasts and osteoclasts, both of which can
promote bone degradation. Synovial fibroblasts contribute to cartilage and joint destruction
through the expression of matrix-degrading enzymes, such as matrix metalloproteinases
(MMPs), and are activated by a variety of cytokines, including TNF-α and interleukin-1. The
identification and understanding of this process has led to the development of several novel
therapeutic strategies that target these cytokines[15]. Osteoclasts resorb bone matrix and are
complemented by osteoblasts that produce bone matrix. Macrophage colony-stimulating factor
(MCSF) and the receptor antagonist of NF-KB ligand (RANKL) are required for the growth
and differentiation needed by osteoclasts to become fully developed. An abnormal activation
of osteoclasts leads to the bone destruction observed in RA patients, in whom osteoclast
7
formation in inflamed joints is promoted by pro-inflammatory cytokines through their influence
on RANKL expression. Figure 1.1 represents a current model of the hypothesized pathogenesis
of RA[15].
Figure 1.1 Schematic picture of pathogenesis in RA.
1.4.1. Mechanism of actions of bDMARDs and csDMARDs
These drugs are immune-suppressive and are designed to slow cartilage damage. There are two
types of DMARDs; (i) conventional synthetic DMARD commonly reoffered to as csDMARDs,
examples of which include methotrexate, sulfasalazine, hydroxychloroquine, or leflunomide
and (ii) Biologic DMARDs (bDMARDs) which only came to market in the early 1990s[16].
These include Lenercept, etanercept, abatacept, infliximab, rituximab, tocilizumab etc. some of
these drugs are monoclonal antibody based are produced in prokaryotic or eukaryotic cells
using hybridoma technology (Table 2.1). Such monoclonal antibodies are engineered to have
specific targets and pharmacodynamic properties. In addition, these drugs are engineered to
improve their pharmacokinetics and pharmacodynamics properties such as long stability and
serum half-life to reduce their frequency of administration. Such modifications include addition
of Fc potion of human IgG antibodies or by PEGylation, addition of polyethylene glycol[16].
8
1.4.2. Mechanism of action of bDMARDs There are a range of bDMARDs, which have been developed as anti-inflammatory drugs,
targeting a range of proinflammatory cytokines (Figure 1.2). Generally, cytokines are targeted
in four ways[17]:
1. Anti-cytokine antibodies
2. Receptor-blocking antibodies
3. Soluble receptors: TNF-α soluble receptors bind to and inactivate TNF-α, thus reducing
the TNF-α pool available for membrane-bound receptors and signal transduction
4. Receptor antagonists
A B C D 5.
6.
7.
8.
9.
10.
Figure 1.2 Mode of action of anti-cytokines (A) normal cytokine-receptor interactions
(B) neutralization of cytokines with either soluble receptors or monoclonal antibodies (C)
receptor antagonist block receptor so that no inflammatory signal is sent (D) suppression of
inflammatory cytokines by activating anti-inflammatory pathways[18],[19] .
Anti-inflammatory drugs each have their own mechanism of action leading to
interfering/blockage of the critical pathways in the inflammatory cascade[16]. For example,
Methotrexate stimulates adenosine release from fibroblasts[16]
Anti -TNFα inhibitors all bind to the cytokine TNF and inhibit its interaction with the TNF receptors [20]
Hydroxychloroquine has mild immunomodulatory action that inhibits intracellular toll-like receptor TLR9 [21].
9
1.4.3. TNFα
Tumour necrosis factor-α (TNF-α) is a 26 kDa membrane bound cell signalling cytokine, which
has several roles in the immune system. These include: (i) antitumor activity (ii) immune system
modulation (iii) inflammation (iv) anorexia, (v) cachexia, (vi) septic shock, (vii) viral
replication and (viii) haematopoiesis. In arthritis, these cytokines collectively induce
chondrocytes to produce metalloproteinases (MMPs), which contribute to cartilage and bone
erosion[22].
Overexpression of TNFα plays a key role in the pathogenesis of many chronic inflammatory
and rheumatic diseases, including rheumatoid arthritis, ankylosing spondylitis, psoriatic
arthritis, Crohn’s disease, as well in pulmonary inflammation and emphysema and myocarditis
etc[22].
1.4.4. TNFα inhibitors
Several biologics have been designed to block the proinflammatory activity of TNFα (Figure
1.3). These include etanercept, infliximab and adalimumab. Such biologics have shown to
reduce symptoms and improve function and quality of life [23]. There are two main strategies
for inhibiting TNF:
1. Monoclonal anti-TNF antibodies
2. Soluble TNF receptors (sTNF-R) -recombinant protein
10
Figure 1.3 Structure of some of the TNFα inhibitors: etanercept, recombinant fusion protein
with two p75 TNF receptors that is solubilised by linking to the Fc portion of human IgG1;
pegsunercept, a soluble tumour necrosis factor receptor which is PEGylated; onerecept,
recombinant human TNFα binding protein-1; adalimumab; infliximab, an IgG1 monoclonal
antibody; and, CDP571, a humanised monoclonal antibody to TNFα[24] , [22].
11
1.5. Major risk factors
There are several risk factors which may ignite the above-mentioned molecular pathways in
RA. Environmental risk factors is one of them. The primary known environmental risk factor
for RA is cigarette smoking, however, an unanticipated finding also shows that taking the oral
contraceptive for 7 or more consecutive years is associated with a lower risk of RA [25].
Smoking usually is associated with sero-positive, not seronegative, RA [25]. An increase in the
duration of smoking years increases the risk of developing seropositive RA [26]. Former
smokers are also at risk. Studies show that former smokers remain at risk of RA for anywhere
between 10 and 19 years after smoking cessation. Another risk factor is air pollution. In a study
by Hart et.al. (2009), the prevalence of RA is higher in the regions of the USA which have
greater air pollution [27]. By gathering the results of these two studies, Hart et.al., in their study,
concluded that inhaled particulate matter from traffic pollution might contribute to the risk of
developing RA [27].
Alcohol consumption, birth weight, and early life hygiene are other well-known risk
factors[28]. It has been suggested that there is a dose-dependent inverse risk associated with
alcohol consumption and RA [28]. Also, in an analysis of women, it was revealed that women
with a higher birth weight (>4.54 kg) had a two- fold increased risk of adult onset RA[28]. In
addition, in a number of studies, oral contraceptive pill (OCP) consumption is associated with
a lower risk for RA (Kłodziński & Wisłowska, 2018)[28]. A comprehensive list of risk
protective and causative risk factors is presented in Table 1.1[8].
1.6. Signs and symptoms and laboratory tests
The main symptoms of RA are pain and stiffness. There are usually four distinct phases in RA:
an initial phase (no clinical manifestations), an early inflammatory phase (clinical
manifestations); a destructive phase (erosions and disease progression); and an ongoing phase
(irreversible joint destruction). Two major overlapping subpopulations in RA include
individuals who are positive for the presence of rheumatoid factor (RF) and individuals who
are positive for the presence of antibodies that can bind cyclic citrullinated peptides (CCP).
Patients with neither of these biomarkers tend to have a more benign course and are referred to
as having “seronegative” RA [6].
12
Table 1.1 List of risk factors in RA [29]
Risk Factors
Increase Chance of disease
Protein tyrosine phosphatase, non-receptor type 22 (PTPN22) Peptidyl arginine deiminase 4 (PADI4) DNA methylation changes CD40, CC chemokine ligand 21 (CCL21), CC chemokine receptor 6 (CCR6) Tumour necrosis factor receptor-associated factor-1 (TRAF1/C5) Interleukin-6 receptor (IL6R) MHC regions especially amino acids at positions 70 and 71. Fc gamma receptor (FCGR) Tumour necrosis factor receptor-associated factor-1 (TRAF1/C5) Signal transducer and activator of transcription 4 (STAT4) Exposure to tobacco smoke Female sex Low vitamin D intake and levels Obesity Occupational dust (silica) High sodium, red meat and iron consumption Air pollution
Possible protective effect:
HLA DRB1*1301 (decreased risk for ACPA positive RA) Statin use Healthy diet Consumption of fish Consumption of alcohol Hormone replacement
Rheumatoid factor is a type of antibody present in around 80% of RA patients. It is believed
that this antibody attacks healthy tissue and causes inflammation. This factor is assessed and
measured in the blood stream and once it passes a certain amount, RF is reported positive.
Previously, RF was the only way to diagnose RA but, nowadays, other antibodies, such as anti
CCP and antinuclear antibodies, are also being used[29]. Anti-CCP is another destructive
antibody in RA causing inflammation and damage to the joints and they may be positive long
before symptoms manifest in RA[29]. Antinuclear antibodies, such as ANA, are also antibodies
which are circulating normally in the body, and when their amount increases, they can attack
normal tissue and are indicators of autoimmune diseases[6]. ESR and CRP are mainly useful
to measure the level of inflammation in a particular patient and cannot be used to diagnose RA
(table 1.2) [30].
13
Table 1.2 A list of signs and symptoms and diagnostic laboratory tests in RA[6].
Signs and symptoms Disrupted sleep Low grade fever Fatigue Depression and mood changes Dry eyes and mouth Weight loss Joint pain (more small joints in hands and feet) Joint swelling (more small joints in hands and feet) Joint stiffness (more small joints in hands and feet) Red joint (more small joints in hands and feet) Warm joints (more small joints in hands and feet) Joint deformity (more small joints in hands and feet) Numbness and tingling (feet and hands) Subcutaneous Nodules Dry eyes and mouth Depression and mood changes Muscle aches Lack of appetite Loss of energy Limping Hoarseness Painful walking
Laboratory tests ESR CRP RF Anti-CCP Antinuclear Antibody (ANA)
1.7. Complications
Although RA is not a terminal disease, related information indicates a gap in mortality between
individuals with RA and the general population. For example, RA increases the prevalence of
ischemic heart disease (IHD) and pulmonary disease, particularly interstitial lung disease
(ILD), type 1 diabetes, obesity, infection in different organs, serious infection, and hypertension
[29][5][31]. Rheumatoid arthritis (RA) may require special attention due to the particularly
14
devastating effects in organs such as lung, heart, CNS, or lymphatic system that sometimes
ensue[29].
1.8. Moderate and serious infections
Probably one of the most important consequences in RA is the development of infections.
Medicine Net has a well worded definition for infection: “Infections may be localized, or may
become systemic (body wide)”[32]. Table 1.3 lists a series of most common microbes which
frequently cause infection in RA.
Table1.3 Common RA-associated microbes[33].
Name of microbe
Proteus mirabilis
Epstein-Barr Virus
Mycoplasma spp.
Prophyromonas spp.
Periodontal disease (PD) is probably one of the most commonly infections associated with
RA. This association has been considered since the early 1820s. Almost twenty different
bacterial species can cause PD. P. gingivalis, Prevotella intermedia, Tannerella forsythia,
and Aggregatibacter actinomycetemcomitans are the most common ones. There is, however,
another possibility that PD can increase the incidence of RA[33].
It has been shown that a range of bacterial and viral infections can manifest rheumatic disease
symptoms, including reactive arthritis. These infections include gastrointestinal or
genitourinary infections with Salmonella, Shigella, Campylobacter, Yersinia, and Chlamydia
trachomatis, HIV, parvovirus and hepatitis viruses B and C [34].
15
Table 1.4 Incidence cohort of RA patients, followed from 1955 to 1994 at the Mayo Clinic [32]
Infection Rate ratio Urinary tract infection 1.1 Septicaemia 1.5 Pneumonia 1.6
Lower respiratory tract infection 1.9 Other 2.0 Intra-abdominal 2.8 Skin or soft tissue 3.3 Osteomyelitis 10.6 Septic arthritis 14.9
RA can also increase the rate of serious infection (SI), from less than one per hundred patient
years (100PYs) in the normal population to around 5 per 100PYs in RA, overall (Table 1.4)
[5]. The risk of infection in RA increases due to several changes. Some of these changes include
RA disease and pathophysiology of changes in the immune system, RA medications, a number
of which suppress the immune system, and, finally, sometimes there are coexisting genetic
factors, such as Mannose Binding Lectin (MBL) deficiency, which increases the risk of
immunodeficiency through well-known or unknown mechanisms [35][33].
The risk for the development of serious infection (SI) can also increase in RA. In the literature,
the term serious infection is usually used for an infection which requires specific interventions,
such as hospitalisation or intravenous antibiotics or both, or any infection which results in death
or severe disability. In this study, data have been collected from participants in the ARAD
database, who have self-reported details of their illness, treatment, and course over time,
including complications, such as infections. While infection can happen in any organ, based on
the literature, the most predominant infections in RA include (i) bronchopulmonary (ii)
urogenital (iii) soft tissue and (iv) skin, bone/joint sepsis and gastrointestinal infections [31].
Less common infections in RA include the CNS, the cardiovascular system and the lymphatic
system[31].
1.9. Medical treatment
The primary goals in treating patients with rheumatoid arthritis (RA) are to reduce pain and
stiffness, slow disease development and improve function. Medications, such as non-biologic
16
and biologic disease-modifying antirheumatic drugs (DMARDs), can reduce pain, retard
disease progression, and improve functional outcomes[36] whenever it proves possible to
reduce dosages or eliminate these medications. However, studies show that there are probably
some positive associations when taking biological medications if there should be a serious
infection. Richter et.al., in a study published in 2015, performed an observational cohort study
of 947 patients with serious infection in a total cohort of 11,150 participants in the German
registry (RABBIT) [35]. He and his colleagues observed that persons exposed to bDMARDs at
the time of an SI had a reduced risk of sepsis (septicaemia) and mortality[37].
Wherever DMARDs are discussed in this study, DMARDs is divided to csDMARDs and
bDMARDs. csDMARDs, or Conventional synthetic DMARDs, include: 1- IM Methotrexate
2- Hydroxychloroquine, 3- Sulphasalazine, 4- Arava (Leflunomide), 5- Azathioprine, 6-
Cyclosporin. bDMARDs, or biologics or biological DMARDs, include: 1-
Humera/Adalimumab, 2- Etanercept/Etanercept, 3- Kineret/Anakinra, 4- Remicade/Infliximab,
5- Mabthera/Rituximab, 6- Orencia/Abatacept, 7- Actemra/Tocilizumab, 8-
Simponi/Golimumab, 9- Cimzia/Certolizumab Pegol. Prednisolone, IM gold and penicillamine
do not belong to any group and are studied separately.
Mechanism of action of csDMARDs
Methotrexate (MTX), usually the first drug of choice for people with RA, stimulates
adenosine release from fibroblasts. When csDMARDs, such as MTX, are ineffective or
partially ineffective, other treatments options will involve bDMARDs.
Mechanism of actions of bDMARDs and csDMARDs
These drugs are immune-suppressive and are designed to slow cartilage damage [38]. There are
two types of DMARDs; (i) conventional synthetic DMARD, commonly reoffered to as
csDMARDs, examples of which include methotrexate, sulfasalazine, hydroxychloroquine and
leflunomide, and (ii) biologic DMARDs (bDMARDs), which only came to market in the early
1990s. These include lenercept, etanercept, abatacept, infliximab, rituximab and tocilizumab.
Some of these drugs are monoclonal antibody-based and are produced in prokaryotic or
eukaryotic cells using hybridoma technology (Table 2.1). Such monoclonal antibodies are
engineered to have specific targets and pharmacodynamic properties[38].
17
In addition, these drugs are engineered to improve their pharmacokinetics and
pharmacodynamics properties, such as long stability and serum half-life, to reduce their
frequency of administration. Such modifications include the addition of the Fc portion of
human IgG antibodies or the addition of polyethylene by glycol PEGylation [39].
Mechanism of action of bDMARDs
Cytokines are therapeutic targets for a range of bDMARDs, which are designed to reduce their
production (overexpression) or function. There are four ways in which cytokines are targeted.
These include the application of (i) anti-cytokine antibodies (ii) receptor-blocking antibodies
(iii) soluble receptors and (iv) receptor antagonists. Most bDMARDs fall into one of these
categories. Examples include infliximab, lenercept and etanercept and adalimumab among
others inhibiting the “second signal” required for T-cell activation, and depleting B-cells or
inhibiting factors that active B-cells (rituximab and belimumab) [17].
Considering the difference between anti-RA medication, different countries have developed
different therapeutic guidelines, based on factors such as the availability of medication and the
health economy. Table 1.5 shows a comparison between the different treatment modalities in
Australia, the United States and Canada.
1.9.1. Medication and risk of infection in the literature
Richter et al, in their study in 2015, concluded that bDMARDs supress the immune system
[37]. In some studies, bDMARDs are very safe and adding or not adding human antibodies will
not change this safety. For example, in a study by Wong Pack published in 2016, the authors
examined the incidence of serious infections in RA patients treated either with the combination
of denosumab and an immunosuppressive biologic DMARD or with an immunosuppressive
biologic DMARD alone [37]. Denosumab is a human monoclonal antibody which is used to
treat osteoporosis arising from multiple different causes. The sample included patients over 18
years of age with RA, registered in the practice 3 months before and after the index date, and
who had received 1 injection/infusion or filled a prescription for an immunosuppressive
biologic DMARD therapy for RA. Among all 308 patients in the sample, the authors concluded
that there is a low incidence of SIs in RA patients receiving bDMARDs, including patients
who currently are taking bDMARDs (Table 1.5) [43].
18
Table 1.5 Cross-comparison of RA therapy between Australia, the United States and Canada Countries Therapeutic Guidelines Australian
Initially, start treatment with simple analgesics, such as paracetamol, and supplements, such as Omega-3. Also, patient education and referral to physiotherapist and podiatrist are essential. Pharmaceutical therapy starts initially with NSAIDs and COX-2 inhibitors. If, in spite of using these medicines, swelling is persistent beyond six weeks, the patient needs to be referred to a rheumatologist to start DMARDs or low dose glucocorticoids. Referral to a rheumatologist can happen initially after multiple swollen joints are detected or if six weeks of NSAID therapy does not improve signs and symptoms. Advanced therapy in RA includes combination of DMARDs, leflunomide or cyclosporin or taking biologic agents, anti-TNFs, anakinra and rituximab[40].
American Use a treat-to-target strategy. Start with monotherapy (with MTX) rather than double therapy or triple therapy. In moderate or high disease activity without previous DMARDs, patient should take DMARD monotherapy, which is better than double or triple therapy. If the disease is still active, a combination of DMARDs or a TNFi or a non-TNF biologic, in no particular order, is preferred. If the disease activity remains moderate or high, use TNFI monotherapy or TNFi plus MTX. If disease activity persists and is moderate to high, add low dose glucocorticoid. Depending on the activity of disease, the dosage of glucocorticoid can be increased but it should remain as low as possible[41].
Canadian Start DMARD as soon as possible, through combination with methotrexate (MTX) or monotherapy with MTX. If response is inadequate, then switch to DMARDs. Usually first choice is anti-TNF with MTX, then ABAT/RTX or TCZ. If there is still an inadequate response, switch to any biologic or switch to traditional DMARDs. Inadequate response is defined as not reaching targets by 3 to 6 months[42].
Notes. MTX: Methotrexate; Anti TNF: Tumor necrosis factor inhibitor; ABAT: Abatacept, RTX: Rituximab, TCZ: Tocilizumab
19
Table 1.6 Samples of bDMARDs and their molecular structure[44] Type Name Description
mAb based
Infliximab (Remicade®)
Mouse-human chimeric anti-human TNF mAb
Adalimumab (HumiraTM)
Fully human anti-human TNF mAb
CDP571 a humanised monoclonal antibody to TNF- α
Golimumab Tumor necrosis factor alpha (TNF-alpha) inhibitors
Recombinant Fusin protein
Etanercept (Enbrel®) p75sTNF-RII-Fc (dimeric)
Lenercept p55sTNF-RI-IgG1 (dimeric)
Pegylated Certolizumab (Cimzia) PEGylated anti-TNFα biologic
1.10 Discussion
Zamora-leoff et.al. (2016), in a retrospective study among 181 patients suffering from RA,
found that the risk of serious infection is the highest in the first year after diagnosis of interstitial
lung disease (ILD). They found that the most common types of infection among this group
included pneumonia, septicaemia, and opportunistic infections. It was also revealed that
prednisolone in doses more than 10 mg, with or without DMARDs, was associated with the
highest rate of infection. The authors of this study concluded that the underlying autoimmune
process and use of immunosuppressive drugs or both are potential risk factors for higher
infection rates among patients with RA-ILD [45].
Curtis et.al. (2018) investigated a sample of 17433 RA patients with hospitalised
pneumonia/sepsis SIs and 16796 with myocardial infarction (MI) and coronary heart disease
(CHD). They found that higher multi-biomarker disease activity (MBDA) scores were
associated with hospitalised infections, predominantly in the older, US RA population [31].
Morel et. al.(2017), in a study of 1491 patients with RA who were treated with tocilizumab,
found that a high absolute neutrophil count (ANC) (above 5.0 × 109 at baseline), a negative
anti-citrullinated peptide antibody (ACPA) and concomitant therapy with leflunomide (LEF)
are predictive factors of serious infection [46].
20
Accortt et.al. (2018), in a study of patients over 18 years old and with a disease activity index
score of two or more than two, found that, compared to low RA disease activity (LDA),
moderate‐to‐high RA disease activity (MHDA) had a greater number of serious infections. The
authors concluded that lower RA disease activity was associated with lower serious infection
rates and recommended that treating physicians strive for remission of RA rather than accept
an LDA [31].
Salmon et. al. (2015) revealed that, in practice, usually patients with rheumatoid arthritis treated
with abatacept (ABT) have more comorbidities and serious infections are slightly more
frequently observed. In the Orencia and Rheumatoid Arthritis (ORA) registry, predictive risk
factors for serious infections included age and a proceeding history of serious infections [47].
Hashimoto et. al. (2015), in a study of 370 patients with RA, demonstrated that, although the
current disease activity was similar in patients with SIs, patients with multiple SI had greater
radiographic joint damage and more advanced physical dysfunction. [48]. Rutherford et.al.
(2017) examined 19282 patients with rheumatoid arthritis for 46771 patient-years and they
found that the incidence of serious infection was lowest with certolizumab. Rituximab and
tocilizumab both have higher rates of infection and there is a possibility that patient factors as
opposed to the drug itself were responsible for the observed difference [49].
In another study by Tarp et al. (2017), the crude incidence rate (IR) per 100 patient-years for
serious infections was calculated for the sustained remission, low disease activity (LDA), and
moderate to high disease activity (MHDA) groups. [31]. Baradat et. al. (2017) published a
systematic review of 16 RCTs. In this systematic review, the rates of serious infection and death
were compared between patients with RA who were treated with a combination therapy of
methotrexate and biological disease-modifying antirheumatic drug (bDMARDs) and patients
with RA who were using biological disease-modifying antirheumatic drug (bDMARDs)
monotherapy. The authors in this study concluded that there was no significant difference
between the two groups. They confirmed that using methotrexate and bDMARDs combination
therapy in RA does not cause an increased risk of serious adverse events [50].
De Andrade (2017) published a study in which he concluded that there is no difference in the
rate of SIs between patients who were taking rituximab (RTX), on one hand, or bDMARDs
(such as TNF inhibitors), on the other. Silva-Fernandez et. al. (2016) presented another study
21
in which the authors demonstrate that there is no difference at all in the risk of SIs over the first
year of treatment in patients treated with RTX compared with those treated with a second TNFα
after discontinuing a first TNFα [51]. Subesinghe et.al (2018) published a report concerning
the recurrence rate of SI among RA patients registered with the British Society for
Rheumatology Biologics Register. Among 5289 subjects with at least one serious infection,
contributing to 19 431 patient-years follow-up, the first SI rate was 4.6% (95% CI: 4.5, 4.7),
increasing to 14.1% (95% CI: 13.5, 14.8) [49].
Pappas et.al. (2017) conducted an extended observation analysis in clinical trials and showed
that rituximab does not increase the risk of serious infection events (SIE) in patients with
rheumatoid arthritis (RA). They describe characteristics of rituximab-treated patients who
experienced a SIE versus those who did not. In this study, they concluded that retreatment with
rituximab infusions was not associated with a higher rate of SIEs. [52].
Henry et. al. (2017) showed that, among a sample of 1278 RA patients who were treated with
standard vs reduced doses of rituximab for 5 years, the SI rate was lower in those who received
reduced doses. [53].
Zhang et.al. (2017) investigated 688 patients with pure RA and examined the association
between the infections and disease outcome. The authors concluded that repeated exposure to
infectious agents during the disease duration might lead to poor outcome for RA. They advised
paying more attention to those patients who have repeatedly infectious agents during their
disease duration in order to improve their prognosis [33]. Jinno et.al. (2017) examined 792,921
hospitalisations for infection where there was a secondary diagnosis of RA and concluded that
the proportion of hospitalisations for infections among RA patients appeared to decline over
time for pneumonia and opportunistic infections (OIs). They also observed a slight decrease in
UTIs, a slight increase in skin and soft tissue infections (SSTIs), and an increase in
hospitalisations with sepsis. [54]. Bortoluzzi et.al. (2016) studied the databases of the
Lombardy Region in the period between 1/1/2004 and 31/12/2013. They concluded that among
4656 RA patients recorded in the database, treatment with bDMARDs was not associated with
an increase in hospitalised infection. The risk was lower with abatacept, which accords with the
perception that it has a better safety profile [55].
22
In a meta-analysis by Singh, Cameron, Noorbaloochi et.al. (2015), 525 serious infections were
identified in 59 studies. A total of 342 infections occurred with biologic therapy with or without
DMARD adjunct therapy and 183 infections occurred with conventional DMARD
therapy. They concluded that therapy with standard dose or increased dose biologics or
DMARD combination biologics increases the risk of serious infections. Therefore, they advised
that practitioners should consider risk factors, such as corticosteroid therapy, increased age and
comorbidities to estimate the individual risk of infection when undertaking treatment with
biologic DMARDs[56]. Unfortunately, from this Meta-analysis, it is not clear if traditional
DMARDs had included corticosteroid as well or whether the data were limited to Methotrexate
only.
In an article published by Salmon et.al. (2016), the authors concluded that factors predictive of
serious infections include: age, history of previous serious infections, diseases such as diabetes
and a lower number of previous anti-TNFα therapies. However, on multivariate analysis, only
a history of previous serious or recurrent infections (HR 1.94, 95% CI 1.18 to 3.20, p=0.009)
and age (HR per 10-year increase 1.44, 95% CI 1.17 to 1.76, p=0.001) were significantly
associated with a higher risk of serious infections. [47]. Subesinghe et.al.(2018) used data from
the British Society for Rheumatology Biologics Register -Rheumatoid Arthritis, to follow up
5289 subjects with at least one SI 19 431 patient-years. [49].
Tarp et.al. (2015), in a meta-analysis of 106 trials found that, compared with traditional
DMARDs, standard-dose biological drugs and high-dose biological drugs were associated with
an increased risk of serious infections, although low-dose biological drugs were not. [57].
In another study by Singh et.al. (2015), the authors performed a systematic review and meta-
analysis of patients with RA recorded in Copenhagen University Hospital. They identified 106
trials that reported serious infection among patients who were taking biologics. They concluded
that, of traditional DMARDs, standard-dose biological drugs and high-dose biological drugs,
only high-dose biological drugs were associated with an increased risk of serious infections. In
their analysis, low-dose biological drugs and csDMARDs were not. [57].
In a study by Kawashima et. al. (2017), the impacts of the long-term use of biologic agents on
serious infection were investigated. The authors showed that the incidence rate of serious
infections was not significantly different between biologics-treated and non-biologic or
23
csDMARDs-treated patients [48]. Prednisolone usage (1-4 mg/da) was significantly associated
with serious infections [48]. Curtis et.al. (2016) in their study among 3355 RA patients in the
sustained remission group and 3912 in the sustained LDA group, found that patients in
sustained remission have a lower risk of serious infections compared to those in sustained LDA
[31]. Diederik et.al (2017) in their study compared the effects of TNF inhibitors (TNFi) and
rituximab (RTX) on SI rate. The analysis included patients registered in the British Society for
Rheumatology Biologics Register (BSRBR)-RA. A total of 3419 patients who were taking
tumour necrosis factor inhibitor (TNFi) and 1396 patients who were taking rituximab (RTX)
were compared. Patients contributed almost 2765 and 1224 person-years (pyrs), respectively.
The risk of SIs was comparable in RA patients using rituximab or a TNFα, in the first year [52].
Altogether, it is difficult to reconcile the conflicting information that has arisen from many
disparate studies of infections complicating RA, since the patient cohorts are not always
comparable and the drugs under evaluation differ in respect to class or family, dose, duration
of therapy and adjunctive agents. Furthermore, the infections are not always well defined. Data
pertaining to deaths as a result of SIs tend to be limited or scanty. Assuming very little
difference in RA cohorts and that SIs can be relied upon, despite somewhat differing
definitions, the following conclusions can be drawn:
Higher rates of SIs are encountered in RA per se, irrespective of treatment.
SIs are a function of RA disease activity.
Corticosteroids are a potent risk factor for SIs. No safe dose has been defined.
Current widely used csDMARDs, such as HCQ, SAS, MTX and LEF, confer only a
modest risk for SIs.
SIs are increased in the first year of therapy with a bDMARD and rates taper,
thereafter, but probably remain above background risk throughout treatment.
1.11 Organisation of this thesis
This thesis interpolates materials from one paper by the authors Dr. Hamid Ravanbod, Dr Jalal
Jazayeri, Dr Graeme Carroll and Professor Ken Russell. In Chapter 2, the medical literature
concerning serious infections in rheumatoid arthritis is reviewed. In this chapter strategies to
prevent serious infection in RA are discussed. In total, 3,324 articles were reviewed to form
24
this chapter. Among these articles, 31 studies met the selection criteria such as large population
size, heterogeneous populations, and English language with adolescence RA.
In Chapters 3 and 4, there are descriptive and inferential analyses of the impact of all available
anti-RA medications on serious infections in different organs. The data for this analysis have
been gathered from the Australian Rheumatology Association Database (ARAD). Participants
with rheumatoid arthritis provided information for the database in response to structured
questionnaires. Also, some equations are generated to predict the risk of infection based on
some well-known cofactors. An example of the questionnaire is provided in the appendix.
Chapter 4 presents the inferential analysis of the RA and organ infections with some of the
potential risk factors among the Australian population.
In chapter 5, serious infection with all its potential risk factors is discussed and analysed in
detail. Globally, serious infection (SI) is still the main cause of death in RA and investigating
the basis for SIs is important because of the risk of immediate mortality, ongoing morbidity,
and health economic burdens. Moreover, an increased understanding of SIs may lead to the
development of improved strategies for prevention.
The risk of serious infection pertains to most if not all organs in RA is estimated to be in the
order of 5 per 100PYs in RA overall (35). It is important to know the most common sites and
the most common pathogens for these infections.
A thorough review has been undertaken to identify arrange of risk factors, which include
pathophysiology of RA, medications and immunodeficiencies. There is also a discussion of the
risk of SIs in RA, potential risk factors and a concise summary concerning the contributions of
csDMARDs and bDMARDs to SIs.
1.12 Hypotheses to be examined in this thesis
Infection (including serious infection) occurs commonly in RA worldwide.
Regarding medication-induced infection and serious infections in RA, bDMARDs are
the safest available anti-RA medications
25
There are several detectable risk factors for SI in RA and anti-RA medications are
amongst these risk factors. Genetic factors can also impact the risk of serious infection
among patients with RA.
Infections and Sis among patients registered with ARAD are common and taking anti-
RA medicines can impact the frequency of these infections.
The risk of infection is different between different patients and it is possible to predict
this risk based on cofactors.
The frequency of self-reported infection in different organs varies in ARAD
participants.
Commonly used anti-RA medications have different impacts on the risk of infection in
different organs.
1.13 Significance of undertaking this review
Treatment in RA targets pain relief, reduction of joint damage and improved joint function. A
growing number of medications are available for the treatment of RA. They are categorised as
csDMARDs and bDMARDs. Usually treatment plans can change depending on the disease
activity, severity of symptoms, signs and prognosis and sometimes expected medication side
effects (25). Conventional synthetic disease-modifying anti-rheumatic drugs (csDMARDs)
interfere with the immune system to suppress it, indirectly and non-specifically. On the other
hand, biologic DMARDs specifically suppress a pathway in the immune system. There is an
increasing trend to use bDMARDs rather than rely on csDMARDs alone in RA (25) (38).
In this study, the contributions of csDMARDs and bDMARDs to infections, including Sis, are
evaluated and compared. We further categorised infections according to organ type and
severity and compared the impact of medication on the development of infection in each
organ, separately.
2. Methods
A systematic review of all available articles concerning serious infection was conducted.
ARAD reports (patients’ responses to questionnaires) from 2001to 2014 were tested under
several descriptive analyses and results were analysed statistically by Chi-square test, and
Fisher test where appropriate or by means of logistic or multinomial logistical regression
26
modelling. Demographic, disease specific, treatment and infection record data were extracted
from the ARA Database which contains details of a cohort of 3569 RA patients (960 males and
2609 females), who had completed related questionnaires 28176 times (during 2001-2014).
Among the 3569 patients, 459 patients were eliminated because they had filled out the
questionnaire only once. We were therefore left with 3110 patients. After deducing eight
duplications and eliminating accordingly, there remained 27709 visits from 3110 patients.
In Chapter 3, using filtering procedure in SPSS, the whole data was divided into two groups;
those who were just taking bDMARDs without any csDMARDs and patients and those who
were taking just csDMARDs without taking any bDMARDs. The demographic distributions of
the risk factors were assessed and compared utilizing SAS software.
Furthermore, the whole dataset was tested by SAS software to work out the impact of each anti-
RA medicine on the frequency of different types of organ infection and the results are reported
in Chapter 4. For this purpose, data were fitted in the logistic regression model and results were
tested by using Chi square and Fisher tests. The odds ratio of effectiveness is also calculated
for the medicines that have significant effects on infection. Furthermore, each type of organ
infection was categorised based on the severity of the infection.
Chapter 5 presents more complex assessments around the incidence of serious infection, its
demographic characteristics, and potential risk factors. Patients’ reports among 27709 visits
from 3110 patients during 2001 to 2014 were searched for evidence of hospitalisation or IV
infusion for infection. Resultant data were tested by inferential and descriptive analyses and
odds ratios for potential risk factors were calculated.
In this section, a few equations were also created to help predict the likelihood of serious
infection in a single patient based on known risk factors.
3. Summary of the Results
In the systematic review of 3324 articles, only 31 articles met the criteria for the review.
Descriptive analysis of ARAD revealed the mean age amongst participants with RA was 61.47.
In the group taking csDMARDs the mean age was 59.24 years and, in the group taking
bDMARDs, the mean age was 62.62 years. The Wald P-value of the differences between both
groups of Ras, based on risk factors, is very large. Taking csDMARDs alone and bDMARDs
27
alone were associated with statistically significant difference in the rates of heart infection, lung
infection, urinary tract infection, and GIT infection.
During 2001 to 2014, the most frequent infections amongst RA participants was related to the
eye, ear, nose and throat or EENT (14.75%). Cyclosporine and prednisolone were associated
with increased rates for all types of infections, whereas bDMARDs, such as adalimumab, were
associated with a reduction in the frequency of nail/skin infection.
Just under 3% of the ARAD cohort reported SIs. Adalimumab and etanercept were the most
commonly used bDMARDs in patients who reported SIs, but they were also the most frequently
prescribed agents in this category. Age, gender, alcohol consumption, medication, diabetes,
kidney disease, liver disease, heart attack and, sometimes, previous coronary artery bypass
grafting (CABG) were each implicated in higher rates of SI.
3.1. Strengths of this research
The research reported here takes a more comprehensive approach to infections in RA, because
it does not focus exclusively on serious infections but, rather, includes self-reported infections
of diverse severity and categorises these infections according to internally defined levels of
severity. Emphasis has been placed on an anatomical and organ-based approach, so that the
factors responsible for greater numbers of infections in specific organs and anatomically
defined systems can be examined methodically. Although there are several studies comparing
csDMARDs and bDMARDs for the development of serious infections, these mainly focus on
SIs and so are narrower in scope. Moreover, they lack consensus. This study has the potential
to provide a more comprehensive analysis and assist in the acquisition of greater agreement. In
the inferential analyses, backward regression was performed. Accordingly, the effect of
medications has not only been examined separately, but also, the compounding impacts that
combinations of medications may have on each other have been considered.
In this study, a large sample has been analysed (28176 patient-visits). This provides
considerable statistical power and likely more generalisability regarding the findings. There is
also a section on descriptive analysis. This section provides a better understanding of the risk
factors and status of the study population and helps to ascertain how far the findings can be
generalised. Importantly, this large sample was followed for almost fourteen years (2001 to
28
2014), which allows longitudinal effects to be more readily captured, thereby strengthening the
estimations of SIs calculated in terms of person-years.
3.2. Limitations
The main limitations of this study relate to the design of the ARAD questionnaire and
the inherent weaknesses associated with the capture and use of self-reported data.
Participants may not have known that some illnesses suffered were in fact infections
or they may not have remembered to report them when completing a questionnaire
some months later for example. Furthermore, it was not possible to validate reported
information, since family practitioner confirmation, hospital records and
microbiological and other pathology and imaging were unavailable. Reporting in
respect to the nature of medications is likely to be reliable, but it is not possible to
assess medication compliance. There are also statistical limitations affecting data analysis.
In the descriptive analysis, reliance was placed on Chi-Square and P-value calculations. In
addition, the reliability of the conclusions in descriptive analysis is compromised by the fact
that participants needed to answer a very general question.
4. Conclusion
Based on the systematic research, SI is far more common in RA than in the general population.
Anti-RA medications have different impacts on this infection, with a huge impact from
corticosteroids followed by bDMARDs and csDMARDs. The time of prescribing bDMARDs
in the first year or after that, a higher dosage of bDMARDs and combination therapy with
bDMARDs all increase the risk of infection.
Compelling evidence has proved that in Australia, RA can increase risk of infection. Although
it seems that in the Australian database (ARAD data which is used in the current study) overall,
csDMARDs alone during prescription can evoke higher rates of infection than bDMARDs
alone; this difference is statistically significant only in self-reports of heart infection, lung
infection (P value 0.0156), urinary system infection (P-Value 0.0002), and GIT infection.
Both csDMARDs and bDMARDs are associated with higher risk of infection in RA. All in all,
without isolating the first year of taking bDMARDs, it seems that bDMARDs causes less
29
infection, but more serious infection. The impact of various medications on infection depends
on the type of infection and severity of infection.
Serious infection can occur in almost 2.92% of anti RA treatments in Australia, and for females
this risk starts in younger ages. Also, in Australia, the majority of patients who develop SIs are
taking biologics. It seems that previously taking bDMARDs is a higher risk for patients who
are currently taking bDMARDs and those who have never taken this medication. In addition,
among different risk factors, which are tested in this review, smoking has a significant
connection to the seriousness of infection.
In the next chapter, a comprehensive literature review has been conducted, not only to collect
the latest published information in the field but also to investigate the prevalence and status of
infection and SI in RA patients and to explore the potential risk factors., including anti-RA
medications. In addition, the implications of such infections in clinical practice are also
explored and discussed.
30
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pp. 258–265, Jul. 2015, doi: 10.1016/S0140-6736(14)61704-9.
[57] S. Tarp et al., “Risk of serious adverse effects of biological and targeted drugs in
patients with rheumatoid arthritis: a systematic review meta-analysis,” Rheumatology
(Oxford), vol. 56, no. 3, pp. 417–425, 01 2017, doi: 10.1093/rheumatology/kew442.
37
CHAPTER 2
Infections in rheumatoid arthritis and strategies for their
prevention: A review and discussion of implications for
clinical practice
38
Abstract
Objectives: Serious infections (SIs) in rheumatoid arthritis (RA) are common and may be life-
threatening. The goal of this chapter is to present a systematic review of the present literature
regarding prevalence and status of infection and SIs in RA and explore potential risk factors
including anti RA medications.
Methods: A systematic review was performed that included multiple databases, viz. PubMed,
Medline, Scopus, and Google Scholar. Search terms used were ‘Rheumatoid Arthritis AND
infection’. Searches were limited to the title of articles, human subjects and non-juvenile
arthritis and to those articles published in English.
Results: In total, 3,324 articles were found. After removing duplicates, 825 articles remained
for further screening, from which 141 articles were selected. These were further assessed and
110 were then excluded because 31 articles were case reports, 35 focused on young subjects
(<16 year and 44 studies focused on non-serious infection. Overall, only 31 studies met our
selection criteria.
Conclusion: SIs are far more common in RA than in the general population. Corticosteroids
are associated with an appreciable increase in SI risk. Most commonly used and currently
favoured synthetic DMARDs confer a small or no risk, biologic DMARDs confer moderate
risk in the first year of therapy and then a diminishing risk, thereafter, and higher dose biologic
or combination biologic therapy should be avoided since the SI risk is unacceptably high.
Undetectable mannose binding lectin (MBL) is a major risk factor for SI in RA, comparable to
prednisolone.
Keywords: infections, arthritis, serious infections, risk factors
39
1. Introduction
Rheumatoid Arthritis (RA) is a chronic, systemic autoimmune disorder. It affects over 1% of
the world population and confers significant economic burdens, not only on the individual, but
also society as a whole. The rheumatoid patient is exposed to many complications including
infections, cardiovascular disease and malignancy [1, 2]. Among these, serious infection (SI)
(infection that is life-threatening or fatal, requires hospitalization or intravenous antibiotics or
results in severe disability) is of special importance, because of the immediate risk of mortality,
ongoing morbidity and because of the health economic implications. Serious infections are still
the number one cause of death in RA, globally [2]. The most prevalent infections in RA include
bronchopulmonary, urogenital, soft tissue and skin, bone/joint sepsis and gastrointestinal
infections [1]. Infections in the lung and urogenital system or generalized sepsis in a patient
with RA is common and may be fatal, with incident frequencies up to ten times that in the
general population [2,3]. Less commonly, infection in RA may affect the central nervous
system (CNS), the cardiovascular system or the lymphatic system [4].
It is clear that in RA, there is a marked increase in rates of SI from less than one per 100PYs in
the normal population to around 5 per 100PYs in RA overall [5]. Bronchopulmonary, urogenital
and skin infections are the most common SIs. The main pathogens are S. pneumoniae, S. aureus,
gram-negative bacilli and anaerobes [6]. Some studies have investigated diverse risk factors,
such as RA disease pathophysiology, RA medication and immunodeficiency including
Mannose Binding Lectin (MBL) deficiency as potential causes for this higher incidence rate
[2,7-15] A failure to appreciate this de novo increase in frequency of SIs in RA can give rise to
a perception of more frequent SIs in csDMARDs and bDMARDs treated RA.
In this review, we have searched the literature in order to determine and analyse (i) the extent
to which RA patients are predisposed/susceptible to developing SIs (ii) the potential risk factors
associated with SIs in RA patients, and (iii) whether the rate of SIs is higher in patients who are
on medications, such as anti-TNF-α, and DMARDs. The goal was to identify, categorise and
evaluate the main causes of SIs in RA. In addition, methods and possible strategies to minimise
or prevent infection in RA and, in turn, reduce the rate of hospitalisation and out-of-hospital
treatments will be discussed.
40
2. Methods
2.1. Search strategy and selection criteria
A systematic review was performed that included multiple databases, viz. PubMed, Medline,
Scopus, and Google Scholar. Search terms used were ‘Rheumatoid Arthritis AND infection’.
Searches were limited to the title of articles, human subjects and non-juvenile arthritis and to
those articles published in English. The search timeframe was 1996-2015. Articles were only
included in this review if they investigated or discussed ‘infection in Rheumatoid arthritis’
specifically focusing on SI in patients over 16 years of age. Eleven cohorts, four reviews, one
cross-sectional study, one observational prospective study, five case-control studies, five
randomized controlled trials (RCT), three systematic reviews and two meta-analyses were
included. Even though diverse methodologies and a relatively long time- frame mitigating
against embracement of the modern biologic era were used, the advantages of inclusivity were
deemed to outweigh the inconsistencies in methodology. A PRISMA chart has been constructed
to show the systematic selection of the articles (Tables 2.1 and 2.2).
3. Results and discussions
3.1. Study selection
In total, 3,324 articles identified through PubMed, Medline, Scopus and Google Scholar
repository were found. After removing duplicates, 825 articles remained, from which upon
further screening, 684 articles were culled due to one or more of the following: (i) population
size was very small or the studies were not within the designated time frame; namely 1996 -
2015 (ii) heterogeneous populations, which made it difficult to identify infections pertaining
explicitly to RA and (iii) the language in which the articles were published was not English.
41
Figure 2. 1 Prisma chart showing search results and article selection
42
Table 2.1. Relationship between medications and infections in RA leading to defective cell-mediated immunity
Medications Bacteria Fungal Protozoan Viral
Corticosteroids, Cyclophosphamide and Azathioprine
Gram-positive: Staphylococcus aureus
Streptococcal spp
Nocardia spp
Gram-negative: E.coli
Klebsiella pneumoniae
Pseudomonas aeruginosa
Other Enterobacteriaceae
Candida Albicans
Aspergillus Spp
Human herpes virus
Measles virus -
Varicella zoster virus -
Adenovirus
Cytomegalovirus
Epstein-Barr virus
-Corticosteroids -Cyclophosphamide -Other alkylating agents -Azathioprine -Methotrexate -Cyclosporin A
Nocardia Spp
Listeria monocytogenes
Salmonella Spp
Mycobacterium Spp
Histoplasma capsule
Coccidioides immiti
Cryptococcus neoformans
Pneumocystis carina -
Strongyloidiasis stercoralis
-Azathioprine -Corticosteroids (high dose) -Cyclophosphamide
Haemophilus Influenzae
Streptococcus pneumoniae
43
Table 2.2. Summary of studies showing the rate of SIs in patients treated with synthetic and biologic DMARDs
*PYs- Patients years - For the period 1955 – 1994, rates may have declined over time #Mean follow-up (period of observation) was 1.4 years ^ ETA, INX and ADA
Study Group Study Design Rate of SIs per 100PYs (synthetic
DMARD)
Rate of SIs per 100PYs (biologic
DMARD) Reference
Galloway J B, et.al. (2011)
BSRBR Registry Review (UK) 3.20 (csDMARDs controls) 4.20 (all Bx DMARDs) ^ [63]
Doran, et.al. (2002) RA vs Population Controls 19.23 per 100 PYs* NA [30]
Listing, et.al. (2005) German RABBIT Registry Review (GDR)
2.28 (csDMARDs controls) 6.15 (INX) and 6.42(ETA) [17]
Lacaille, et.al. (2008) Large RA cohort (n=27,710) 4.5 -5.5 per 100PYs NA [27]
Greenberg, et.al. (2010) MTX vs controls (n=7,971) 3.1 – 3.2 per 100 PYs# NA [24]
Askling J, et.al. (2006) Swedish Biologics Register - 4.5 (INX, ETA and ADA) [39]
Atzeni, et.al. (2012) GISEA Registry (Italy) NA 3.18 (for INX, ADA and ETA) [48]
van Dartel SAA, et.al. (2012)
DREAM Registry (Netherlands) NA 2.91 (over 5 years) [14]
44
Nolla, et.al. (2000) reported that among RA patients, during 1990-1998, the most prevalent
bacterial infections were Staphylococcus aureus and Streptococcus pneumonia [16]. Both are
Gram-positive cocci. They also showed that skin infection was the principal source of infective
disease in RA patients and S. aureus was among the most important pathogens for septic
arthritis. S. pneumoniae was also a relevant pathogen in septic arthritis in RA patients, but it
was less frequent. The majority of cases of septic arthritis in RA were mono-articular with
involvement of the knee, elbow and wrist most often reported (Tables 2.1 and 2.3) [6].
Table 2.3. Serious Infection rates for diverse biologic agents (numbers per 100 PYs)
Anti TNF 4.90, [95%CI 4.4-5.4, 57 trials], n = 26492, Cum. Exp. = 29429 years
ABT 3.04, 95%CI 2.49-3.72,11 trials, n = 5953, Cum. Exp. = 6070 years
RITUX 3.72, 8 trials, n = 2926, Cum. Exp. = 2687 years
TCZ 5.45, 13 trials, n = 5547, Cum.Exp. = 4522 yrs.
TOF# 2.93, 14 trials, n = 5671, Cum.Exp. = 12,664 yrs.
ETA 4.06
ADA 5.04
GOL 5.31
INX 6.11
CERT 7.59
# denotes long term extension studies, Cum. Exp. denotes cumulative exposure. TNFi = Tumour Necrosis Factor inhibitor, ABT = Abatacept, RITUX = Rituximab, TCZ = Tocilizumab, TOF = Tofacitinib. Data extracted from Strand V et.al, Arthritis Research and Therapy 2015;17:36 [64]
3.3. Risk factor categories
Many studies have shown a greater than two-fold increased risk of SI in RA patients [1,2,7-20].
There are several contributing factors involved. Briefly these include:
The pathobiology of the disease itself;
Chronic comorbid conditions, such as diabetes mellitus, heart failure, lung or kidney
disease, bronchiectasis and alcoholism;
Age: elderly-onset RA patients are more vulnerable;
Drug dosage, duration of treatment, and side effects: it has been shown that some drugs
at high dosage and prolonged treatment therewith confer significant risk, especially in
older patients with RA;
45
The immunosuppressive nature of at least some of the drugs used to treat RA; these
include a range of medications, such as corticosteroids, synthetic DMARDs, and
biologic DMARDs;
Genetic factors: these include mannose binding lectin (MBL) deficiency, which has
recently been shown to contribute significantly to serious infections in RA and is the
commonest form of innate immune deficiency. In addition, hypogammaglobulinemia
(common variable immunodeficiency or CVID and selective IgA deficiency or SIgAD),
which are much less common but may, nevertheless, occasionally contribute to SIs.
Roberts, et.al. (2015) have reported immunoglobulin deficiency after rituximab for
lymphoma and rheumatoid arthritis [21,22]; and
Lifestyle factors, such as poor diet, reduced physical activity, smoking, and alcohol
consumption.
3.4. The impact of medications (non-biologics)
A summary of studies showing the rate of SIs in patients treated with synthetic and biologic
DMARDs is shown in Table 2.2. Galloway, et.al. (2011) showed the SI incidence rates to be
42/1000 and 32/1000 patient-years for anti-TNF and csDMARDs respectively. And the risk did
not differ significantly between the three agents; adalimumab, etanercept and infliximab. The
risk was highest during the first six months of therapy [23]. Greenberg et.al. showed that a
major risk factor for infection is the immunosuppressive therapy used. They also showed that
newer therapies for RA may lead to increased rates of infection by pathogens, such as
Mycobacterium tuberculosis [24]. In another study, to examine the association of methotrexate
(MTX) and tumour necrosis factor (TNF) antagonists with the risk of infectious illness,
Greenberg et.al. showed that MTX, TNF antagonists and prednisone at doses >10 mg daily
were associated with increased risks of infections overall. Low-dose prednisone and TNF
antagonists (but not MTX) increased the risk of opportunistic infections [24]. Van Dartel, et.al.
(2013) showed the incidence rates for a first serious infection in patients with RA per 100
patient-years were 2.61, 3.86 and 1.66, for adalimumab, infliximab and etanercept, respectively
[14]. The impacts of other medications are discussed below.
46
3.5. Corticosteroids
Corticosteroid (CS) use is a major contributor to SIs in RA. The effects are dose- and duration-
dependent [25]. Both high dosage and the duration of CS treatment confer significant risk,
especially in older patients with RA. The infection risk has been clearly shown to be dose
dependent, but whether there is a minimum safe dose with respect to serious infection risk is
unclear. Of considerable concern, a patient who has taken at least 5 mg of prednisolone daily
for three months has a 30% chance of hospitalization due to infection [26]. Therefore, in the
treatment of RA, in order to minimize the risk of an SI, the lowest possible dose of CS for the
shortest possible duration should be prescribed [25]. Increasingly, with the advent of more
effective synthetic and biologic DMARDs, the scope to progressively taper and switch from
CS to DMARDs alone has increased.
Listing, et.al. showed that there is evidence that glucocorticoids (GCs) increase the risk of
serious infections up to 4-fold in a dose- dependent manner. In addition, anti-TNF-α inhibitors
increase the serious infection risk up to two-fold. The risk of infection is substantial if patients
need higher dosages of GCs in addition to treatment with anti-TNF-α therapy. It was
recommended that such combination therapies should avoided, if possible, especially in
patients with additional risk factors such as older age or comorbid conditions [20].
3.6 Synthetic DMARDS
Whether synthetic DMARDs at recommended doses contribute to infections in RA is uncertain
and still a matter of conjecture. Lacaille et.al. (2008) conducted a retrospective, longitudinal
study of a population-based RA cohort in British Columbia, Canada (from January 1996 to
March 2003). In this study, a total of 27,710 RA patients provided 162,710 person-years of
follow-up. The authors showed that 92% of patients had at least one type of mild infection and
18% had a SI. Corticosteroids were shown to be unequivocally implicated in Sis, with an
adjusted rate ratio of 1.9 (CI 1.75-2.05) [27]. Importantly, these investigators showed that use
of DMARDs without corticosteroids was not associated with an increased risk for SI [adjusted
rate ratio of 0.92 (CI 0.85-1.00)]. They concluded that, unlike corticosteroids, synthetic
DMARDs, in general, do not elevate the risk of serious infection in RA. It is, however, worth
noting that, in their study, the SI rate for RA patients receiving cyclophosphamide (CYC) was
19.8 to 39.4 per 100 patient years of exposure, which is well above the rate seen for SIs in RA
47
overall (~ 4.4 to 5.5 per 100 Pys in their study), suggesting that some immunosuppressive
DMARDs might still be an exception to the rule. CYC of course is now rarely used as a
DMARD in uncomplicated RA.
Dixon et.al. (2016) [10] conducted a prospective study of participants in the British Society for
Rheumatology Biologics Register (BSRBR). They compared synthetic DMARD-treated
patients (n=1,354) with anti-TNF (biologic DMARD)–treated patients (n=7,664). After
adjustment for baseline risk, it was concluded that anti-TNF therapy was not associated with
increased risk of SI overall, compared with synthetic DMARD treatment. However, they did
show that anti-TNF therapy was associated with serious skin and soft tissue infections [10].
The impact of other DMARDs on the development of infections in RA patients has also been
investigated. The medication-related findings are set out below:
(i) Cyclosporine (CyA, Neoral), which is a fungal peptide, inhibits interleukin-2 and
proliferation of T-cells and promotes apoptosis in macrophages. When used in combination
with methotrexate for treatment of severe RA, CyA can increase the rate of urinary tract
infection (UTI) [7,28].
(ii) Methotrexate (MTX, Methoblastin)-related infections are varied and appear to be dose-
dependent. Because MTX is commonly used in combination with other drugs, it is often
difficult to assess the contribution of MTX alone. There have been several studies which have
investigated MTX and its role in the development of infection. For example, in a randomized
controlled trial (RCT), incorporating 571 RA patients who were treated with a mean MTX
dosage of 10.8 mg/week, without concomitant biological DMARDs, Sakai et.al. (2011) showed
that MTX did not confer an increased risk for serious infections in RA patients [15]. However,
there were limitations to this study, not least the lower mean dosage of MTX than that
commonly used in the United States, Australia and Europe. Boerbooms et.al. (1995) in a six-
year open prospective study and in a 12-month randomized double blind trial comparing MTX
with AZA, showed that the infection rate during MTX treatment was higher in severe RA than
in moderate RA. Once again, this highlights the likely contribution of inherent disease activity
to SI risk [29].
Doran et.al. [30] reported that the hazard ratio for SIs in RA patients treated with MTX was
0.96 while Greenberg et.al. (2010), who followed a total of 7,971 patients, showed that the rate
48
of infection per 100 person-years was increased among MTX users. They expanded their
studies to TNF antagonists and prednisolone and concluded that both MTX and Prednisolone,
at doses more than 10 mg daily, were associated with increased risks for infections overall [24].
Bernatsky et.al. (2007) in a cohort of 23,733 RA patients, showed that methotrexate increases
the rate of pneumonia (RR: 1.16, 95% CI: 1.02–1.33) [7] (Table 2.2).
3.7 The impact of medications (biologics)
Due to their modes of action and the fact that they target cells involved in the immune system,
there is an ongoing concern that these medicines may potentially increase the risk of SI in RA
[31-33]. In this study, the orally active tofacitinib, a tyrosine kinase inhibitor (TKI) is included,
but other TKIs in development have been excluded. We will now consider the groups of
biologic agents in turn.
3.8. TNF-α Inhibitors
Biologics such as Adalimumab, Certolizumab, Etanercept, Golimumab and Infliximab inhibit
TNF-α and thereby modulate the inflammatory process in RA. However, TNF-α is also
important for defence against common and uncommon infections. When TNF function is
inhibited, there is increased risk of diverse infections. These include (i) bacterial infections such
as Gram-positive and Gram-negative bacteria, Mycobacterium tuberculosis, atypical
mycobacterial infection, Listeriosis monocytogenes, (ii) viral infections e.g. cytomegalovirus
(CMV), and (iii) fungal infections e.g. Pneumocystis jirovecii, aspergillosis, histoplasmosis,
coccidioidomycosis and cryptococcal infections [34,35].
The evidence for re-activation of Mycobacterium tuberculosis infection in RA patients has been
discussed in at least two different studies [36]. All TNF inhibitors have a propensity to re-
activate tuberculosis. Infliximab appears to confer greater risk than Etanercept [31,34]. There
is also a significantly increased rate for Hepatitis B virus reactivation, especially when
immunosuppression is diminished or withdrawn. Therefore, a combination of treatments with
hepatitis B (HB) antiviral agents in conjunction with TNF inhibitors is suggested in patients
with evidence of previous HB infection [35]. In addition, there is a known, albeit small, increase
in risk for herpes zoster and a very small risk for leukoencephalopathy (PML) in TNF inhibitor
recipients [36,37]. Historically, the greatest risk for PML has been associated with use of
Natalizumab in multiple sclerosis, but the risk for TNF blockers and Rituximab in RA is not
49
negligible and will require further study to accurately quantify [31,38] and predict
susceptibility. A meta-analysis in 2006 revealed that anti- TNF treatment can also increase the
risk of serious pyogenic infections [8]. The German Biologics Registry investigators found the
risk of serious pyogenic infection to be two-fold [8,10,17]. In contrast to the above studies, the
BSRBR and the Swedish Arthritis Treatment group have reported that a non-significant relative
risk ratio exists for severe infections in patients treated with TNF inhibitors [10,39]. These
differences may be explained by the longevity of the studies. SIs appear to be much more
frequent within the first year of usage / observation. Thus, long term follow-up studies may
report lower rates of SI compared to short-term studies.
It is worth noting that van Dartel, et.al. (2012) found that Adalimumab and Infliximab conferred
higher, albeit similar risks for serious infection in RA patients, whereas Etanercept conferred
lower risk [14]. In addition, Trung, et.al. (2013) in their studies provided a table (Table 2.3) to
categorize the risk of infection with different anti-synovitis medications. In that study, it is
reported that Etanercept, Infliximab and Golimumab were associated with the highest rates of
serious infection. It was shown that Etanercept, Adalimumab, Abatacept and Tocilizumab were
associated with opportunistic infections and tuberculosis (TB) [40]. In a systematic review by
Greenberg et.al. (2002), it was demonstrated that some of the anti-TNF medicines increased
the rates of opportunistic infections while traditional immunosuppressants such as
corticosteroids and synthetic DMARDs were major risk factors for serious infection in RA
(Table 2.2) [2]. Moreover, Dixon et.al. (2006) in an observational study of a large cohort of RA
patients (n=7664) enrolled in the BSRBR, emphasized the important role that TNF has in host
defence in the skin and soft tissue [10]. In their study, patients who were treated with anti TNF-
α agents, as compared to synthetic DMARDs, developed more serious skin and soft tissue
infections. However, importantly, they found that the overall risk of serious infection for anti-
TNF medicines compared to synthetic DMARDs was the same in both groups [10].
3.9. Abatacept (ABT), Rituximab, Anakinra, Tofacitinib and Tocilizumab
ABT safety has been evaluated in several long-term extension (LTE) studies (duration usually
2-3 years). Within this timeframe, in respect to SIs, ABT performs well with SI rates of 1.6 to
3.6 per 100 PYs of treatment in age unstratified RA recipients [41,42]. Given that up to 60% of
these patients were also taking corticosteroids in doses not always clearly defined, the rates are
low for the most part and somewhat lower than for most other biologic agents (Table 2.3). The
50
elderly is more vulnerable as is true in respect to SIs in general and especially after there has
been an antecedent hospitalization for infection, whereupon rates of 26.5 per 100 PYs apply for
ABT and 36.1 for ETA [42,43]. Lahaye, et.al. found that SI rates in Abatacept recipients rose
progressively from 1.73 per 100PYs in persons under 50 to 4.65 in persons 50-64, 5.90 in
persons 65-74 and 10.38 per 100PYs in persons equal to or greater than 75 years of age [43].
Thus, whilst relatively safe in the young and up to extended middle age, the SI rates rose
concerningly for ABT in those over 65 years of age and especially when there has been an
antecedent hospitalization for an infection (Table 2.3).
For Rituximab, Tocilizumab and Tofacitinib, the rates of SI are comparable to those reported
for all TNF inhibitors. However, it should be noted that the cumulative exposure for most of
these agents, like the TNF inhibitors is limited and mostly does not exceed 2 years.
Furthermore, not enough additional data is available to evaluate associated SI risk factors in
these cohorts. For example, a breakdown for age, corticosteroid dosage and important
comorbidities such as diabetes, neutropoenia and lymphopenia is not available sufficiently
often to allow these parameters to be taken fully into account in respect to their independent or
additive effect on SI risk.
In the case of Infliximab (INX) and tocilizumab (TCZ) there is some data, which suggests that
SIs are dose dependent with higher rates seen with higher doses [44]. This has already been
referred to in respect to INX. For TCZ, SI rates of 3.4 per 100 patient’s years (100PYs) were
observed for comparator groups, 3.5 per 100 PYs for TCZ 4 mg/Kg and 4.9 per 100 PYs for
TCZ 8 mg/Kg [45]. In contrast, for Rituximab (RITUX), the SI rates for 500 mg x 2 versus
1000 mg x2 at 24-week intervals were similar at 2.62 and 1.96 SIs per 100PYs [46]. There is a
limitation in this systematic review, and it is not clear if discussed doses are adjusted for Body
mass index (BMI) or not.
Salliot, et.al. [47] investigated the risk of SIs during treatment of RA with rituximab, abatacept
and anakinra. SI frequencies were investigated using meta-analyses of randomized placebo-
controlled trials. It is important to remember that this approach inevitably is short term due to
the design of the trials. Moreover, sicker patients are often excluded. Nevertheless, no
significant increase in the risk for SIs attributable to these biologics was observed. The authors
concluded that, based on these randomized placebo-controlled trials, rituximab, abatacept and
51
anakinra have a relatively good safety profile for SIs. However, an increased risk for SIs was
observed for high doses of anakinra (⩾100 mg per day) in patients with comorbidities.
3.10. Risks associated with combination therapies
It is now common practice to combine synthetic DMARDs with biologic DMARDs, since
efficacy is greater. Some studies have shown that synthetic DMARDs in combination with anti-
TNF-α increase the rate of SIs. For example, Atzeni, et.al. (2012) in a case control study
examined 2,769 patients with long term RA [48]. Treatment with corticosteroids and other
synthetic DMARDs in combination with various anti-TNF agents, viz. Infliximab (INX),
Adalimumab (ADA) and Etanercept (ETN), was investigated. The authors found that the risk
of SI was significantly different across these medication groups (p<0.0001). In these patients,
the following factors were identified as significant infection predictors: (i) The concomitant use
of corticosteroids (p<0.046 with hazard ratio (HR) of 1.849) (ii) concomitant DMARD
treatment during anti- TNF therapy (p=0.004 with HR of 2.178) (iii) advanced age at the start
of anti-TNF treatment (p<0.0001 with HR of 1.03) and (iv) the use of INX or ADA rather than
ETN ( p<0.0001with HR 4.291 for INX vs ETA and p=0.023 with HR 1.942 for ADA vs ETA).
In this study the authors also found that treatment with anti-TNF was associated with a small,
but statistically significant risk of SI (HR of 1.03 and P < 0.0001). In Atzeni et.al’s study,
disease duration and the disease severity score were not found to be predictive of serious
infection [48].
In a systematic study by Campbell, et.al. (2011), the effect of tocilizumab (TCZ), in
combination with MTX, in patients with RA was investigated [49]. The researchers concluded
that this combination treatment for RA is associated with a small, but significantly increased
risk of adverse effects and infections. Their meta-analysis revealed that tocilizumab 8 mg/kg
compared with controls increased the risk of infection. This risk is comparable with other
biologic agents, although the risk of serious infection may be less than that for TNF antagonists.
Perhaps more so than any other biologic agent, the capacity of IL-6 antagonists to markedly
reduce CRP further compounds the difficulty in recognizing serious infection, since great
reliance is usually placed on the CRP concentration when determining the probability of an SI
in an unwell rheumatoid patient. Such delays may adversely affect patient outcomes.
52
3.11. Tuberculosis (TB) and non-tuberculous mycobacterial (NTM) infections
In a recent meta-analysis conducted by Winthrop, et.al. (2015), both TB and NTM infections
were shown to be increased in patients with RA who have been treated with a range of biologics
[36,50] These include Infliximab, Etanercept and Adalimumab, which target TNF-α, as well as
Rituximab, which targets CD20 receptors on the surface of B cells. All these agents have been
shown to re-activate TB and predispose to NTM infections, albeit at different rates. Infliximab
was implicated in TB and NTM infections (11 and 7 cases respectively). In contrast, in this
meta-analysis Abatacept was not shown to predispose to TB or NTM infections. It remains
important to carefully screen for latent TB, both clinically and otherwise (Mantoux skin testing,
Quantiferon GOLD testing) and where necessary, to treat these conditions appropriately before
initiating bDMARDs in RA.
3.12. Serological and other laboratory parameters that influence SI risk
Diverse cellular and serological abnormalities are known to increase susceptibility to infection.
These include neutropoenia, especially in the context of Felty’s syndrome, where high disease
activity is often a factor as well, lymphopenia, immunoglobulin deficiencies (innate and
acquired) and terminal complement deficiencies, although the frequency of Ig and terminal
complement deficiency is low or very low respectively. deficiency is far more common with
frequencies in the order of 5-8% in the population in general and 8 - 15% in rheumatoid
populations. The prevalence of serious infection in ARAD participants was 2.92 % of all patient
visits. The rate in other studies such as the study by Doran et al. in Minnesota US reported 9.6
infections/100 person-years (1). The reasons for this difference are partially due to the different
study designs, settings and therapeutic guidelines [2]. Periodontal infection occurs due to
almost twenty different bacterial species and occurs about two-fold higher in RA patients. In
addition, the prevalence of moderate to severe periodontitis in RA patients is almost 51% which
is more than age and gender matched patients with osteoarthritis (26%) [1]. Concurrent diseases
in patients with RA include depression (15%), asthma (6.6%), cardiovascular events (6%),
cancer (4.5%), and chronic obstructive pulmonary disease 3.5%.
3.13. Mannose Binding Lectin (MBL) and other immune deficiencies
Mannose Binding Lectin (MBL) deficiency is implicated in a variety of infections in neonates
and children, but less so in otherwise healthy adults [11,51-53] Mannose Binding Lectin (MBL)
53
is a component of the innate immune system. It is a carbohydrate binding protein produced by
the liver and is involved in innate immunity [54]. Structurally, this molecule comes in trimer
and tetramer forms and binds to the glycan on the pathogen’s cell surface mannose receptor.
Generally, immune-compromised patients and patients with chronic diseases or impaired
adaptive immune systems including those with Mannose Binding Lectin (MBL) deficiency
have increased risks of serious infection [11,55-57] Mannose Binding Lectin (MBL) has also
been shown to have roles in manifestations of RA disease and the development of other
complications of RA, such as cardiovascular disease [58].
In a recently reported study of risk factors for SIs in RA, both undetectable Mannose Binding
Lectin (MBL) and CS use (prednisolone at doses of 5 mg per day or more) were shown to
confer a 4-5-fold increased risk for SIs [53]. This takes on greater importance when it is
remembered that up to 15% of RA patients have undetectable Mannose Binding Lectin (MBL)
and that rates of CS use in RA, although they vary a great deal from centre to centre are still
high despite the availability of more efficacious DMARDs (up to 70%) [53]. In fact, apart from
severe neutropoenia, such as in Felty’s syndrome for example, no other laboratory marker
appears to confer greater SI risk then undetectable Mannose Binding Lectin (MBL). Common
variable immunodeficiency (CVID) is estimated to affect up to 1 in 25,000 individuals and can
be associated with auto-immune diseases including RA [59-61]. The exact risk associated with
CVID or its various disease expressions such as panhypogammaglobulinaemia, selectively
reduced immunoglobulins (e.g. IgA deficiency) and IgG subset deficiency in RA is unknown,
but given that these deficiencies are much less frequent than undetectable Mannose Binding
Lectin (MBL), they are likely to be relatively less important clinically.
Selective IgA deficiency or SIgAD, which is the most common of these immunoglobulin
deficiencies, occurs in less than 1 in 100 persons of Arabic descent and in less than 1 in 800
Caucasians in the UK. Although increased rates of severe respiratory tract infections are
observed in SIgAD persons, compared to unaffected controls (3-fold increased risk), life-
threatening infections were not recorded in this group [62] Elsewhere, risk factors predisposing
to the development of hypogammaglobulinemia and infections post-rituximab treatment have
been reported [63]. Terminal complement components C5 - C9, otherwise referred to as the
membrane attack complex also predispose to recurrent infection, especially with encapsulated
organisms, such as Neisseria, but do not appear to associate strongly with auto- immune
54
diseases and are relatively rare in Caucasians, although not in Afro-Americans and probably
not in native Africans.
3.14. Implications for Clinical Practice
The treating clinician needs to consider the following when choosing therapeutic agents for
patients with RA who are at risk for SIs:
3.14.1. Age
SIs are substantially increased in persons of advanced age -for example in a large USA
Medicare beneficiaries’ cohort, the SI rate in those over 65 was 14.2 per 100PYs compared to
4.8 in those less than 65 years of age [43]. A recently reported study by one of the authors
indicates that the risk of an SI increases by 19% for every 5 years increase in age and by 41%
for every 10-year increase in age [53]. The prescribing clinician should consider, the differing
relative risks for SIs when prescribing DMARDs for the old and the very old rheumatoid
patient.
3.14.2. Corticosteroid (CS) Use and Dosage
In RA patients, the SI risk is appreciably higher in recipients of CS. Furthermore, this risk is
most likely dose dependent. For example, in one study, a daily dose of 10 mg of Prednisolone
or more was associated with an odds ratio (OR) for an SI of 4.70, whereas a dose of 1-4.5 mg
per day was associated with an OR of 2.57 [9]. Initial use of CS at first presentation may be
unavoidable, but scope to wean the dose of CS should be explored, as a matter of priority, once
a satisfactory response to synthetic DMARD or biologic therapy has been achieved. The
minimum safe dose of CS is unknown. Indeed, in respect to SIs, there may be no safe minimum,
but until more definitive data is available, a dose of 3 mg/day may represent a reasonable
compromise target for maintenance of lower disease activity and at the same time minimization
of SI risk [64,65].
3.14.3. Doses of biologic agents
The dose of any therapeutic agent should be periodically reviewed. For some biologic agents,
where there is dose flexibility, lower SI risks have been convincingly demonstrated with lower
doses of the biologic agent. For example, for Infliximab (INX), a 3 mg/Kg dose confers less
risk than 6 mg/Kg and for Adalimumab (ADA), 40 mg every other week (EOW) confers less
55
risk than 40 mg qw. Similar observations have been made for Tocilizumab (TCZ) where 4
mg/kg was found to confer less SI risk than 8 mg/ kg [46]. Where SI risk is a major concern
and disease control will allow, reduced doses of DMARDs in general, including bDMARDs,
should be considered or monotherapy with a bDMARDs should be preferred.
3.14.4. Vaccination Pneumonias and lower respiratory tract infections in general are the most common SIs in all
RA patients irrespective of biologic or synthetic DMARD therapy. Pneumococcal vaccination
should be advised, unless contra-indicated, and follow-up post- vaccination serology performed
to confirm adequate immunity. When it becomes more widely available/accessible, the new
subunit zoster vaccine (Shingrix) should be considered, especially in those most at risk due to
age.
3.14.5. Comorbidities related and unrelated to RA
Amongst related disorders, consider Felty’s syndrome and other conditions that may give rise
to Neutropoenia. Consideration should also be given to innate immune deficiencies such as
Mannose Binding Lectin (MBL) deficiency and Hypogammaglobulinemia. Undetectable
Mannose Binding Lectin (MBL) concentrations carry a considerable risk for SIs in RA
(OR=4.67) comparable to 10 mg of prednisolone daily [3,9]. Since pneumonias are more often
fatal in Mannose Binding Lectin (MBL) deficient persons and since 8-15% of RA patients are
Mannose Binding Lectin (MBL) deficient (serum concentrations less than 50 ng/mL), there is
an even stronger case for pneumococcal vaccination in those with RA with undetectable
Mannose Binding Lectin (MBL). The treating clinician should consider determining the
Mannose Binding Lectin (MBL) concentration in advance of commencing CS, csDMARDs or
bDMARDs therapy, as this information taken together with age and CS usage will inform
decision making in respect to the nature and risks of therapy.
4. Conclusion
In conclusion, SIs are far more common in RA than in the general population, CS are associated
with an appreciable increase in SI risk (5 fold at doses of 10 mg per day or more), most
commonly used and currently favoured synthetic DMARDs confer a small or no risk, biologic
DMARDs confer moderate risk in the first year of therapy and then a diminishing risk
thereafter, and higher dose biologic or combination biologic therapy should be avoided since
56
the serious infection risk is unacceptably high. Combinations of CS and bDMARDs or of
csDMARDs and bDMARDs should be used with caution in those with a track record for one
or more SIs and perhaps also in the elderly. Undetectable Mannose Binding Lectin (MBL) is a
major risk factor for SI in RA, comparable to Prednisolone 10 mg per day or more and
measurement thereof will inform SI risk stratification.
57
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63
CHAPTER 3
Descriptive analysis of the infection status in rheumatoid
arthritis patients (using ARA data)
64
Abstract
Objectives: To conduct a descriptive analysis of the type and frequency of self-reported
infections in rheumatoid arthritis (RA) based upon reports to the Australian Rheumatology
Association Database (ARAD). These include the effects of anti-RA medications in the
development of infections across various organs.
Methods: ARAD reports (patients’ responses to questionnaires) from 2001 to 2014 were
examined in respect to demographic and treatment categories. Observed differences were
subjected to descriptive statistical appraisal.
Results: Based on this analysis, the mean age in RA is 61.47 years and, in the group taking
csDMARDs, it is 59.24 years and, in the group taking bDMARDs, it is 62.62 years. Also,
patient groups who were taking csDMARDs alone and bDMARDs alone were comparable
based on risk factors, such as taking prednisolone, smoking or alcohol consumption. Finally, in
comparison to bDMARDs, taking csDMARDs alone was significantly associated with higher
rate of infection in a few organs, such as lung, urinary system, and GIT.
Conclusion: Compelling evidence suggests that RA can increase the risk of infection and
potentially serious infection and that different medications are potentially associated with this
risk. The Australian RA population in ARAD shows that risk factors, such as smoking, can play
a role in the development of serious infection. Although it seems that csDMARDs alone is
connected to more rates of infection than bDMARDs alone, the difference is only significant
in a few types of infections. The findings in this analysis indicate that smoking is a likely
contributor to increased infection risk in RA. The Australian RA population in ARAD
shows that risk factors, such as smoking, can play a role in the development of serious
infection. Although it seems that csDMARDs alone is associated with higher
frequencies of infection than bDMARDs alone, the difference is only statistically
significant for a few types of infections. Accordingly, these apparent differences
require closer scrutiny. Importantly, the findings contrast with those reported in most
registries, where bDMARDs use is associated with higher rates of infection or at least
serious infection and at least in the first year of treatment. The different definitions
65
applied for infection in ARAD and the very long follow-up may account for the
differences observed.
66
1. Introduction Rheumatoid arthritis (RA) as a chronic multisystemic, immuno-inflammatory disease is a
common disease affecting millions of people worldwide [1]. In developed countries, RA affects
0ꞏ5–1ꞏ0% of adults, with 5–50 per 100 000 new cases annually[2]. The female to male ratio in
this disease is more than three to one. The risk of RA increases with age, perhaps pointing to
loss of tolerance as the immune system undergoes age-related loss of antigen discriminatory
capacity. Genetic and environmental risk factors also contribute to the risk of RA [3].
There are some well-known risk factors in this disease, including genetic susceptibility, gender,
age, smoking, infectious agents, hormonal factors and ethnic factors[4]. Roughly 50% of the
risk for RA is attributable to genetic factors. Around 30 genetic loci have been implicated in
RA. These suspicious genes have been classified, however, the pathogenesis of their influence
in developing RA is still unclear[4]. Smoking is also a main environmental risk factor.
Smoking-related tissue necrosis is thought to be of influence in the onset of excessive
inflammation and immune response to self-antigens [3]. Age and sex can also play
aetiopathogenetic roles.
The incidence, severity, and the outcome of the disease show inconsistencies between diverse
ethnical-origin units, which is related to socioeconomic levels, as well as genetic and
environmental factors. For example, patients in underdeveloped countries have poorer
prognosis. They demonstrate a more severe clinical course due to limited access to medical care
and medication, amongst other factors. Studies on RA has revealed that various genetic and
environmental factors can influence the disease in diverse ethnical groups[5].
RA signs and symptoms include persistent synovitis and systemic inflammation due to
autoantibodies particular to rheumatoid factor (RF) and antibodies to certain peptides. The
typical symptoms at onset are symptoms of synovitis (pain, swelling, loss of function, including
stiffness, restricted motion, and possible heat and redness in joints, if severe), which are most
often in a symmetrical pattern and sometimes accompanied by systemic symptoms, such as
lethargy /malaise, weight loss and sometimes fever[6].
Clinical onset of this disease is generally symmetrical involvement of the small joints, pain,
morning stiffness, and limitation of movement for more than one hour. RA may also involve
67
any joint, but most frequently it involves the meta-carpophalangeal (MCP) joints, the proximal
interphalangeal (PIP) joints, the wrists, the metatarsophalangeal (MTP) joints and the knee
joints[6]. Articular symptoms are usually symmetric and systemic pattern. The large joints
which may be involved include the shoulders, elbows, knees and ankles. The small joints
include the MCP, PIP, MTP, thumb interphalangeal joint and wrists[7]. The clinical
presentation of RA varies, although an insidious onset of pain accompanied by symmetric
swelling of the small joints is the most common symptom cluster at the outset [7]. Rheumatoid
arthritis also increases the risks of several other comorbidities including cardiac disease,
depression, lymphoma and other malignancies [7]. Complications are not limited to the joints
and can involve extra-articular tissues including vasculitis and ophthalmic,
neurologic, and cutaneous complications[8]. RA is not directly life-threatening, but
uncontrolled active rheumatoid arthritis can also lead to joint damage, decreased quality of life,
disability, and cardiovascular comorbidities[9].
Complications are not limited to the joints and can involve extra-articular tissues, including the
serosal surfaces (pleural and pericardial effusions), bone marrow ( anaemias and cytopaenias)
the lungs ( interstitial lung disease ), blood vessels (vasculitis), the eyes (episcleritis and
scleritis with blindness due to occasional perforation), neurologic and cutaneous
complications[9]. RA is not directly life-threatening but uncontrolled active rheumatoid
arthritis can be very debilitating, reduce the quality of life, contribute to substantial disability,
and contribute to cardiovascular comorbidities [10]. Importantly, infection is the commonest
cause of death in RA; the disease, its treatment and probably co-existent immunodeficiencies
all likely contribute to this increased risk [10].
The pathophysiology of RA is yet to be elucidated completely, but it seems that molecular and
cellular pathways of inflammation with involvement of both B cells and T cells play important
roles. Distinct autoantibodies are always present in the sera of patients[10]. Rheumatoid factor
(RF), both IgM rheumatoid factors (IgM-RF) and IgG rheumatoid factors (IgG-RF) are present
in different stages of RA pathogenesis. The IgM rheumatoid factors (IgM-RF) are the main RF
class found in RA and they can be detected in 60–80% of established cases of RA and 50–60%
of RA patients in the early stages of the disease[11]. This implies that RF is probably an
outcome of non-specific immune activation[11].
68
Modern treatment of RA has been substantially improved by the introduction of biologic
therapies. For most patients, these drugs represent an effective and safe management strategy;
however, serious infections connected with biologic therapies are a major concern for both
patients and clinicians. Registry data from the UK and Sweden have shown an augmented risk
of serious infection in new anti-TNF starters, especially in the first 6–12 months of treatment
[12]. Infections are usually due to the same organisms seen commonly in the general population,
and a small number of infections are due to opportunistic infections (OI)[12]. Treatment in RA
is usually based on immune suppression through csDMARDs) or (bDMARDs)[13].
1.1. DMARDs Disease-modifying anti-rheumatic drugs (DMARDs) are drugs which reduce the level of
inflammation, slow joint damage and decrease the systemic effects of RA. There are three major
groups; these include:
conventional synthetic DMARDs (csDMARDs),
targeted synthetic DMARDs (csDMARDs), and
biological DMARDs (bDMARDs)[14].
csDMARDs alone or Conventional synthetic DMARDs include: 1- Methotrexate (oral or
parenteral), 2- Hydroxychloroquine, 3- Sulphasalazine, 4- Leflunomide, 5- Azathioprine, 6-
Cyclosporin.
1.2. bDMARDs These are engineered medications and are designed to regulate the immune response.
Hereditarily-engineered proteins initiating from human genes form biologic drugs targeting
the specific portions of the immune system that fuel inflammation. csDMARDs alone, such as
methotrexate, are less targeted [15].
Biologics are usually used singly or in combination with other non-biologics. What
distinguishes biologics, besides how they work and what they target, is their makeup, how they
are delivered, and some risks, although all of them probably confer an increased risk for
infection. Different groups of biologics include: Tumor necrosis factor inhibitors (TNF-
Inhibitors) which block tumor necrosis factor, one of the chemical messengers of inflammation
69
that drives joint inflammation and destruction. Interleukin-1 (IL-1) blocker ( for example
anakinra) which blocks IL-1, the factor with a major role in inflammation, B-cell inhibitor
(rituximab), T-cell inhibitors (abatacept), humira/adalimumab, etanercept or brenzys
/etanercept, remicade/infliximab, actemra/tocilizumab, simponi/golimumab [15].
Others with different mechanisms of action include actemra/tocilizumab (a monoclonal
antibody directed against IL-6 receptor), Interleukin-1 (IL-1) receptor antagonist (for example,
anakinra), which blocks IL-1, B cell depletors (rituximab) and T-cell inhibitors (abatacept). A
new family of Jak inhibitors, which can be taken orally, is now emerging and is in clinical use
with growing uptake. These include tofacitinib and baricitinib.
It should be noted that bDMARDs are not used concomitantly because of concerns regarding
still higher rates of serious infection, however, the evidence base underpinning this fear is not
strong and newer agents with low infection propensity have not been combined with older
agents and studied rigorously in clinical trials. This section will cover the demographic
characteristics of RA, SI, different types of infections and their severities. In addition, there will
be a descriptive assessment of the association between various modalities of treatment in RA
and the severity of different types of infection. Through a comprehensive descriptive analysis,
potential associations and other relationships may emerge. Later in this section observed
differences and potential relationships will be evaluated statistically and discussed in detail.
1.3. Aims and Objectives The aim of this study is to increase knowledge about the pattern of RA in the Australian
population and to determine the frequency and significance of self-reported infections. Specific
objectives in this section include:
• Describe the demographic characteristics of the ARA database and to report the type, severity and frequency of self-reported SIs as well as the relevant potential risk factors for infection.
• Describe the different types of infections in RA and their association with the major treatment modalities, csDMARDs alone or bDMARDs alone.
• Provide essential tools for other researchers from other parts of the world to perform similar analyses and compare demographic characteristics in different parts of the world.
70
• Discuss potential associations between different available modalities for treatment and different types of infections and their severity.
2. Methods
2.1. Data Collection The data were collected from the ARAD, in which a cohort of 3569 RA patients (960 males
and 2609 females) who had completed related questionnaires 28176 times (during 2001-2014),
were investigated for the development of infections. Among the 3569 patients, 459 patients
were eliminated because they had filled out the questionnaire only once. We were left with
3110 patients. Eight duplicates were eliminated, leaving 27709 visits from 3110 patients. All
these visits were examined to capture self-reported infections in different organs and the
medications that were being taken at the time.
2.2. Statistical Analysis Amongst the 3110 Rheumatoid Arthritis patients who had taken part in the study and had filled
in the questionnaire more than once, the central tendency for age and sex distribution was
calculated. In the first step all data were entered in excel. Single, faulty and duplicate reports
were eliminated. Then data was divided into two groups of patients who were taking either just
csDMARDs alone and patients who were taking just bDMARDs alone. Overall, 1653 visits
from 405 patients applied to those taking csDMARDs alone and 323 visits from 80 patients
applied to those taking bDMARDs alone. All the patients who were taking both csDMARDs
and bDMARDs concurrently were eliminated from the analysis at this stage. Both csDMARDs-
alone and bDMARDs-alone participants and the overall RA population were examined closely
and compared in respect to sex distribution, age distribution, smoking history, alcohol
consumption, and different organ infection. Possible differences were tested with the chi-
squared test and the Fisher test, wherever it was applicable.
3. Results and discussions
3.1. Demography of whole RA population The amount and frequency of smoking [16], alcohol consumption [17], T1DM, T2DM[18]
and prednisolone consumption[19] all can play a role in the incidence of infection among RA
71
patients. In addition, a patient’s age and sex can have a different distribution among RA
patients and the normal population. Therefore, in the following tables (tables 3.1 to 3.6), these
differences are explored and compared.
The mean and median were used as points of estimate and accuracy was measured by Standard
Error (Table 3.1). This shows the distribution of age, the number of cigarettes smoked and the
duration of smoking, as well as the amount of alcohol consumed.
Table 3.1 Comparison demography of RA (Data collected from ARAD)
Variable Mean SD Median
Age in RA 61.48 12.31 63.00
Number of cigarettes smoked among smokers 14.89 13.23 15.00
Duration of smoking among smokers 17.26 13.95 16.00
Alcohol Consumption units among alcohol consumers
1.32 0.47 1.00
A sample of 1653 visits, pertaining to 405 patients who were receiving csDMARDs alone and
80 patients who were receiving bDMARDs alone, was examined for the impact of several
predictor variables: age, gender, alcohol consumption, smoking history and prednisolone
intake. The results were compared through chi-squared tests, in the next stage. This is
described below.
3.2. Demography of patients taking purely bDMARDs Only eighty patients were just taking bDMARDs alone during the period of this study (2001 to
2014) without any concurrent csDMARDs. Among this number, 63.75 % were female and
36.25% were male. Smoking as a risk factor for infection was also measured in this population
[16].
Table 3.2 Central tendency among patients who received bDMARDs alone
Variable Mean SD Median
Age
Average number of cigarettes smoked by smokers
Smoking duration (yrs) among smokers
62.62
25.90
21.53
12.57
21.67
12.26
62.50
20.00
22.00
72
The mean age for the patients receiving bDMARDs alone was 62 years old (Table 3.2). 64 %
of the patients who were taking bDMARDs alone were female and 36% were male (Table 3.3).
Table 3.3 Sex distribution among patients who received bDMARDs alone
Sex Frequency % Cumulative frequency Cumulative %
Male 29 36.25 29 36.25
Female 51 63.75 80 100.00
These differences in the sex distribution are best appreciated in the pie chart (Figure 3.1).
Note. Sex1= Male; Sex 2= Female
Figure 3.1 Sex distribution among patients receiving bDMARDs alone
The majority of patients (almost 82%) who were taking bDMARDs alone were non-smokers.
According to centraltendency data, patients, on average, were smokers for 21 years and
consumed almost 26 cigarettes per day (Table 3.2, Table 3.4)
Male36%
Female64%
SEX DISTRIBUTION AMONG BDMARDS ALONE
Male Female
73
Table 3.4 Smoking status amongst patients receiving bDMARDs alone
It is apparent that the majority of patients who received bDMARDs alone were alcohol
consumers (Table 3.5).
Table 3.5 Status of alcohol consumption among patients who were taking bDMARDs
Alcohol consumption among patients who received bDMARDs alone
Alcohol Frequency % Cumulative
Frequency
Cumulative
%
Never 32 40.00 32 40.00
Sometimes 39 48.75 71 88.75
Everyday 9 11.25 80 100.00
Note. Frequency missing = 325
A small majority of patients (almost 52%) who were taking bDMARDs alone were taking
prednisolone as well (Table 3.6)
Table 3.6 Status of taking methyl prednisolone among patients taking bDMARDs alone
Also,
according to
the following
tables,
approximately 7.5% of patients on bDMARDs have current T1DM, whereas 12.5% have
current T2DM (Table 3.7, Table 3.8).
Smoking status amongst patients who received bDMARDs alone
Smoking status Frequency % Cumulative Frequency Cumulative %
No 66 82.50 66 82.50
Yes 14 17.50 80 100.00
Note. Frequency missing = 325
Prednisolone status Frequency % Cumulative Frequency Cumulative %
Never taken 25 31.25 25 31.25
Currently taking 42 52.50 67 83.75
Stopped taking 12 15.00 79 98.75
Don’t know 1 1.25 80 100.00
Note. Frequency missing = 325
74
Table 3.7 Status of T1DM among patients who were taking bDMARDs alone
T2DM was observed to be more common amongst patients who were receiving bDMARDs
alone (Table 3.8).
Table 3.8 Frequency of T2DM amongst patients who were receiving bDMARDs alone
3.3. Demography of patients receiving csDMARDs alone During the period of this study (2001 to 2014), 405 patients were taking csDMARDs alone
without any csDMARDs. The mean age was around 59 years (Table 3.9).
According to the following table (Table 3.9), among all patients with RA, smokers were, on
average, smoking for 12 years about 10 cigarettes per day.
Table 3.9 Mean and central tendency in csDMARDs alone
Variable Mean SD Median
Age
Smoking status among smokers
Smoking duration (yrs) among smokers
59.24
10.49
12.40
12.69
12.12
14.46
60.00
10.00
9.00
Amongst the 405 patients who were receiving csDMARDs alone, 77.04% were female. In
comparison to bDMARDs alone, the proportion of females receiving csDMARDs alone was
higher (Table 3.10).
T1DM Frequency % Cumulative
Frequency
Cumulative
%
Never 73 91.25 73 91.25
Current 6 7.50 79 98.75
Past 1 1.25 80 100.00
Frequency missing = 325
T2DM Frequency % Cumulative frequency Cumulative %
Never 68 85.00 68 85.00
Current 10 12.50 78 97.50
Past 2 2.50 80 100.00
Note. Frequency missing = 325
75
Table 3.10 Sex distribution among patients who received csDMARDs alone
Gender
Frequency % Cumulative frequency Cumulative %
Male 93 22.96 93 22.96
Female 312 77.04 405 100.00
The difference in sex distribution is best appreciated in the following Figure (Figure 3.2). It
can be seen that females predominate.
.
Figure 3.2 Gender distribution among patients who took csDMARDs alone
The majority of patients receiving csDMARDs alone were non-smokers. According to central
tendency data (Table 3.2), the smokers in this group consumed almost 10 cigarette s per day
with an average smoking duration of 12 years.
Table 3.11 Smoking status in those receiving csDMARDs alone
Smoking status
Smoker Frequency % Cumulative frequency Cumulative %
Missing 1 0.25 1 0.25
No 367 90.62 368 90.86
Yes 37 9.14 405 100.00
The majority of patients, up to around 90%, were non-smokers, whereas almost 70% were
consumers of alcohol (Table 3.9, Table 3.11, Table 3.12).
Male23%
Female77%
SEX DISTRIBUTION IN CSDMARDS ALONE
Male Female
76
Table 3.12. Alcohol consumption in those receiving csDMARDs alone
Alcohol Frequency % Cumulative frequency Cumulative %
Never 123 30.37 123 30.37
Sometimes 209 51.60 332 81.98
Everyday 73 18.02 405 100.00
The significance of differences observed between the csDMARDs-alone and bDMARDs-alone
groups were examined by statistical testing. Prednisolone was noted to be used by 46% of
patients receiving csDMARDs alone (Table 3.13).
Table 3.13 Prednisolone usage amongst those receiving csDMARDs alone
Prednisolone status Frequency % Cumulative frequency Cumulative %
Never 110 27.16 110 27.16
Currently 187 46.17 297 73.33
Stopped 107 26.42 404 99.75
Don’t know 1 0.25 405 100.00
T1DM, as another well-known risk for infection, was reported in just 3.71% of all patients who
were taking csDMARDs [20] (Table 3.14).
Table 3.14. Insulin Dependent Diabetes Mellitus in CsDMARDs alone
Frequency of T1DM in patients taking (csDMARDs)
T1DM Frequency % Cumulative
Frequency
Cumulative
%
Never 390 96.30 390 96.30
Current 14 3.46 404 99.75
Past 1 0.25 405 100.00
The frequency of T2DM reported was slightly higher than for T1DM, but still less than 10%
(Table 3.15).
77
Table 3.15 Non-insulin dependent diabetes mellitus in those receiving csDMARDs alone
3.4. Comparison of patients receiving bDMARDs and patients on csDMARDs alone
In order to compare the frequency of qualitative variables, for each variable a separately chi-
square table was used (Table 3.17, Table 3.19, Table 3.21, Table 3.23, Table 3.25, Table 3.27,
Table 3.29, Table 3.35, Table 3.41, Table 3.45, Table 3.55, Table 3.59). In these tables, the
significance of the differences is tested by different test methods, including the Wald chi-square
test (pearson Chi-Square), the Continuity-Adjusted Chi-Square test, the Mantel-Haenszel Chi-
Square test, the Likelihood Ratio Chi-Square test, the Phi Coefficient, and Cramer’s V. Among
these tests, the Wald Chi-Square, which is also known as the Pearson Chi-Square, is the most
commonly used test. The null hypothesis is that the frequency of the variable is similar for
recipients of biologic DMARDs alone and recipients of conventional synthetic DMARDs alone
recipients. A criticism of this test is that the Wald Chi-square fixes the row and column margin
totals which, in effect, makes an assumption about the distribution of the variables in the
population being studied (Table 3.17)[21].
The second test in this table (Table 3.17) is the Continuity-Adjusted Chi-Square test statistic.
This test consists of the Pearson Chi-Square modified with an adjustment for continuity and is
dependent on the sample size. The Mantel-Haenszel Chi-Square test is usually related to the
Pearson Chi-Square test. In the 2x2 case, as the sample size gets larger, the Mantel-Haenszel
and Wald Chi Square statistics tests converge[22]. In the case of 2xC or Rx2 tables, if the
variable with more than two categories is ordinal, the Mantel-Haenszel Chi-square is a test for
trend while the Pearson Chi-square remains a general test for association[22]. This test is
currently calculated and reported in SAS, but it was not evaluated further in this study. The
Likelihood Ratio Chi-Square is asymptotically equivalent to the Pearson Chi-Square and
T2DM Frequency % Cumulative Frequency Cumulative %
don’t know 1 0.25 1 0.25
Never 379 93.58 380 93.83
Current 20 4.94 400 98.77
Past 5 1.23 405 100.00
78
Mantel-Haenszel Chi-Square but not usually used when analysing 2x2 tables. It is used in
logistic regression and log linear modelling which involves contingency tables[22].
[22]. Cramer’s V is derived from the chi-square and in the 2 x2 table, which is identical to the
Phi coefficient. The contingency coefficient, the Phi coefficient, and Cramer’s V are well-suited
for nominal variables in which the order of the levels is meaningless[23].
3.4.1. Prednisolone comparison
Prednisolone is one of the anti-rheumatic medications known to play a role in infection (Chapter
2, section 3.12.2.). It is important to check if prednisolone intake differs between patients who
are taking csDMARDs and patients who are taking bDMARDs (Table 3.16).
Table 3.16 Comparison of prednisolone consumption among patients receiving csDMARDs
alone and bDMARDs alone
Group Response
Status Never
Taking
Currently
taking
Stopped
taking
Not
Known
Total
csDMARDs
Frequency 110 187 107 1 405
% 22.68 38.56 22.06 0.21 83.51
Row % 27.16 46.17 26.42 0.25
Column % 81.48 81.66 89.92 50.00
bDMARDs
Frequency 25 42 12 1 80
% 5.15 8.66 2.47 0.21 16.49
Row % 31.25 52.50 15 1.25
Column % 18.52 18.34 10.08 50.00
Total Frequency 135 229 119 2 485
% 27.84 47.22 24.54 0.41 100
Based on the p-value of the Chi-square in the ARAD sample, there is not a significant
difference between the csDMARDs and bDMARDs groups in taking prednisolone. As the
number of samples in 25 % of calculating cells was less than 5, other tests (chi- square,
likelihood ratio, Mantel- Haenszel, phi Coefficient, Contingency coefficient, Cramer’s V)
were also checked, and all of these tests align with the original finding (Table 3.17) [19].
79
Table 3.17 Chi-squared test for difference in frequency of prednisolone usage between
csDMARDs-alone recipients and bDMARDs-alone recipients, sample size=485
Statistic DF Value Probability
Chi-Square 3.00 6.15 0.10
Likelihood Ratio Chi-Square 3.00 6.15 0.10
Mantel-Haenszel Chi-Square 1.00 2.25 0.13
Phi Coefficient 0.11 -
Contingency Coefficient 0.11 -
Cramer's V 0.11 -
3.4.2. Alcohol comparison
There are also other factors which may play a role with respect to infection susceptibility and
also impact on general health. Alcohol consumption and cigarette smoking are two such factors.
Alcohol consumption in recipients of csDMARDs alone and bDMARDs alone was examined.
The results are shown in Table 3.18 and Table 3.19.
Table 3.18 Comparison of alcohol consumption among patients receiving csDMARDs alone
and bDMARDs alone
Group Response
Status Never
taking
Sometimes Everyday Total
CsDMARDs Frequency 123 209 73 405
% 25.36 43.09 15.05 83.51
Row % 30.37 51.60 18.02
Column % 79.35 84.27 89.02
bDMARDs Frequency 32 39 9 80
% 6.60 8.04 1.86 16.49
Row % 40.00 48.75 11.25
Column % 20.65 15.73 10.98
Total Frequency 155 248 82 485
% 31.96 51.13 16.91 100
80
Notes. Sample size is a combination of 405 patients who were taking csDMARDs alone and 80 patients who were
bDMARDs alone. Based on the chi-square test, the differences in alcohol consumption between csDMARDs alone
and bDMARDs alone were not statistically significant (Table 3.19).
Table 3.19 Chi-squared for differences in frequency of alcohol use between recipients of
csDMARDs alone and recipients of bDMARDs alone, sample size=485.
3.4.3. Smoking comparison
Smoking is another potential risk factor for infection and deterioration in patient health status.
Statistically, it seems that more people in the csDMARDs group intend to smoke (Table 3.20a).
However, this is not statistically significant different from the other group (Table 3.21).
Table 3.20a Comparison smoking status among patients receiving csDMARDs alone and
bDMARDs alone
Group Response
Status No Yes Missing Total
csDMARDs
Frequency 367 37 1 405
% 75.67 7.63 0.21 83.51
Row % 90.62 9.14 0.25
Column % 84.76 72.55 100.00
bDMARDs
Frequency 66 14 0 80
% 13.61 2.89 0.00 16.49
Row % 82.50 17.50 0.00
Column % 15.24 27.45 0.00
Total Frequency 433 51 1 485
% 89.28 10.52 0.21 100.00
Statistic DF Value Probability
Chi-Square 2.00 3.86 0.15
Likelihood Ratio Chi-Square 2.00 3.94 0.14
Mantel-Haenszel Chi-Square 1.00 3.85 0.05
Phi Coefficient 0.09
Contingency Coefficient 0.09
Cramer's V 0.09
81
Different studies reveal the strong connection between smoking and RA. For example,
Criswell et al. (2002), in a cohort study, showed that those who stopped smoking could have
a reduced risk of RA, particularly among postmenopausal women [24]. In a case study by
Padyukov et al. (2004), it was shown that the risk of RA with SE of HLA-DR is strongly
influenced by the presence of an environmental factor (e.g., smoking) in the population at risk
[16]. Costenbader et al. (2006) showed in a cohort study that past and current smoking were
related to the development of RA, in particular seropositive RA [25]. In a meta-analysis by
Sugiyama et al. (2010), it was shown that smoking is a risk for RA, especially seropositive
RA in men. For women, the risk for smokers is about 1.3 times greater than for non- smokers
[26]. Di Giuseppe et al. (2014) showed that lifelong cigarette smoking was positively
associated with the risk of RA, even among smokers with a low lifelong exposure [27].
Furthermore, other studies show a connection between the effectiveness of smoking cession
and better responsiveness of bio-treatment [28]. Sustained smoking cessation within four
years of RA diagnosis is connected to a reduction in mortality risk, this rate is same as non-
smokers. However, smoking more than 5 years after RA diagnosis increased mortality well
above the risk of non-RA patients [29].
According to the ARAD, the rate of smoking between 2001 and 2014 was 10.5% (328/3111).
This was almost 8.9% of all patient visits (2484/27712). Table 3.20b shows the rate of
smoking in the general population in Australia during the same time.
Table 3.20b Comparing rate of smokers during the years 2001 to 2013, Australia [30]
Year %Total smokers
2001 22
2004 20
2007 19
2010 18
2013 15
It seems that the rate of smokers in RA is less than the rate of smokers among the general
population in Australia during those years. Two major possibilities for this discrepancy include
82
the facts that (1) ARAD is a subjective report and data are not reliable, and (2)there are several
risk factors for causing RA, and risk factors other than smoking play a more significant role in
Australia, especially since the rate of RA disease in Australia is higher than in many other
countries [31].
Based on the information in the smoking and alcohol consumption tables, the apparent
differences in smoking history and alcohol consumption status are not confirmed, statistically.
Accordingly, they are unlikely to account for any differences in infections observed between
the csDMARDs and bDMARD groups. There is marginal evidence for differences with respect
to smoking between the csDMARDs-alone and bDMARDs-alone groups (Table 3.21). In this
test, almost 33% of cells had an expected count of less than five, which means that further
statistical testing is required. For this purpose, the likelihood ratio test was performed, and this
test confirmed the results[32].
Table 3.21 Chi-squared for differences in frequency of smoking between csDMARDs-alone
and bDMARDs-alone groups, sample size=485
3.4.4. Sex distribution comparison
Sex differences can also play a major role in many conditions. Therefore, it is important to
determine if there is a sex difference between patients taking csDMARDs and those taking
bDMARDs (Table 3.22).
Statistical tests DF Value Probability Chi-Square 2.00 5.14 0.08 Likelihood Ratio Chi-Square 2.00 4.72 0.09 Mantel-Haenszel Chi-Square 1.00 4.07 0.04 Phi Coefficient 0.10 Contingency Coefficient 0.10 Cramer's V 0.10
83
Table 3.22 Comparison sex distribution among patients receiving csDMARDs alone and
bDMARDs alone (sample Size = 485)
Group Response
Status Male Female Total
csDMARDs Frequency 93 312 405
% 19.18 64.33 83.51
Row % 22.96 77.04
Column % 76.23 85.95
bDMARDs Frequency 29 51 80
% 5.98 10.52 16.49
Row % 36.25 63.75
Column % 23.77 14.05
Total Frequency 122 363 485
% 25.15 74.85 100.00
Based on the information in Table 3.23, the Chi-Square test result for sex difference is highly
significant, at the level of 0.05. This means that the sex distribution amongst recipients of
csDMARDs and bDMARDs is different. Accordingly, gender may confound inferences made
in relation to these two groups. In order to confirm this difference and confirm that the small
size of the population is not responsible, a Fisher's test was performed (Table 3.24)[18].
Table 3.23 Chi-squared for differences in frequency of sex distribution among recipients of
csDMARDs alone and recipients of bDMARDs alone
Statistic DF Value Probability
Chi-Square 1.00 6.26 0.01
Likelihood Ratio Chi-Square 1.00 5.88 0.02
Continuity Adj. Chi-Square 1.00 5.58 0.02
Mantel-Haenszel Chi-Square 1.00 6.25 0.01
Phi Coefficient -0.11
Contingency Coefficient 0.11
Cramer's V -0.11
84
Based on the Fisher’s test, it was confirmed that the two populations have a different sex
distribution (Table 3.24).
Table 3.24 Fisher test for differences in frequency of sex distribution between recipients of
csDMARDs alone and recipients of bDMARDs alone
Based on the statistical tests, the sex distribution is different between csDMARDs and
bDMARDs. In the following pie chart, all sex distributions are presented to make this
comparison easier (Figure 3.3). According to this chart, in both csDMARDs and bDMARDs,
the population of the female sex is greater than the male sex and this difference is greater
amongst those taking csDMARDs (Figure 3.3).
Figure 3.3 Sex distribution among csDMARDs alone and bDMARDs alone
Biologic Male6%
csDMARDs Male19%
bDMARDS Female11%
csDMARDs Female64%
Sex differences in csDMARDs alone and bDMARDs alone
Biologic Male
csDMARDs Male
bDMARDS Female
csDMARDs Female
Fisher's Exact Test
Cell (1,1) Frequency (F) 93.00
Left-sided Pr <= F 0.01
Right-sided Pr >= F 0.99
Table Probability (P) 0.01
Two-sided Pr <= P 0.02
85
3.4.5. T2DM comparison
Table 3.25 summarises the differences in the frequency of T2DM in recipients of csDMARDs
and bDMARDs.
Table 3.25 Comparison of T2DM among patients receiving csDMARDs alone and bDMARDs
alone
Group Response
Status No known Never Current Past Total
CsDMARDs Frequency 1 379 20 5 405
% 0.21 78.14 4.12 1.03 83.51
Row % 0.25 93.58 4.94 1.23
Column % 100 84.79 66.67 71.43
bDMARDs Frequency 0 68 10 2 80
% 0.00 14.02 2.06 0.41 16.49
Row % 0.00 85.00 12.50 2.5
Column % 0.00 15.21 33.33 28.57
Total Frequency 1 447 30 7 485
% 0.21 92.16 6.19 1.44 100.00
There is marginal evidence that the frequency of T2DM in ARAD participants is different
between patients taking csDMARDs alone and those taking bDMARDs alone. The difference
was not significant at the 0.05 level of significance (Table 3.26).
Table 3.26 Chi-squared for differences in frequency of T2DM between csDMARDs alone and
bDMARDs alone, sample size= 485
Statistic DF Value Probability
Chi-Square 3.00 7.65 0.05
Likelihood Ratio Chi-Square 3.00 6.60 0.09
Mantel-Haenszel Chi-Square 1.00 6.26 0.01
Phi Coefficient 0.13
Contingency Coefficient 0.12
Cramer's V 0.13
86
3.4.6. T1DM comparison
Table 3.27 summarises the differences in frequency of T2DM in recipients of csDMARDs
alone and bDMARDs alone.
Table 3.27 Comparison T1DM among patients on csDMARDs alone and bDMARDs alone
Group Response
Status No known Never Current Past
CsDMARDs Frequency 390 14 1 405
% 80.41 2.89 0.21 83.51
Row % 96.30 3.46 0.25
Column % 84.23 70.00 50.00
bDMARDs Frequency 73 6 1 80
% 15.05 1.24 0.21 16.49
Row percentage 91.25 7.50 1.25
Column % 15.77 30.00 50.00
Total Frequency 463 20 2 485
% 95.46 4.12 0.41 100.00
Based on the chi-square test, there is no difference in the frequency of T1DM between patients
who are taking csDMARDs alone and those who are taking bDMARDs alone. However, the
population size is low and there is a possibility that using just Chi-squared reduces the accuracy
of this test. Therefore, other tests (chi- square, likelihood ratio, Mantel- Haenszel, phi
Coefficient, Contingency coefficient, Cramer’s V) have also been assessed (Table 3.28) [18].
Table 3.28 Chi-squared for differences in frequency of T1DM between csDMARDs alone
and bDMARDs alone, Sample size=485
Statistic DF Value Probability
Chi-Square 2.00 4.46 0.11
Likelihood Ratio Chi-Square 2.00 3.61 0.16
Mantel-Haenszel Chi-Square 1.00 4.41 0.04
Phi Coefficient 0.10
Contingency Coefficient 0.10
Cramer's V 0.10
87
In the following, we study each organ infection in csDMARDs versus bDMARDs in more
detail. In different sections, we discuss different levels of infection, including mild, moderate
and severe. Based on the ARAD questionnaire (Appendix M), mild infection is defined as an
infection which does not change activities and the patient did not see a doctor and did not
require prescription medicine for treatment. Moderate infection is defined as an infection
which changes activities occasionally and the patient needed a prescription medication for the
symptoms. Severe infection is an infection which can cause a major change in activities and
the patient needed to see a doctor and received prescription medication, however, the
medication only provided partial relief.
3.4.7. Skin and nail infections comparison
Skin and nail infections are amongst the commonest infections in RA. Skin and nail infection
was reported for three different levels of severity. The relationship between the frequency of
these levels and type of medication are reviewed in Tables 3.29 and 3.30
Table 3.29 Table of frequency of skin and nail infections in recipients of csDMARDs alone
recipients.
Skin and nail infection in csDMARDs alone
Severity Frequency % Cumulative
Frequency
Cumulative
%
Mild 76 53.15 76 53.15
Moderate 56 39.16 132 92.31
Severe 11 7.69 143 100.00
Note. Frequency missing = 1510
Severe skin and nail infection occurred more frequently in patients who were taking bDMARDs
alone compared to csDMARDs alone (Tables 3.29 and 3.30). In contrast, other types of
infections were either similar or more frequently observed in csDMARDs recipients (Tables
3.29-3.30).
88
Table 3.30 Table of frequency of skin and nail infections in bDMARDs alone
Skin and nail infection in Biologics
Severity Frequency % Cumulative
Frequency
Cumulative
%
Mild 16 53.33 16 53.33
Moderate 11 36.67 27 90.00
Severe 3 10.00 30 100.00
Note. Frequency missing = 1623
Almost 44 % of patients who were taking csDMARDs alone have reported mild levels of
skin/nail infection. This is about 53% of all patients who were taking csDMARDs alone and
82% of all patients who reported mild skin infection (Table 3.31). Almost 9% of patients who
were taking bDMARDs alone reported mild infection; this is about 53% of all patients who
were taking bDMARDs alone and almost 17% of all patients who reported mild infection
(Table 3.31). Almost 6 % of patients who were taking CsDMARDs alone have reported severe
level of skin infection. This is about 7.69% of all patients who were taking csDMARDs alone
and 78.5% of all patients who reported severe skin infection (Table 3.31).
Almost 2% of patients who were taking bDMARDs alone reported severe skin infection, this
is about 10% of all patients who were on bDMARDs alone and almost 21% of all patients who
reported severe infection (Table 3.31).
Table 3.31 Differences in frequency of skin infections in csDMARDs and bDMARDs alone
Group
Response Status Mild Moderate Severe Total
csDMARDs
Frequency 76 56 11 143 % 43.93 32.37 6.36 82.66 Row percentage 53.15 39.16 7.69 Column % 82.61 83.58 78.57
bDMARDs
Frequency 16 11 3 30 % 9.25 6.36 1.73 17.34 Row % 53.33 36.67 10.00 Column % 17.39 16.42 21.43
Total Frequency 92 67 14 173 % 53.18 38.73 8.09 100.00
Based on the table 3.32, the point of estimate was used on the Chi-square and calculating p-
values. This shows that the null hypothesis cannot be rejected at the 0.05 level of significance.
Therefore, the frequency of self-reported infection is similar in both groups (Table 3.32).
89
Table 3.32 Table and figures showing differences in frequency of skin/nail infections in
recipients of csDMARDs alone and bDMARDs alone, Sample size=173
3.4.8. Eyes, Ears, nose, Throat (EENT) Infections – a comparison
EENT infections are among the most common infections in RA. EENT infection also was
reported for three different levels of severity.
The data suggest that csDMARDs can increase the frequency of mild and moderate EENT
infection, while bDMARDs appeared to increase the frequency of severe EENT infection
(Table 3.33, Table 3.34).
Table 3.33 Frequency of eye, ear, nose & throat infections in patients taking csDMARDs alone
Ear Nose Throat infection in csDMARDs alone
Eent Infection Frequency % Cumulative
Frequency
Cumulative
%
Mild 83 44.39 83 44.39
Moderate 82 43.85 165 88.24
Severe 22 11.76 187 100.00
Note. Frequency missing = 1466
According to Table 3.33 and Table 3.34, severe EENT infection occurs more often in patients
who are taking bDMARDs alone compared to those who take csDMARDs alone. The
frequency of other types of infections was similar in both groups (Table 3.33-3.34)
Statistic DF Value Probability
Chi-Square 2.00 0.20 0.90
Likelihood Ratio Chi-Square 2.00 0.19 0.91
Mantel-Haenszel Chi-Square 1.00 0.03 0.87
Phi Coefficient 0.03
Contingency Coefficient 0.03
Cramer’s V 0.03
90
Table 3.34 Frequency of Ear Nose Throat infections in in patients taking bDMARDs alone
Ear Nose Throat infection in recipients of bDMARDs
EENT
infection
Frequency % Cumulative
Frequency
Cumulative
%
Mild 10 41.67 10 41.67
Moderate 10 41.67 20 83.33
Severe 4 16.67 24 100.00
Note. Frequency missing = 1629
Almost 10% of patients who were taking csDMARDs alone reported severe EENT infection,
this is about 12% of all patients who were taking csDMARDs alone (Table 3.35).
In contrast, 16 % of patients who were taking bDMARDs reported severe EENT infection
(Table 3.35). The figures in Table 3.35 are just descriptive, and don’t allow the strength of
the association to be confirmed.
Table 3.35 Table of differences in frequency of ear, nose, and throat infections in csDMARDs
alone and bDMARDs alone
Group
Response
Status Mild Moderate Severe Total
csDMARDs
Frequency 83 82 22 187
% 39.34 38.86 10.43 88.63
Row % 44.39 43.85 11.76
Column% 89.25 89.13 84.62
bDMARDs
Frequency 10 10 4 24
% 4.74 4.74 1.90 11.37
Row% 41.67 41.67 16.67
Column % 10.75 10.87 15.38
Total Frequency 93 92 26 211
% 44.08 43.60 12.32 100.00
Calculating Chi- Square and P-Value for differences in EENT infection reveals that frequency
of self-reported infection is similar in both bDMARDs alone and csDMARDs alone. The large
value of the chi-square statistic, 0.4737, and the p-value of 0.7891 indicate that the null
91
hypothesis cannot be rejected at the 0.05 level of significance. Therefore, we conclude that
frequency of eye, ear, nose and throat (EENT) infection is similar in both groups (Table 3.36).
A criticism of this test is that it fixes the row and column margin totals, which in effect makes
an assumption about the distribution of the variables in the population being studied.
Table 3.36 Chi-squared for differences in frequency of EENT infections between csDMARDs
alone and bDMARDs alone, sample size=211
Statistic DF Value Probability
Chi-Square 2.00 0.47 0.79
Likelihood Ratio Chi-Square 2.00 0.44 0.80
Mantel-Haenszel Chi-Square 1.00 0.27 0.61
Phi Coefficient 0.05
Contingency Coefficient 0.05
Cramer's V 0.05
3.4.9. Heart infections comparison
Heart infection also was reported for three different levels of severity. Based on estimation of
the frequency of heart infection, the frequency of heart infection among patients taking
bDMARDs alone and csDMARDs alone weas different, with a significantly lower frequency
of infection among patients on bDMARDs (Table 3.37). Amongst the large number of
reports, 1646 participants did not report any heart infection.
Table 3.37 Table of frequency of Heart infections in csDMARDs alone
Heart infection in csDMARDs alone Heart infection Frequency % Cumulative Frequency Cumulative % Moderate 3 42.86 3 42.86 Severe 4 57.14 7 100.00 Note. Frequency Missing = 1646
Amongst bDMARD-alone recipients, only one case of mild heart infection was reported,
whereas in csDMARDs-alone recipients, a few patients reported moderate or severe
infections (Table 3.38). Patients with a moderate level of infection were 58,66 and 66 years
old, while those with a severe level of infection were 59, 60, 65 and 69 years old.
92
Table 3.38 Table of frequency of Heart infections in bDMARDs alone
Heart infection in Biologics
Heart
infection
Frequency % Cumulative
Frequency
Cumulative
%
Mild 1 100.00 1 100.00
Note. Frequency missing = 1652
As the difference between csDMARDs and bDMARDs in heart infection appeared to be
possibly significant, the differences are demonstrated in a cylindrical graph (Figure 3.4). As it
is shown in the graph, the number of patients who were taking biologics and reported heart
infection was close to zero or negligible (Figure 3.4). The only patient in this group was 67
years old.
Figure 3.4 Comparison of the rate of heart infection in recipients of csDMARDs alone and
bDMARDs alone (with logarithm base).
3.4.10. Lung infections comparison
Lung infection was also reported for three different levels of severity. Lung infection is also
an important infection in rheumatoid arthritis and, based on Table 3.39, almost 61.29 % of the
participants in the csDMARDs group reported moderate lung infection (Table 3.39).
0.01
0.1
1
10
100
Mild Moderate Severe No infection
Number of patients in
Hundreds
Heart Infection
csDMARDs BDMARDs
93
Table 3.39 Table of frequency of Lung infections in csDMARDs alone
Lung infection in csDMARDs alone
Lung infection Frequency % Cumulative
Frequency
Cumulative
%
Mild 25 16.13 25 16.13
Moderate 95 61.29 120 77.42
Severe 35 22.58 155 100.00
Note. Frequency missing = 1498
According to Table 3.39 and Table 3.40, severe lung infection occurs almost twice as frequently
in patients who are taking csDMARDs alone compared to those who were taking bDMARDs
alone.
Table 3.40 Table of frequency of Lung infections in recipients of bDMARDs alone
Lung infection in bDMARDs recipients
Lung infection Frequency % Cumulative
Frequency
Cumulative
%
Mild 11 40.74 11 40.74
Moderate 10 37.04 21 77.78
Severe 6 22.22 27 100.00
Note. Frequency missing = 1626
Almost 19 % of patients who were taking csDMARDs alone reported severe lung infection.
This is about 22 % of all patients who were taking csDMARDs alone and almost 85% of all
patients who reported lung infection (Table 3.41). Almost 3% of patients who were taking
bDMARDs alone reported severe lung infection, this is about 22% of all patients who were on
bDMARDs alone and almost 15% of all patients who reported severe lung infection (Table
3.41). These figures are just descriptive, and any statistical differences need to be confirmed
with Chi-squared tests (Table 3.41).
94
Table 3.41 Table and Figure of differences in frequency of Lung infections in recipients of
csDMARDs alone and bDMARDs alone
Group by response
Group Mild Moderate Severe Total
Lung infection in recipients of
csDMARDs alone
Frequency 25 95 35 15
% 13.74 52.20 19.23 85.16
Row % 16.13 61.29 22.58
Column % 69.44 90.48 85.37
Lung infection in recipients of
bDMARDs alone
Frequency 11 10 6 27
% 6.04 5.49 3.30 14.84
Row % 40.74 37.04 22.22
Column % 30.56 9.52 14.63
Total 36 105 41 182
19.78 57.69 22.53 100
The difference in lung infection between those taking csDMARDs alone and bDMARDs alone
is statistically significant (p-value 0.01), at the level of 0.05 (Table 3.42).
The null hypothesis is that the frequency of self-reported infection is similar in both users of
bDMARDs alone and csDMARDs alone. The large value of the chi-square statistic, 9.3875,
and the small amount of p-value of 0.01 indicate that the null hypothesis can be rejected at the
0.05 level of significance. Therefore, it can be concluded that the frequency of lung infection
is different in both csDMARDs alone and bDMARDs alone. In other words, the frequency of
lung infection is significantly higher among patients who are taking csDMARDs alone (Figure
3.5).
Table 3.42 Chi-squared for differences in frequency of lung infections between csDMARDs
alone and bDMARDs alone, sample size= 182
Statistic DF Value Prob Chi-Square 2.00 9.39 0.01 Likelihood Ratio Chi-Square 2.00 8.32 0.02 Mantel-Haenszel Chi-Square 1.00 3.38 0.07 Phi Coefficient 0.23 Contingency Coefficient 0.22 Cramer's V 0.23
95
The difference in lung infection in between csDMARDs and bDMARDs is demonstrated in
the following chart (Figure 3.5).
Figure 3.5 Comparison of the frequency of lung infection for recipients of csDMARDs alone
and bDMARDs alone (with logarithm base).
3.4.11. Gasterointestinal tract (GIT) infections
GIT infection also was reported for three different levels of severity. Based on the frequency
table below, it can be seen that the frequency of gastrointestinal tract (GIT) infection among
patients who were taking biologic DMARDs was much lower than that for recipients of
csDMARDs alone (Tables 3.43- 3.44).
Table 3.43 Table of frequency of GIT infections in recipients of csDMARDs alone
Gastero Intestinal Tract (GIT) infection in csDMARDs alone
GIT Infection Frequency % Cumulative
Frequency
Cumulative
%
Mild 7 29.17 7 29.17
Moderate 8 33.33 15 62.50
Severe 9 37.50 24 100.00
Note. Frequency missing = 1629
0.01
0.1
1
10
100
Mild Moderate Severe No infection
Number of patients in Hundreds
Lung Infection
CsDMARDs BDMARDs
96
In recipients of bDMARDs, the only GIT infection reported was of moderate severity, while
in recipients of csDMARDs, there were numerous reports of GIT infections in the mild,
moderate and severe categories (Tables 3.43- 3.44).
Table 3.44 Table of frequency of GIT infections in in recipients of bDMARDs alone
As GIT infection was different between the csDMARDs and bDMARDs groups, we compared
this type of infection in these two groups (see Figure 3.6). Based on this figure, csDMARDs is
the major contributing factor for this type of infection and, with a minor difference between
groups, most of the patients reported a severe type of infection.
Figure 3.6 Comparison of the frequency of GIT infection in patients taking csDMARDs alone
and bDMARDs alone
0.01
0.1
1
10
100
Mild Moderate Severe No infection
Number of patients in Hundreds
GIT infection
csDMARDs BDMARDs
Gastro-intestinal Tract (GIT) infection in recipients of bDMARDs alone
Biologics
GIT
infection
Frequency % Cumulative
Frequency
Cumulative
%
Moderate 1 100.00 1 100.00
Note. Frequency missing = 1652
97
3.4.12. Urinary tract infections (UTI) Urinary tract infection was reported at three different levels of severity. Based on Table 3.45,
moderate infection is the most common type of UTI in recipients of csDMARDs, followed by
mild and severe infections (Table 3.45).
Table 3.45 Table of frequency of UTI in recipients of csDMARDs alone
Urinary System infection in recipients of csDMARDs alone
Kidney and Urinary
infection
Frequency % Cumulative
Frequency
Cumulative
%
Mild 12 15.19 12 15.19
Moderate 56 70.89 68 86.08
Severe 11 13.92 79 100.00
Note. Frequency missing = 1574
Almost 6% of patients who were taking bDMARDs alone reported severe urinary system
infection. This is about 25% of all patients who were receiving bDMARDs alone and almost
35% of all patients who reported severe urinary tract infection. These is descriptive information
and will be tested in the following section (Table 3.46).
Table 3.46 Table and figure showing differences in frequency of urinary tract infections in
recipients of csDMARDs alone and bDMARDs alone
Urinary tract infection recipients of bDMARDs
Kidney and Urinary
infection
Frequency % Cumulative
Frequency
Cumulative
%
Mild 12 50.00 12 50.00
Moderate 6 25.00 18 75.00
Severe 6 25.00 24 100.00
Note. Frequency missing = 1629
In order to compare UTIs in recipients of csDMARDs alone and bDMARDs alone, the tables
were combined. (Table 3.47).
98
Table 3.47 Table and Figure of differences in frequency of urinary tract infections in
csDMARDs alone and bDMARDs alone recipients
Group
Response
Status Mild Moderate Severe Total
csDMARDs
Frequency 12 56 11 79
% 11.65 54.37 10.68 76.70
Row % 15.19 70.89 13.92
Column % 50.00 90.32 64.71
bDMARDs
Frequency 12 6 6 24
% 11.65 5.83 5.83 23.30
Row % 50.00 25.00 25.00
Column % 50.00 9.68 35.29
Total Frequency 24 62 17 103
% 23.30 60.19 16.50 100.00
Differences between UTIs in recipients of csDMARDs alone and bDMARDs alone were tested
by Chi-square and p-value. The null hypothesis is that the frequency of Urinary tract infection
(UTI) differs between recipients of bDMARDs alone and csDMARDs alone. Examined for the
three categories, notably mild, moderate, and severe. The large value of the chi-square statistic,
17.3798, and the low p-value of 0.0002 indicate that the null hypothesis should be rejected at
the 0.05 level of significance. Therefore, it was concluded that the frequency of UTI is different
for these two groups, and that the observed difference is highly statistically significant. In other
words, the frequency of moderate and severe UTI is significantly higher among recipients of
csDMARDs alone. The associations observed for moderate and severe UTIs in recipients of
csDMARDs alone were not apparent for mild UTIs.
Table 3.48 Chi-squared for differences in frequency of urinary tract infections between
csDMARDs alone and bDMARDs alone, sample size=103
Statistic DF Value Probability
Chi-Square 2.00 17.38 0.00
Likelihood Ratio Chi-Square 2.00 17.07 0.00
Mantel-Haenszel Chi-Square 1.00 2.61 0.11
Phi Coefficient 0.41
Contingency Coefficient 0.38
Cramer's V 0.41
99
Almost 10% of patients who were taking csDMARDs alone reported severe urinary tract
infections, this is about 14% of all patients who were receiving csDMARDs alone and almost
64% of all patients who reported urinary tract infection (Figure 3.7)
Figure 3.7 Frequency of urinary tract infections in recipients of csDMARDs alone and
bDMARDs alone
UTI is more prevalent among patients on csDMARDs. UTI is also more prevalent among the
female sex. As there is a significant difference between female and male distribution in between
csDMARDs and bDMARDs, the current difference in UTI can be partly and completely due to
this difference in the sex distribution[33].
3.4.13. Musculoskeletal infections (MSK) MSK infection also was reported for three different levels of severity. The frequencies for MSK
infection in recipients of csDMARDs alone and bDMARDs alone were very similar. Moderate
MSK infection was more frequent in recipients of csDMARDs alone. (Tables 3.49-3.50).
0.01
0.1
1
10
100
mild moderate severe No infection
Number of patients in
Hundreds
Urinary System infection
csDMARDs BDMARDs
100
Table 3.49 Table of frequency of musculoskeletal system infection in recipient of csDMARDs
alone
Musculoskeletal system infection in csDMARDs alone
Bone Joint and
Muscle infection
Frequency % Cumulative
Frequency
Cumulative
%
Mild 6 19.35 6 19.35
Moderate 16 51.61 22 70.97
Severe 9 29.03 31 100.00
Note. Frequency missing = 1622
The figures in Tables 3.49 to 3.51 are just descriptive and need to be tested further by
application of a Chi-squared test.
Table 3.50 Table of frequency of musculoskeletal system infection in bDMARDs alone
Musculoskeletal system infection in recipient of bDMARDs
Bone Joint and
Muscle infection
Frequency % Cumulative
Frequency
Cumulative
%
Mild 4 33.33 4 33.33
Moderate 4 33.33 8 66.67
Severe 4 33.33 12 100.00
Note. Frequency missing = 1641
Almost 21 % of all patients who were receiving csDMARDs alone or bDMARDs alone
reported severe MSK infection; this is about 29 % of all patients who were on csDMARDs
alone and almost 69% of all patients who reported severe MSK infection. Almost 9% of
patients who were receiving bDMARDs alone reported severe MSK infection, this is about
33% of all patients who were on bDMARDs alone and almost 30% of all patients who reported
severe MSK infection (Table 3.51)
101
Table 3.51 Table and figure of differences in frequency of muscular skeletal system infections
in recipients of csDMARDs alone and bDMARDs alone
Group Response
Status Mild Moderate Severe Total
csDMARDs
Frequency 6 16 9 31
% 13.95 37.21 20.93 72.09
Row % 19.35 51.61 29.03
Column % 60.00 80.00 69.23
bDMARDs
Frequency 4 4 4 12
% 9.30 9.30 9.30 27.91
Row% 33.33 33.33 33.33
Column % 40.00 20.00 30.77
Total Frequency 10 20 13 43
% 23.26 46.51 30.23 100.00
The null hypothesis is that the frequency of musculoskeletal (MSK) infection differs between
recipients of bDMARDs alone and csDMARDs alone. MSK infection was categorized into
mild, moderate and severe. The low value of the chi-square statistic, 1.4013, and the p-value
of 0.4963 indicate that the null hypothesis should be rejected at the 0.05 level of significance.
Therefore, it can be concluded that frequency of MSK infection is similar in both groups
(Table 3.52).
Table 3.52 Chi-squared for differences in frequency of Musculoskeletal system infections
between recipients of csDMARDs alone and bDMARDs alone, sample size=4
Statistic DF Value Prob Chi-Square 2.00 1.40 0.50 Likelihood Ratio Chi-Square 2.00 1.39 0.50 Mantel-Haenszel Chi-Square 1.00 0.15 0.70 Phi Coefficient 0.18 Contingency Coefficient 0.18 Cramer's V 0.18 WARNING: 33% of the cells have expected counts less than 5. Chi-Square may not be a valid test.
102
3.4.14. Artificial joint infections Artificial joint infection also was reported for three different levels of severity. Almost 80% of
the artificial joint infections in recipients of csDMARDs were classified as severe infection
(Table 3.53).
Table 3.53 Table of frequency of Artificial Joint infection in csDMARDs alone
Artificial joint infection in csDMARDs alone
Artificial Joint
Infection
Frequency % Cumulative
Frequency
Cumulative
%
Moderate 1 20.00 1 20.00
Severe 4 80.00 5 100.00
Note. Frequency missing = 1648
The only artificial joint infection which was reported in recipients of bDMARDs was
classified in the self-report as mild infection (Table 3.54).
Table 3.54 Table of frequency of artificial joint infection in bDMARDs alone
Artificial joint infection in Biologics
Artificial Joint
Infection
Frequency % Cumulative
Frequency
Cumulative
%
Mild 1 100.00 1 100.00
Note. Frequency missing = 1652
Based on the above frequency table (Table 3.54) artificial joint infections in recipients of
bDMARDs alone were numerically less frequent than those in recipients of csDMARDs alone.
Using estimation methods and testing methods is not appropriate here because the only
infection among patients who were taking bDMARDs alone was categorised as mild, whereas
those self-reported by recipients of csDMARDs alone were categorized as moderate or severe
(Table 3.54). Furthermore, the numbers are too small to allow meaningful comparison.
However, it should be indicated that artificial joint infection is almost always an emergency
case and needs hospital admission. Therefore, there should not be tolerance of mild or moderate
infection. In other words, the subjective data here do not concur with reality but, still, our
conclusion remains that csDMARDs cause more artificial joint infection than bDMARDs.
103
3.4.15. Nervous system infections Nervous system infection also was reported for three different levels of severity. Based on
Table 3.55 Nervous system infection is not common in RA and the only episode of this
infection caused mild symptoms and occurred in one patient who was taking csDMARDs alone
(Table 3.55).
Table 3.55 Table of frequency of Nervous System infection in csDMARDs alone
Nervous system infection in csDMARDs alone
Infection Neuro Frequency % Cumulative
Frequency
Cumulative
%
Mild 1 100.00 1 100.00
Note. Frequency missing = 1652
There is no report of nervous system infection in patients who were taking bDMARDs alone
(Table 3.56).
Table 3.56 Table of frequency of Nervous system infection in recipients of bDMARDs alone
Nervous system infection in Biologics
Biologic Infection
Neuro
Frequency % Cumulative
Frequency
Cumulative
%
0 0 0 0
Frequency Missing = 1653
3.4.16. Tuberculosis (TB) infection Tuberculosis (TB) also was reported at three different levels of severity. However, there were
just two reports of moderate to severe level tuberculous infection. Based on the frequency table
tuberculous infection was very uncommon in RA (Table 3.57-3.58).
104
Table 3.57 Table of frequency of tuberculous infection in recipients of csDMARDs alone
TB infection in csDMARDs alone
TB Infection Frequency % Cumulative
Frequency
Cumulative
%
Moderate 2 100.00 2 100.00
Note. Frequency missing = 1651
Only two episodes of tuberculous infection with moderate symptoms were reported and these
infections were restricted to recipients of csDMARDs alone (Tables 3.57-3.58).
Table 3.58 Table of frequency of tuberculous infection in recipients of bDMARDs alone
TB infection in patients taking Biologics
TB Infection Frequency % Cumulative
Frequency
Cumulative
%
0 0 0 0
Note. Frequency missing = 1653
3.3.17. Blood infections Blood infection also was reported at three different levels of severity. The majority of blood
infections in recipients of csDMARDs were of moderate severity (54.55%) (Table 3.59).
Table 3.59 Table of frequency of blood infection in csDMARDs alone
Blood infection in recipients csDMARDs alone
Severity Frequency % Cumulative
Frequency
Cumulative
%
Mild 1 9.09 1 9.09
Moderate 6 54.55 7 63.64
Severe 4 36.36 11 100.00
Note. Frequency missing = 1642
In contrast, the majority of blood infection reports in recipients of bDMARDs were reported to
be mild (50%) (Table 3.60).
105
Table 3.60 Table of frequency of blood infection in recipients of bDMARDs alone
Blood infection in recipients of bDMARDs alone
Severity Frequency % Cumulative
Frequency
Cumulative
%
Mild 2 50.00 2 50.00
Moderate 1 25.00 3 75.00
Severe 1 25.00 4 100.00
Note. Frequency missing = 1649
Almost 27 % of patients who were taking csDMARDs alone reported severe blood infection,
this is about 36 % of all patients who were taking csDMARDs alone and almost 80% of all
patients who reported severe blood infection. However, it should be indicated that infection is
almost always emergency and needs hospital admission. Therefore, there is no mild or moderate
infections. In other words, subjective data here does not coordinate with reality, but still our
conclusion stays similar and states that csDMARDs cause more blood infection than
bDMARDs. Almost 7% of patients who were taking bDMARDs alone reported severe blood
infection. This was about 25% of all patients who were taking bDMARDs alone and almost
20% of all patients who reported severe blood infection (Tables 3.61).
Table 3.61 Table and figure for differences in frequency of blood infections in recipients of
csDMARDs alone and bDMARDs alone
Group Response
Status Mild Moderate Severe Total
csDMARDs
Frequency 1 6 4 11
% 6.67 40.00 26.67 73.33
Row % 9.09 54.55 36.36 -
Column % 33.33 85.71 80.00 -
bDMARDs
Frequency 2 1 1 4
% 13.33 6.67 6.67 26.67
Row % 50.00 25.00 25.00
Column % 66.67 14.29 20.00
Total Frequency 3 7 5 15
% 20.00 46.67 33.33 100.00
106
The figures from tables 3.59 to 3.61 are just descriptive, and they do not reveal anything about
the association. In order to estimate the association, we need to do Chi-squared tests. In order
to undertake the Chi-squared test, the null hypothesis is that the frequency of blood infection is
greater in the recipients of csDMARDs alone. Blood infection reports allowed categorisation
into three groups, notably mild, moderate, and severe. The large value of the chi-square
statistic, 3.1169, and the moderately high p-value of 0.2105 indicate that the null hypothesis
cannot be rejected at the 0.05 level of significance. Therefore, we conclude that the frequency
of blood infection is similar in the two groups. In this test, 83% of cells were less than 5, so
other tests (chi- square, likelihood ratio, Mantel- Haenszel, phi Coefficient, Contingency
coefficient, Cramer’s V) need to be applied to check the veracity of the findings (Table 3.62).
Table 3.62 Chi-squared for differences in frequency of Blood infections between recipients of
csDMARDs alone and bDMARDs alone, sample size= 15
3.4.18. Viral Infections Viral infection was also reported at three different levels of severity. The majority of reported
viral infections in both the csDMARDs and bDMARD recipients were moderate in severity
(Tables 3.63 to 3.64).
Table 3.63 Table of frequency of viral infection in recipients of csDMARDs alone
Viral infection in csDMARDs alone
Viral
Infection
Frequency % Cumulative
Frequency
Cumulative
%
Mild 30 34.88 30 34.88
Moderate 42 48.84 72 83.72
Severe 14 16.28 86 100.00
Note. Frequency missing = 1567
Statistics used DF Value Probability (p-value)
Chi-Square 2.00 3.12 0.21 Likelihood Ratio Chi-Square 2.00 2.83 0.24 Mantel-Haenszel Chi-Square 1.00 1.45 0.23
Phi Coefficient 0.46 Contingency Coefficient 0.41
Cramer's V 0.46
107
Table 3.64 Table of frequency of viral infection in recipients of bDMARDs alone
Viral infection in Biologics
Viral
infection
Frequency % Cumulative
Frequency
Cumulative
%
Mild 5 35.71 5 35.71
Moderate 7 50.00 12 85.71
Severe 2 14.29 14 100.00
Note. Frequency missing = 1639
Almost 14 % of patients who were taking csDMARDs alone reported severe viral infection.
This is about 16% of all patients who were on csDMARDs alone and almost 87% of all patients
who reported severe viral infection (Table 3.65).
Almost 2% of patients who were taking bDMARDs alone reported severe viral infection. This
is about 14% of all patients who were on bDMARDs alone and almost 12.5% of all patients
who reported severe viral infection (Table 3.65).
Table 3.65 Frequency of viral infections in recipients of csDMARDs alone and bDMARDs
alone
Group Response
Status Mild Moderate Severe Total
csDMARDs
Frequency 30 42 14 86
% 30.00 42.00 14.00 86.00
Row % 34.88 48.84 16.28
Column % 85.71 85.71 87.50
bDMARDs
Frequency 5 7 2 14
% 5.00 7.00 2.00 14.00
Row % 35.71 50.00 14.29
Column% 14.29 14.29 12.50
Total Frequency 35 49 16 100
% 35.00 49.00 16.00 100.00
108
The null hypothesis is that the frequency of viral infections is similar in recipients of
bDMARDs alone and csDMARDs alone. Viral infections were categorised into three groups,
notably mild, moderate, and severe. The values of the chi-square statistic, 0.0356, and the very
high p-value of 0.9824, indicate that the null hypothesis should be confirmed at the 0.05 level
of significance. Therefore, we conclude that the frequency of viral infection is similar for
recipients of csDMARDs alone and bDMARDs alone. In this test, 33% of cells contained a
number less than 5. By checking the likelihood ratio and performing other tests (chi- square,
likelihood ratio, Mantel- Haenszel, phi Coefficient, Contingency coefficient, Cramer’s V) the
findings can be confirmed (Table 3.66).
Table 3.66 Chi-squared test for differences in the frequency of viral infections between
patients who were receiving csDMARDs alone and bDMARDs alone, sample size = 100
Statistic DF Value Probability
Chi-Square 2.00 0.04 0.98
Likelihood Ratio Chi-Square 2.00 0.04 0.98
Mantel-Haenszel Chi-Square 1.00 0.02 0.89
Phi Coefficient 0.02
Contingency Coefficient 0.02
Cramer's V 0.02
3.5. Chapter discussion and conclusion
In this section, the demography of the ARAD data has been discussed. Demography analysis
of ARAD data can allow for the comparison of differences between the Australian population
and other populations around the world. This comparison is, potentially, helpful for other
practitioners when they want to generalise the results of studies. This valuable information also
provides a good tool to assess the reliability of inferential analysis findings when ARAD reports
are examined (53).
Different studies reveal the strong connection between smoking and RA. For example,
Criswell et al. (2002), in a cohort study, showed that abstinence from smoking may reduce
the risk of RA among postmenopausal women [24]. A case control study by Padyukov et al.
(2004) showed the risk of RA positive with the SE of HLA-DR is strongly influenced by the
presence of an environmental factor (e.g., smoking) in the population at risk [16].
109
In 2006, Costenbader et al., in a cohort study, showed that past and current smoking were
related to the development of RA, seropositive RA. In this study it was shown that both
smoking intensity and duration were directly related to risk of infection [25]. In a meta-
analysis by Sugiyama et al. (2010), it was shown that smoking is a risk for RA, especially
seropositive RA in men. For women, the risk for smokers is about 1.3 times greater than for
non- smokers [26]. Di Giuseppe et al. (2014) showed that life-long cigarette smoking was
associated with the risk of RA, even among smokers with a low life-long exposure [27].
Furthermore other studies show the connection between effectiveness of smoking cession
and better responsiveness of bio-treatment [28].
Sustained smoking cessation within four years of RA diagnosis is connected to a reduction in
mortality risk, this rate is same as non-smokers. However, smoking more than five years after
RA diagnosis increased mortality beyond the risk of non-RA patients [29]. According to the
ARAD, the rate of smoking between 2001 to 2014 is 10.5% (328/3111). This is almost 8.9%
of all patient visits (2484/27712). Table 3.67 shows the rate of smoking in the general
population in Australia during the same time.
Table 3.67 comparing rate of smokers during the years 2001 to 2013, Australia [30]
Year %Total smokers
2001 22
2004 20
2007 19
2010 18
2013 15
It seems that the rate of smokers in RA is less than rate of smokers among general population
in Australia during those years. Two major possibilities for this discrepancy include (1) ARAD
is a subjective report and data are not reliable; and (2) there are several risk factors for causing
RA and risk factors other than smoking play more significant roles in Australia, especially since
the rate of RA disease in Australia is higher than many other countries [31].
With resoect to alcohol consumption, a few studies have demonstrated that consuming a
moderate amount of alcohol is associated with a reduction in the signs and symptoms of
110
arthritis in RA [17] [34]. Overall, based on ARAD reports, the amount of alcohol consumed
by RA participants is lower compared to that in the general population (1.32 compared to 2.72
standard drinks per day)[35]. According to the British Society of Rheumatology, alcohol
abusers are unsuitable for methotrexate therapy [36]. Rheumatologists should inform RA
patients receiving methotrexate (MTX) to limit alcohol intake and to consider changing MTX
to a safer medication [36]. Based on this advice, most of the patients consuming excessive
alcohol should have been shifted from MTX to bDMARDs; in other words we would expect
to see a meaningful difference in drinking alcohol between patients on csDMARDs compared
to patients on bDMARDs. However, in ARAD this difference between the two groups is not
significant. There is a guideline in Australia to assess alcohol intake in patients before
prescribing MTX, but few data question the contribution of alcohol to the risk of
hepatotoxicity [37] [38].
Based on ARAD, there is a marginal difference in alcohol consumption between bDMARD
and csDMARD users (Mantel-Haenszel Chi-Square P value 0.05). There are several
possibilities for this discrepancy. Australian prescribers may not be permitted to change
medication based on alcohol consumption. According to Australian therapeutic guidelines,
prescribers should assess a patient’s alcohol intake before prescribing methotrexate.
According to this guideline, if methotrexate is prescribed for an alcohol abuser, closer kidney
and liver assessments are required [39]. In addition, Rajakulendran et al. (2008) includes
other medications, such as leflunomide, in this alcohol restriction as well [37]. Other potential
reasons for this difference include (1)n relatively few patients are heavy alcohol users,(2)
subjective data about drinking alcohol is not reliable, and (3) prescribing biologics was not
that common during the study period and (4) most of the alcohol abusers remained in the
csDMARDs group. However, the last possibility was not significant in ARAD.
Based on the findings presented in Tables 3.37-3.38 and in Figure 3.4, it can be seen that the
rate of heart infection is very low among RA patients. The very small numbers reported makes
it difficult to compare the frequency of such infections between recipients of csDMARDs alone
and bDMARDs alone. There is a trend toward higher rates of self-reported moderate or severe
heart infection in csDMARDs users. However, these findings need to be interpreted with
considerable caution, since they are self-reported and participants may not have grasped the
distinction between infection involving the heart and other diverse heart conditions.
111
Lung infections were reported frequently in recipients of both csDMARDs and bDMARDs.
Interestingly, recipients of bDMARDs alone reported lower rates and milder or less severe lung
infections (Table 3.39-3.42). Whether this may be due to a protective effect of bDMARDs is
unclear, but this is considered unlikely, even though there may be better outcomes in bDMARD
recipients for more severe lung infections. An alternative possibility is that certain synthetic
DMARDs, such as methotrexate andlLeflunomide may have conferred greater lung infection
susceptibility. Participants may not have been able to easily distinguish between viral infections
affecting the respiratory tract and lung infections, which may have resulted in unequal
variations in assignment to these two categories of infection.
Urinary tract infections were found to be more common and more severe amongst recipients of
csDMARDs alone (65% compared to 25%) (Table 3.45-3.47). Here again, certain csDMARDs
may have increased the propensity to UTIs to a greater degree than bDMARDs. Prednisolone
use and in particular dosage may also be relevant in this regard. Gastrointestinal tract (GIT)
infection was relatively uncommon and not unequivocally associated with any particular
treatment group. (Table 3.43-3.44).
In summary, the csDMARDs-alone and bDMARDs-alone treatment groups in the ARAD
dataset were found to be well matched and, thus, quite comparable. With respect to self-
reported infections of differing severity, lung infections (LRTIs) and urinary tract infections
(UTIs) were strongly associated with use of csDMARDs, implying either a biologic DMARD
protective effect, which is considered improbable, or a greater propensity to these infections
due to the use of one or more synthetic DMARDs, such as methotrexate or leflunomide for
example, both of which have been implicated in LRTI.
The descriptive analysis of ARAD reports during 2001 to 2014 shows that, when infections of
differing severity are compared between csDMARDs and bDMARD recipients, bDMARDs
alone are associated with less risk of infections among Australian patients with RA than
csDMARDs alone.
Overall, the type of infection, differences in the severity of infections and whether the
frequencies differ significantly statistically between patients who are taking csDMARDs alone
and patients who are taking bDMARDs alone are shown in table 3.68 and figure 4.1.
112
Table 3.68 Comparison of the frequencies of different infections of varying severity between
recipients of csDMARDs alone and bDMARDs alone
Distribution of the severity of infection %
Frequency of the type of infection (%) Type of
infection Severity csDMARDs bDMARDs p-value of
difference LRTI
Mild 16.13 40.74 0.0156
9.8
Moderate 61.29 37.04 Severe 22.58 22.22
GITI
Mild 29.17 0 NA
2.82 Moderate 33.33 100
Severe 37.5 0 UTI
Mild 15.19 50 0.0002
6.33 Moderate 70.89 25
Severe 13.92 25 Viral
Mild 34.88 35.71 0.9819
7.52 Moderate 48.84 50
Severe 16.28 14.29 Skin/nail
Mild 53.15 53.33 0.9072
13 Moderate 39.16 36.67
Severe 7.69 10 EENT
Mild 44.39 41.67 0.8032
14.75 Moderate 43.85 41.67
Severe 11.76 16.67 Heart Mild 0 0
NA
0. 38 Moderate 42.86 100 Severe 57.14 0
MSK, Bone, Joint
Mild 19.35 33.33 0.4982
3.11 Moderate 51.61 33.33
Severe 29.03 33.33 Artificial joint
Mild 0 100 NA
0.61 Moderate 20 0
Severe 80 0 Blood
Mild 9.09 50 0.2426
0.707 Moderate 54.55 25
Severe 36.36 25 LRTI: Lower respiratory tract infection; GITI: Gastrointestinal tract infection; UTI: Urinary Tract Infection
In the above tables, p-values indicate whether there is a significant difference in the frequency
of infections between recipients of csDMARDs alone and bDMARDs alone.
The literature review also shows that both csDMARDs and bDMARDs can increase the risk
of serious infection and non-serious infection. However, according to the literature review,
113
the rate of infection by different medicines is slightly different and, overall, bDMARDs are
causing more infections. FORWARD is one of the largest National Databank for Rheumatic
Diseases across the world. According to this database, TNFIs have almost the highest rate of
serious infection 26.9 (95% CI 24.5‐29.6) compared to non TNFIs 23.3 (95% CI 19.0‐28.5),
and csDMARDs 22.4 (95% CI 19.2‐26.1) [18].
In the same study, the smoking rate was compared between csDMARDs and bDMARDs (p
value 0.738) and ARAD. No significant difference was identified [18].
It has also been demonstrated that, compared to patients with OA, orthopaedic surgery in RA
is associated with a higher risk of infection[40]. This risk also increases in patients who are
taking bDMARDs or cs DAMARDs[41]. Due to this risk, the American College of
Rheumatology advises stopping TNFα inhibitors one week or more prior to surgery[41]. The
British Rheumatology Society also, for the same reason, recommends withholding therapy for
3 to 5 times the half-life of the drug[42], and the Canadian Rheumatology Association
reduces this period to 2 half-lives of the drug[43].
Among all the different anti-RA medications, csDMARDs (methotrexate,
hydroxychloroquine, sulfasalazine, and azathioprine) are safer[40]. Among bDMARDs, anti-
TNFα inhibitor therapy significantly increases the risk of surgical site infection and should be
stopped for more than two weeks prior to orthopaedic surgery[40]. Infliximab and etanercept
from bDMARDs are usually prescribed in longer disease duration and are associated with
further risk of acute surgical site infection (SSI)[44]. Withholding medications before and
after a procedure depends on the pharmacokinetic properties of the individual medication
and the region of the world[45].
Other risk factors associated with an increased risk of infection include steroid doses over 15
mg/day, coronary artery disease, and being underweight [44]. Therefore, it is important to
taper prednisone in the peri-operative management strategy [44]. Sometimes, the risk of
csDMARDs and bDMARDs, compared to other risk factors, is ignorable [44].
Based on the different studies in the literature, the risk of infection is not always the same
among all bDMARDs. For example, TNF inhibitors and methotrexate are both associated
114
with increased incidence of infections, much more than biologics [46]. However, in a cohort
study in the USA conducted with 609 patients with RA before the introduction of biologics,
the infection rate was reaching almost 19.64/100 patient-years and, after bDMARDs, was
reduced to 12.87/100 patient-years in matched controls. In this study, septic arthritis (14.89;
95% CI: 6.12-73) was the most common infection, followed by osteomyelitis (10.63; 95% CI:
3.39-126)[47]. TNFIs also seem to be associated with an almost 2- to 4-fold increased risk of
serious bacterial infections and a slight increase in non-serious infection. Still, a combination
of TNF inhibitors with methotrexate can increase the risk of serious infection, significantly.
[48][49].
Etanercept and infliximab are other samples of biologics. The risk of serious infection in
monotherapy with these medications is the same as for methotrexate [50][51]. These serious
infections include bacterial infection, fungal infection, bronchitis, cellulitis, herpes zoster
infection, pneumonia, peritonitis, pyelonephritis, sepsis, and tuberculosis [50]. In both
etanercept and infliximab, if there is a combination therapy with methotrexate, the risk
reaches higher than the risk with methotrexate alone (P 0.05 for both infliximab doses)[52].
On the other hand, adalimumab from bDMARDs assumes to cause limited incidence of
serious infections. The overall rate of infections in the pooled adalimumab (1.55/patient-year)
is similar to methotrexate monotherapy (1.38/patient year)[53].
With the use adalimumab, the incidence of serious infections is almost 2.03/100 patient-years
[48]and, from the most common to the least common, infections include pneumonia, urinary
tract infections, and septic arthritis. The safety of adalimumab has been approved in other
studies. For example, in a study on 10,000 patients with approximately 12,500 patient-years
of adalimumab exposure only 5.1/100 patient-years developed serious infection[49].
Anakinra is another sample from bDMARDs. Anakinra has been connected to serious
infection in organs, such as lung and skin (5.37/100 patient-years in compare to 1.65/100
patient-years)[54]. However, it seems that most of this connection to infection occurs when a
patient is taking a baseline corticosteroid, otherwise the serious infection rate was
substantially lower (2.87/100 patient-years compare to 7.13/100 patient-years)[54].
115
Abatacept also has a higher rate of serious infection compared to many other bDMARDs (78).
The rate of serious infection in abatacept sometimes is higher than the infection rate in the
methotrexate monotherapy group (2.5% versus 0.9%; 95% CI: 0.3-3.6) [55]. Overall, the
incidence of both serious and nonserious bacterial infections increases in abatacept and it is
better to avoid prescribing abatacept and TNFIs together [55].
Rituximab, in comparison to other bDMARDs, has been associated with less serious
infection. However, there are a few reports regarding a 4- to 7-fold increased risk of
reactivation of latent tuberculosis when using TNFIs together with infliximab, and this rate is
even more than the combination of etanercept and TNFIs[56].
Overall, as a result of the modes of action in medicine, among bDMARDs TNF inhibitors,
anakinra, abatacept, and rituximab can change immune response, leaving patients at an
increased risk of infection. This risk increases by combining some bDMARDs and TNFIs.
For example, when infliximab is added to methotrexate in compare to methotrexate
monotherapy, the risk of serious infection increases, significantly [52].
The most common types of infections in bDMARDs are respiratory tract infections (including
pneumonia), following by skin and soft-tissue infections and urinary tract infections [52].
There is also a risk of tuberculosis with TNFIs. Some evidence reveals that that this risk is the
highest with infliximab and less with anakinra[52]. Rituximab and abatacept seem to have a
lower risk of viral serious infection compared to TNFIs. However, in long term studies, this
was not approved [57][58]. Rituximab monotherapy seems to be associated with serious
infection when it is prescribed for a longer period of time but, overall, the rate is lower than
for many other bDMARDs (94). However, decreases in the levels of IgM during prolonged
treatment with rituximab is associated with a higher incidence of opportunistic infections,
such as non-Hodgkin's lymphoma (NHL) [58][59]. In most studies, corticosteroids (CS) are
assessed among csDMARDs. CS use is associated with an increased relative risk (RR) (1.67,
1.47–1.87) of infection[60]. MTX in a Canadian study was associated with slight increase of
risk of pneumonia (RR 1.2; 95% CI 1.0–1.3) [61], while another study from US indicated a
decreased infection risk in MTX users [49]. In conclusion, the slightly increased risk of
infection in MTX is counterbalanced by the effective control of rheumatic disease, leading to
improved function.
116
In one study, the incidence of severe infection with LEF can reach up to 3.3% person-year
[62][21]. Hydroxychloroquine (HCQ), sulfasalazine (SSZ), and cyclosporine A (CsA) in a US
study were not associated with risk of infection[21]. If there is an association, that is very
mild unless the patient is suffering from other conditions, such as transplanted organs[63].
In summary, according to the literature and ARAD results, both bDMARDs and
csDMARDs will increase the risk of bacterial infection, especially pneumonia. With some
exceptions, it seems that, overall, bDMARDs are associated with higher rates of infection
compared to csDMARDs. However, the ARAD analysis depends on the severity of
infection, this ratio can change or the differences not be regarded significant (Table 3.68).
The reason for this discrepancy might be due to geographical differences; for example, in
TB infection, TB risks in TB-endemic areas with TNFIs is much higher than other
regions[64].
117
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125
CHAPTER 4
Inferential Analysis of Infection in Rheumatoid
Arthritis
126
Abstract
Objective: To conduct an inferential analysis of the association between the risk of infection
and each Anti-RA medication. The current analysis provides valuable information concerning
the relative frequency of self-reported infections in users of diverse anti-rheumatic therapies.
Various organs including eye, ear, nose, throat, lungs, urinary tract, heart, gastrointestinal tract,
CNS were examined as well as systemic infections of a viral and pyogenic nature (sepsis /
septicaemia) are investigated.
Methods Self-reported and unverified data concerning infections was collected from 3110
Australian Rheumatology Association Database (ARAD) participants, who reported
sequentially from 2001 to 2014. Through the processes of data cleaning all duplicated
answers, single answers and faulty reports were deleted. Overall 27,709 visit reports were
available. Data was tested by multinominal logistic regression in SAS software. Mild,
moderate and severe infections assigned according to a priori descriptive criteria were
categorised in relation to organ involvement / body system affected and examined in relation
to current therapy.
Results: The most frequent infections reported by ARAD participants were EENT system
infections (eye, ear, nose and throat,14.75%) followed by skin and nail infections (13%), lung
infections (9.83%), and viral infections of any type (7.52%). Based on the same database, the
most commonly used bDMARDs were Etanercept, followed by Adalimumab. Amongst
csDMARDs the most commonly used medications were: Methotrexate, Hydroxychloroquine
and Sulphasalazine. Among all these medications safest medication in most common
infections were as following. Etanercept and Methotrexate the safest for EENT infection,
Etanercept and Adalimumab the safest in lung infection, and Leflunomide safest option in
skin and nail infections (Table 4.53).
Conclusion: Both csDMARDs and bDMARDs are shown to be associated with higher risk
of infection in RA. It seems that prednisolone (with consumption prevalence of 3.33%)
followed by cyclosporine (with a consumption prevalence of 0.05%) are the most common
medications in most of the moderate to severe infections throughout body. In comparison to
127
csDMARDs, if we consider prevalence of consumption, bDMARDs are rarely causing
moderate or severe infections. Some medications are playing a paradoxical role. For example,
although taking Adalimumab usually increases the risk of infection in skin and nail infection,
in comparison to other medications, it was found to be associated to a reduction in prevalence
of artificial joint or GIT infections. Overall, bDMARDs seems to be safer with lower risk of
infection as compare to csDMARDs.
128
1. Introduction
Rheumatoid arthritis (RA) affects approximately 0.8% of adults and is a cause of significant
morbidity and mortality [1]. RA also affects approximately three times more females than males
[3]. Disease onset is commonly between 40 and 70 years of age, though it can begin at any age.
Understanding the pathogenesis of RA has progressed over the past few decades resulted in the
development of more effective anti-RA medications [4].
Conventionally, in RA, non-steroidal anti-inflammatory drugs, glucocorticoids, and disease-
modifying antirheumatic drugs (DMARDs) are used to treat the disease [5]. The most widely
used DMARD is methotrexate (MTX), which is the basis of most treatment programs for
rheumatoid arthritis [6]. MTX has the highest retention rates compared to other available
medications [7].
Despite progress in developing more efficacious treatment for RA, the risk of infections in
patients receiving biologic or conventional treatments has not been substantially reduced [7].
One theory for this disturbing statistic is that immunosuppressive or disease-modifying
treatments are often required in those most vulnerable to infections, such as elderly patients and
patients with multiple comorbidities. This naturally increases the risk of infection after
treatment with anti-RA medications. Rheumatologists should be aware of the specific patterns
of infection risk that treatment with anti-RA medications confer [8]. This is especially important
with newer treatment modalities. By understanding the risk of infection in different organs and
the severity of those infections, potential risk factors and their connection to other treatments,
health practitioners can adjust medications and institute preventative measures accordingly. For
example, measures such as appropriate screening for and treatment of chronic hepatitis B
virus infection, to ensure optimal vaccination against respiratory pathogens (influenza virus and
pneumococcus) and, where appropriate, offer chemoprophylaxis in patients susceptible to
Pneumocystis jirovecii pneumonia [9]. Patients who have had a splenectomy or in whom there
is chronic sino-pulmonary infection, including bronchiectasis can be identified for increased
vigilance, vaccination and fast-tracking when infections flare or develop [9].
129
Conventionally, in RA, analgesics, non-steroidal anti-inflammatory drugs, glucocorticoids
(GC), and disease-modifying anti-rheumatic drugs (DMARDs) are used to treat the disease.
The former two only suppress symptoms, whereas GC and DMARDs suppress symptoms and
importantly also modify the course of the disease. The most widely used DMARD is
methotrexate (MTX). MTX usually forms the basis of most treatment programs for rheumatoid
arthritis.58–60. It is noteworthy that MTX has the highest retention rates compared to other
available medications [3].
Previous and ongoing research into therapeutic possibilities for RA has led to the development
of potent, biologic medications. Using effective medication should be associated with goal-
oriented treatment plans with regular appraisals of disease activity[10]. The treatment goals for
RA have shifted from mainly symptomatic relief to minimising or eliminating disease activity
and in turn altering the progression of the disease. This can potentially improve long-term
outcomes and reduce morbidity rates [10]. Better treatment strategies have significantly
moderated the severity of RA in the overall population. This has resulted in lower rates of joint
replacement and reduced hospital admissions for RA.
Lower frequencies for vasculitis have also been reported [11]. Better use of csDMARDs
treatment has probably contributed to this improvement because the beginning of the decline in
these measures of disease was noted prior to the use of biologic DMARDs however bDMARDs
may well have reinforced these effects. The use of bDMARDs has further reduced symptoms
and has improved functional and work capacity[12]. The pro-inflammatory cytokines, such as
IL-1, IL-6, and tumour necrosis factor (TNF), have been shown to play an integral role in RA
pathogenesis. Therefore, the development of biologic agents, which target these mediators
could be expected to impact disease activity significantly. IL-1 antagonists proved to be
disappointing, but TNF and IL-6R blockers and IL-6 monoclonal antibodies have shown much
superior efficacy[13].
The approach to the RA treatment has changed during the time and is different among different
nations. During 2001 to 2004, National RA treatment in Australia was based on taking simple
analgesics (e.g., paracetamol), Omega-3 supplements, patient education, physical therapy and
exercise, applying Ice and/or heat, and enhanced primary care referrals (e.g., occupational
therapy and physiotherapy[14].
130
For medicine, usually NSAIDs or COX-2 inhibitors were prescribed in the early stages. If
symptoms continued, the patient was referred to a rheumatologist, where csDMARDs plus a
low dose corticosteroid was prescribed. If disease signs and symptoms were still not
controlled, a rheumatologist could start advanced therapy with DMARDs, leflunomide,
cyclosporine or even the biologic agents, anakinra, anti-TNFs and rituximab[14].
At the same time, almost another 22 different RA management guidelines (American,
APLAR, Australian, Brazilian, British Columbia, British Society for Rheumatology:
established and early, Canadian, EULAR, French, German, Hong Kong, Indian, Latin
American, Mexican, England, Scotland, South African, Spanish, Swedish, Treat to target,
Turkish) show that several general principles were followed. In all these guidelines, remission
or low disease activity is the preferred target. csDMARDs usually started as soon as possible
after the diagnosis and disease activity monitoring, regularly. There is an emphasis in all of
these guidelines that methotrexate is the best initial treatment, and that this can be usefully
enhanced with temporary glucocorticoid treatment. Biologic DMARDs were usually used in
persistently active disease in patients who have already received methotrexate/other
csDMARDs. As soon as the patient achieved a sustained remission, biologics can be
tapered[15].
There are a few minor differences about the value and place for using combinations of
csDMARDs. For example, EULAR guidance is uncertain about using csDMARDs, however,
according to ACR guidance, using csDMARDs is essential. NICE guidance recommends only
starting biologics in patients with disease that has not responded to intensive therapy, using a
combination of conventional DMARDs[16].
Another difference in these guidelines is in the treatment of moderately active RA. While,
different guidelines have ignored to separate moderately active RA from others, the ACR
guidance strongly recommends considering treating moderate RA disease intensively[15].
According to the Australian guidelines for RA treatment in 2020, the guidelines were changed
briefly. In the Australian guideline, the initial treatment starts with simple analgaesics (e.g.,
paracetamol), and omega-3 supplements. In the meantime, patient education (e.g., Arthritis
131
Australia), physical exercise, applying ice and/or heat, and enhanced primary care referrals
(e.g., physiotherapy, occupational therapy, podiatry, psychology and others) are important[19].
Using NSAIDs or COX-2 inhibitors should start after assessment of potential side effects in
first line treatment. If advanced therapy is required, a rheumatologist can prescribe a
combination of csDMARDs (eg. methotrexate, hydroxychloroquine, sulfasalazine) with
biological agents[19].
In this chapter, the impact of risk factors and, in particular, of anti-RA medications on the
frequency of infections in different organs/systems is examined in detail. In addition, based on
patient reports, the severity of infections, the frequency of different types of infections and the
association with different anti-RA medications have been investigated.
1.1. Aims
The aims of this chapter are to determine the:
• frequency of self-reported infections in different organs in RA;
• frequency of prescribed anti-RA medication uses among patients in ARAD;
• impact of different anti- RA medications on self-reported infections; and
• impact of different anti-RA medications on infection severity.
1.2. Hypothesis
The aims are based on the following hypothesis:
Infections are very common in RA and there may be differential effects of anti-rheumatic drugs
on the type and severity of infections that occur in RA.
The following topics will be discussed: 1- Frequency of different types of infection in RA and
categorization of these types of infections, 2- Frequency of different prescribed anti RA
medications, 3- Impacts of anti-RA medications on different types of infection, and 4- impacts
of anti-RA medications on severity of infections.
132
2. Methods
2.1 Data Collection
Data were collected from the Australian Rheumatology Association Database (ARAD), in
which a cohort of 3569 RA patients (960 males and 2609 females), who had regularly
completed questionnaires (28,176 person-reports in total during 2001-2014) and had self-
reported in respect to infections, were investigated for the type and severity of infection and
how these related to currently used anti-RA medications. Among the 3569 patients, 459
patients were eliminated because they had only completed a questionnaire once. After removing
8 duplications 27,709 reports from 3110 patients remained. All underwent a series of inferential
analysis with descriptive tests and logistic regression using SAS software. Only patients who
were currently taking anti-RA medications were selected and the number and severity of
infections in different organs were analysed.
2.2. Statistical Analysis
All 27,709 visits from 3110 patients were subjected to a series of inferential analyses with
descriptive tests and logistic regression in SAS software to extract the required data. Currently
used anti-RA medications were selected and the impacts on different levels of severity of
infection were analysed.
3. Results and Discussions
Based on Table 4.1 and Figure 4.1, the most prevalent infection in RA was EENT infection,
followed by skin and nail infection and lung infection. These data are based on patient reports
from 2001 to 2014. The guidelines in each region and the changes in prescription habits during
time influence these frequencies significantly.
133
Figure 4.1 Frequency of self-reported organ infections in RA based on patient visits during
2001-2014
In order to have a better estimation of the meaning of association between a particular type of
infection and medications, it is necessary to know the frequency with which medications have
been used by ARAD participants.
Based on ARAD, the most common to the least common prescribed medications during 2001
to 2014 among RA patients were: Etanercept, Adalimumab, Methotrexate and Folic acid,
Hydroxychloroquine, Sulphasalazine, Rituximab, Abatacept, Prednisolone, Tocilizumab,
Infliximab, Leflunomide, Anakinra, Azathioprine, Cyclosporine, IM Gold and Penicillamine
(Figure 4.2). In this sample, investigation is based on the patient visit reports. The category of
currently taking medication does not include visits of patient who were previously taking or
had stopped taking that particular type of medication. Also, the data inputs entirely depend on
the patient reports and may include a smaller sample than other studies in these categories.
0.12% 0.16% 0.38% 0.61% 0.71%
2.82% 3.11%
6.33%
7.52%
9.83%
13%
14.75%
0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
12.00%
14.00%
16.00%
Frequency of self‐reported infections
134
Table 4.1 Frequency of taking each medication based on patient visit data
Type of
Medication
Name of
Medication
Population Currently
taking%
Population Never Taken%
csDMARDs
Azathioprine 0.068% (19/27711)
4.64% (1286/27711)
Cyclosporine 0.05% (16/27711)
4.41% (1224/27711)
Leflunomide (Arava (Leflunomide))
1.18% (327/27711)
1.01% (281/27711)
Methotrexate/Folic Acid
19.19% (5318/27711)
58.41% (16188/27711)
Hydroxychloroquine 17.06% (4730/27711)
40.19% (11139/27711)
Sulphasalazine 10.63% (2947/27711)
40.96% (11352/27711)
bDMARDs
Abatacept 3.66% (1016/27711)
92.77% (25708/27711)
Adalimumab 22.18% (6149/27711)
57.71% (15993/27711)
Anakinra 0.14% (39/27711)
96.56% (26758/27711)
Certolizumab 0.88% (246/27711)
97.35% (26979/27711)
Etanercept 30.42% 47.61% Golimumab 1.72%
(479/27711) 96.08% (26625/27711)
Infliximab 2.67% (742/27711)
89.86% (24903/27711)
Rituximab 4.26% (1183/27711)
9.34% (2589/27711)
Tocilizumab 2.72% (756/27711)
94.83% (26281/27711)
Independent
Group
Prednisolone 3.33% (924/27711)
0.45% (127/27711)
IM Gold Injection 0.05% (14/27711)
3.52% (978/27711)
Penicillamine 0.0036% (1/27711)
0.47% (1303/27711)
135
Figure 4.2 Frequency of medication use in RA patients
In order to calculate the effect of medication on different organ infections, logistic regression
was used first and, if there were differences, pairwise chi-squares were calculated. This method
is better than pairwise chi-square at the outset because, with pairwise chi-square, the risk of
mistakes in each test with a p value of 0.05 is up to 5%. When this test is performed more often,
the potential error risk adds up and, for example, if 10 pairwise Chi-square tests are done, the
risk of one wrong answer approaches almost 40%.
By doing logistic regression, the characteristics of the overall test can be evaluated first,
followed by pairwise tests for each factor. Logistic regression takes into account the duration
of medication uses as well, because all occasions upon which the patient has reported are taken
into account[20].
3.1. Different organ infections
In the following part of this chapter we discuss infection in different organs separately.
Multinomial logistic regression was used to compare different severities of each organ
infection with the control group (those who did not have this type of infection). The reason
for using this model was because the outcome was a non-binary categorical variable. Using
pair-wise Chi-square test without applying the model could potentially increase errors
0.003 0.05 0.05 0.06 0.14 1.1 2.6 2.7 3.3 3.6 4.26
10
1719
22
30
Frequency of medications in RA
136
because the number of pairwise tests would be far too high. After checking this test, we assess
convergence status table for the model. It shows if the test meets the criteria for accuracy and
the variables fit the statistical model (Appendices tables A1-A5, B1-B5, C1-C5, D1-D5, E1-
E5, F1-F5, G1-G5, H1-H5, I1-I5, J1-J5, K1-K5).
3.2. Eye, Ears, Nose and Throat (EENT) infection - analysis of Anti-RA medicines
Amongst 21,506 observations 1050/21506 (4.88%) reported at least mild EENT infection,
1829/21506 (8.5%) reported moderate infection, and 406/21506 (1.88%) reported severe
infection, whereas 18221/21506 (84.72 %) reported no EENT infection at all. Overall the
results show a significant difference [(chi square (χ2) of 431.3 with a p-value < 0.0001)]
between the variable effects on the EENT infection (Appendix Tables A.7) [20].
The convergence status table for the model (Appendix Tables A.5-A.7) shows that the test
meets the criteria for accuracy and the variables fit the statistical model. Overall the test shows
a significant difference (likelihood ratio Chi-square of 431.2272 with a p-value of less than
0.0001) for variable effects in relation to EENT infection. This means that one or more of the
medications under study are really associated with EENT infection [20] (Appendix table A.7).
The Wald Chi-square for the overall test is also highly significant (0.0001), with a Chi-square
of almost 415 among 144 degrees of freedom. In other words, the impact on the EENT infection
is not the same in different groups. This Chi-square p-value is almost equivalent to the p-value
in the overall Pearson test. Indeed, the logistic regression result is much the same as the
frequency table (Table 3.35) result because it is a large sample. As the model used is a logistic
regression and not a linear regression model, Chi-square is used to test comparisons.
The Score test (Lagrange multiplier test) requires estimating only a single model. The test
statistic is calculated based on the slope of the likelihood function at the observed values of the
variables in the model. Usually the score tests are compared when we add parameters and it
gives us an estimation of how far the accuracy of the test improves by adding new parameters
or deleting existing parameters [21] (Appendix table A.7). During the backward stepwise model
in the next part of the model, the effects of the medications are dropped one by one to see how
much change occurs in the chi-square and to obtain an estimation of the amount of impact of
that medication on increasing EENT infection [22] (Appendix table A8-A.31).
137
3.2.1. Wald Chi-square, Likelihood ratio test and Score test to test significance of
differences
As the size of the study population in this study is high enough, any of these three tests can be
used, but if the size of the sample is small, then all three tests need to be used to increase the
reliability of the conclusions. Among these tests, the likelihood ratio test is the most reliable
test, because it stays unchanged even if the data is reparametrized [22] (Appendix table A.7).
3.2.2. Effects of medications on Eye Ear Nose and Throat (EENT) infection
As the medication effects in this model are all qualitative, the degree of effect (impact) on the
particular infection can be easily worked out by comparing these categorical variables a
backward procedure in multinominal logistic regression was used. According to the summary
table for the backward procedure (Table 4.2 and Appendix Table A.32), the least significant
effect is from Azathioprine followed by Certolizumab, Penicillamine, IM Gold Injection,
Rituximab, and Golimumab. However, the effect of all of these medications was minimal.
Accordingly, they were dropped from the model (Table 4.2 and Appendix Table A.32).
Table 4.2 Summary of backward elimination of anti-RA medications and risk of EENT
infection
Summary of Backward Elimination
Step Effect
Removed
DF Number
In
Wald
Chi-Square
Pr > ChiSq Variable
Label
1.00 Azathioprine 9.00 17.00 4.99 0.84 Azathioprine
2.00 Certolizumab 9.00 16.00 5.45 0.79 Certolizumab
3.00 Penicillamine 9.00 15.00 7.20 0.62 Penicillamine
4.00 IM Gold injection 9.00 14.00 9.19 0.42 IM Gold
injection
5.00 Rituximab 9.00 13.00 13.65 0.14 Rituximab
6.00 Golimumab 6.00 12.00 11.22 0.08 Golimumab
According to Table 4.3, the following medications have significant association with either
producing or reducing the risk of EENT infection in RA. These medications include Etanercept,
Adalimumab, Anakinra, Infliximab, Abatacept, Tocilizumab, F Methotrexate (plus Folic acid),
138
Hydroxychloroquine, Sulphasalazine, Leflunomide, Cyclosporine, and Prednisolone (Table 4.3
and Appendix Table A.33).
Table 4.3 Medications implicated in the promotion of EENT infection
Type 3 Analysis of Effects
Effect DF Chi-Square Pr > ChiSq
Abatacept 9.00 18.02 0.04
Adalimumab 9.00 22.41 0.01
Anakinra 9.00 18.27 0.03
Cyclosporine 9.00 47.34 <.0001
Etanercept 9.00 52.14 <0.0001
Folic acid plus
Methotrexate
3.00 9.42 0.02
Hydroxychloroquine 9.00 23.37 0.01
Infliximab 9.00 31.02 0.0003
Leflunomide 9.00 17.53 0.04
Prednisolone 9.00 29.48 0.0005
Sulphasalazine 9.00 26.74 0.0015
Tocilizumab 9.00 18.10 0.03
In Table 4.4, the effect of each medication was examined in turn:
Etanercept (ETA): The current use of Etanercept (Etanercept) seems to marginally increases
the overall risk of mild EENT infection up to 18 times (CI: 0.989 to 1.43, P value 0.06), but
somewhat paradoxically, amongst all biologics used, the use of Etanercept was associated with
a significant reduction in the chance of severe EENT infection (Table 4.4-4.5 and Appendix
Tables A.34- A.35).
Adalimumab (ADA): The current use of Adalimumab is associated with an increase (P Value:
0.0021) in the risk of mild and moderate EENT infection. The amount of this increase is in turn
almost 33 times greater for mild (CI: 1.110 to 1.605, P value 0.0021) and 20 times greater for
moderate (CI: 1.041 to 1.390, P value 0.0122) EENT infections (Table 4.4-4.5 and Appendix
Tables A.34- A.35).
Infliximab (INX): The current use of Infliximab is associated with a higher risk of mild (P
value of 0.0002) and moderate EENT infection (P value of 0.0007). For mild EENT infection,
139
the amount of this increase is up to almost 90 times (CI: 1.352 to 2.682) greater compared to
participants who have never taken Infliximab, whereas for moderate EENT infection it was 60
times greater (CI: 1.220 to 2.109) (Table 4.4-4.5 and Appendix Tables A.34- A.35).
Abatacept (ABT): The current use of Abatacept is associated with an increase in the risk of
mild EENT infection (P value: 0.0335). The amount of this increase is 40 times greater than in
patients who have never taken this medication (CI: 1.027 to 1.908) (Table 4.4-4.5 and Appendix
Tables A.34- A.35). The risk for moderate and severe EENT infections was not significant.
Tocilizumab (TOC): The current use of Tocilizumab is associated with an increase in the risk
of mild (P value: 0.0036) and moderate (P value: 0.0143) EENT infection. The amount of this
increase is almost 64 times greater in mild EENT infection (CI: 1.175 to 2.283) and 39 times
greater (CI: 1.068 to 1.812) for moderate infection, compared to patients who have never taken
this medication. (Table 4.4-4.5 and Appendix Tables A.34- A.35).
Methotrexate (plus Folic acid): The use of Methotrexate (plus Folic acid) increased the risk
of infection more than 16 times, but among all csDMARDs users, this medication is associated
with a reduction in the risk of moderate (P value: 0.0049, CI: 0.752 to 0.950) EENT infection
(Table 4.4-4.5 and Appendix Tables A.34- A.35).
Hydroxychloroquine (HCQ) and Sulphasalazine (SAS): Modest increases in rates of EENT
infection were observed for both agents, but these increases were not statistically significant.
(Table 4.4-4.5 and Appendix Tables A.34- A.35).
Leflunomide (LEF): The current use of Leflunomide is associated with an increase in risk of
mild EENT infection (P value: 0.017). The amount of this increase is 31 times greater than in
patients who have never taken this medication (CI:1.065 to 1.613) (Table 4.4-4.5 and Appendix
Tables A.34- A.35). Changes in the rates of moderate and severe EENT infection for LEF were
not significant.
Cyclosporine A (CYA): The current use of cyclosporine A is associated with an increase in
the risk of moderate (P value: 0.0001) and severe (P value: 0.0202) EENT infection. The
amount of this increase is almost 180 times for moderate infection (CI: 1.833 to 4.403) and 170
times (CI: 1.173 to 6.577) for severe infections compared to patients who have never taken this
medication (Table 4.4-4.5 and Appendix Tables A.34- A.35).
Prednisolone: The current use of prednisolone is associated with a significant increase in the
risk of severe EENT infection (P value: 0.0329). The amount of this increase is almost 48 times
140
greater than that in patients who have never taken this medication (CI: 1.032 to 2.118) (Table
4.4-4.5 and Appendix Tables A.34- A.35).
In summary, multiple DMARDs were found to be associated significantly with mild or
moderate EENT infections, whereas only cyclosporin and prednisolone were found to be
associated with severe EENT infection.
Conclusion: The findings demonstrate differential risk for EENT infections for users of both
csDMARDs and bDMARDs. Cyclosporine A and prednisolone confer high risk for example in
comparison to HCQ, SAS and to a lesser extent MTX/FA and LEF. Amongst bDMARDs users,
TNF inhibitors and Tocilizumab confer high risk compared to Abatacept. Whether the
differences between TNF inhibitors are clinically important is doubtful.
Methotrexate (plus Folic acid) confer lower risk for EENT infection, whereas cyclosporine,
prednisolone and infliximab are associated with the highest rates for EENT infections (Table
4.4-4.5 and Appendix Tables A.34- A.35).
141
Table 4.4 Analysis of maximum likelihood estimate in EENT infection
Analysis of Maximum Likelihood Estimates
Parameter Medication
Status
EENT
Infection
DF Estimate Standard
Error
Wald
Chi-
Square
Pr > ChiSq
Adalimumab
currently taking Mild 1.00 0.29 0.09 9.42 0.0021
currently taking Moderate 1.00 0.19 0.07 6.28 0.0122
currently taking Severe 1.00 -0.08 0.15 0.29 0.5873
Cyclosporin
currently taking Mild 1.00 0.53 0.33 2.46 0.1168
currently taking Moderate 1.00 1.04 0.22 21.80 <.0001
currently taking Severe 1.00 1.02 0.44 5.39edrt 0.0202
Etanercept
Stopped taking Severe 1.00 -0.40 0.15 7.47 0.01
Don’t know mild 1.00 1.30 0.54 5.73 0.02
Don’t know Moderate 1.00 1.92 0.34 31.40 <.0001
currently taking mild 1.00 0.17 0.09 3.38 0.07
currently taking Severe 1.00 -0.34 0.14 5.47 0.02
Infliximab
Stopped taking Moderate 1.00 -0.21 0.11 3.50 0.06
currently taking mild 1.00 0.64 0.17 13.59 0.0002
currently taking Moderate 1.00 0.47 0.14 11.46 0.0007
Folic acid
plus
Methotrexate
currently taking Moderate 1.00 -0.17 0.06 7.92 0.0049
Cyclosporine
Stopped taking Moderate 1.00 0.20 0.07 8.71 0.0032
Stopped taking Severe 1.00 0.47 0.13 12.38 0.0004
currently taking Moderate 1.00 1.04 0.22 21.80 <.0001
currently taking Severe 1.00 1.02 0.44 5.40 0.02
Arava
(Leflunomide)
currently taking Mild 1.00 0.27 0.10 6.50 0.01
currently taking Moderate 1.00 0.15 0.08 3.02 0.08
currently taking Severe 1.00 0.0064 0.17 0.0014 0.97
Prednisolone
Stopped taking mild 1.00 0.33 0.11 9.34 0.0022
Stopped taking Moderate 1.00 0.26 0.08 9.27 0.0023
Stopped taking Severe 1.00 0.50 0.18 7.37 0.01
currently taking Severe 1.00 0.39 0.18 4.55 0.03
142
Table 4.5 Estimation of Odd’s ratios in EENT infection
Odds Ratio Estimates Effect EENT
Infection Point Estimate
95% Wald Confidence Limits
Adalimumab- currently taking vs never taken
Mild 1.335 1.110 1.605 Moderate 1.203 1.041 1.390 Severe 0.923 0.692 1.232
Etanercept - currently taking vs never taken
Mild 1.19 0.99 1.43 Moderate 1.09 0.95 1.26 Severe 0.71 0.54 0.95
Infliximab currently taking vs never taken
Mild 1.90 1.35 2.68 Moderate 1.60 1.22 2.11 Severe 0.69 0.33 1.43
Cyclosporine - -currently taking vs never taken
Mild 1.70 0.88 3.29 Moderate 2.84 1.83 4.40 Severe 2.78 1.17 6.58
Prednisolone currently taking vs never taken
Mild 1.18 0.96 1.46 Moderate 1.14 0.97 1.34 Severe 1.48 1.03 2.12
3.3. Chest or lung infection - analysis of anti-RA medicines
Amongst 21506 observations, 371/21506 (1.72 %) were self-reported mild infections,
1379/21506 (6.41%) were self-reported moderate infections and 624/21506 (2.9%) were self-
reported severe infections. In contrast, for 19132/21506 (88.96 %) patient visits, no infections
were reported. In this model, categories of reported chest or lung infection were compared to
participants who reported no chest or lung infection. A multinomial logistic regression model
was used. The reason for using this model is because the outcome is a non-binary categorical
variable. Using pairwise Chi-square test without using the model can increase potential
mistakes because the number of comparisons is high (14). The model convergence status table
(Appendix Tables B.5-B.7) is used to assess that the test meets the criteria for accuracy and the
variables fit the statistical model (14) (Appendix table B.7).
In the model fit statistics table (Appendix table B.7), the likelihood ratio or lr (difference
between -2 Log L or Deviance in the model which contains just the intercept and the one which
contains both the intercept and covariates) is 383.2851. The P value is highly significant. This
shows that a model with covariates is making the test more rigorous and that covariates are
143
actually impacting the cofactors in the lung infection. Other tests such as SC and AIC are also
used to recheck this conclusion [22] (Appendix table B.6).
The Wald Chi-square test for overall test is also highly significant (0.0001) with a Chi-square
of almost 397 among 162 degrees of freedom. In other words, the impact on lung infection is
not the same in different groups. This Chi-square P value is almost equivalent to the p value in
the overall Pearson test. Indeed, the logistic regression result is much the same as that for the
frequency table (Table 3.41) result because it is a large sample. As the model used is logistic
regression and not a linear regression, the Chi-square test permits comparison. [21] (Appendix
table B.7).
The Score test (Lagrange multiplier test) requires estimating only a single model. The test
statistic is calculated based on the slope of the likelihood function at the observed values of the
variables in the model. Usually the score tests are compared when parameters are added, and it
gives us an estimation of how far the accuracy of the test improves by adding new parameters
or deleting existing ones [21] (Appendix table B.7).
During the backward stepwise model in the next part of the model, the effects of the medications
are dropped one by one to see how much change occurs in the Chi-square and to get an
estimation of the amount of impact of that medication on the frequency of lung infection[22]
(Appendix table B8-B.31).
3.3.1. Wald Chi-square, likelihood ratio test and score test to test significance of
differences
As the size of the study population in this study was large enough, any of these three tests can
be used. Had the size of the sample been small, then it would have been necessary to use all
three tests to increase the reliability of the conclusions. Among these tests, the likelihood ratio
test is the most reliable test, because it remains unchanged even if reparameterization is
necessary [22] (Appendix table B.7).
3.3.2. Effects of different medications on lung infection
The effects of each variable on the lung infection was investigated directly by studying its
coefficient. backward procedure in logistic regression, with three levels of mild, moderate and
severe infections was applied because the outcome was categorical. The backward stepwise
144
procedure was used here because it is more accurate than the forward procedure and considers
the accumulating effect of all variables and starts with a bigger model. The model-fit statistics
show that, as using this large model is still fitted to the data, it can be used accordingly. Also,
there is no collinearity and no two variables are identical. This makes it easier to use the
backward model.
As the variables are all categorical variables, the effects of each variable on lung infection can
be examined by studying its coefficient, directly [21].
For this section, a backward procedure in multinominal logistic regression was preferred.
Logistic regression is required, because the outcome is categorical and as lung infection has
three categories of severity, viz: mild, moderate and severe and a no infection category as well,
a multinominal logistic regression is appropriate. Also, using backward stepwise is preferable
here because it is more accurate than a forward procedure and considers the accumulating effect
of all variables and starts with a bigger model. As the model fit statistics show that using this
large model is still well fitted to the data, it can be used with confidence. Furthermore, there is
no collinearity and no two variables are identical. This makes it easier to use the backward
model [22].
According to the summary table of results derived from use of the backward procedure, the
least significant effect is from Certolizumab followed by Penicillamine, Methotrexate (plus
Folic acid), Azathioprine, Rituximab, Infliximab, Tocilizumab, Golimumab, Arava
(Leflunomide), and Adalimumab. However, the effect of all these medications was found to be
minimal and so they were eliminated from the model (Table 4.6 and Appendix Table B.32).
145
Table 4.6 Summary of backward elimination of anti-RA medications and risk of lung infection
Summary of Backward Elimination
Step
Effect
Removed DF
Number
In
Wald
Chi-Square Pr > ChiSq
1 Certolizumab 9 18 5.2822 0.8090
2 Penicillamine 9 17 8.9767 0.4394
3 Methotrexate and Folic acid 3 16 3.0046 0.3909
4 Azathioprine 9 15 10.4127 0.3181
5 Rituximab 9 14 10.6214 0.3026
6 Infliximab 9 13 12.4421 0.1895
7 Tocilizumab 9 12 14.5987 0.1026
8 Golimumab 6 11 11.7349 0.0682
9 Arava (Leflunomide) 9 10 16.1280 0.0643
10 Adalimumab 9 9 16.1807 0.0632
According to the type 3 analysis of effects, the following medications have significant
association with increasing or reducing the propensity for lung infection in RA. These
medications include Etanercept, Anakinra, Abatacept, Hydroxychloroquine,
Hydroxychloroquine, Sulphasalazine, Cyclosporine, Prednisolone, and IM Gold injections
(Table 4.7 and Appendix Table B.33).
146
Table 4.7 Anti-rheumatic medications and propensity for lung infection
Type 3 Analysis of Effects
Effect DF
Wald
Chi-Square Pr > ChiSq
Etanercept 9 31.4874 0.0002
Anakinra 9 20.0990 0.0173
Abatacept 9 34.9246 <.0001
Methotrexate 9 20.5746 0.0147
Hydroxychloroq
uine
9 24.4648 0.0036
Sulphasalazine 9 20.8255 0.0134
Cyclosporine 9 20.6307 0.0144
Prednisolone 9 67.5034 <.0001
IM Gold 9 19.8810 0.0187
In the analysis of maximum likelihood, the statistical findings in respect to each medication
examined are shown in detail, in this section we just discuss significant effect of medications
which are currently being taken by the patient:
Abatacept: Currently taking ABT was found to be associated with a significant increase in the
propensity for moderate lung infection (P value: <.0001). The size of this increase equates to
almost 70 times more in the case of moderate infection compared to patients who don’t take
Abatacept at all (CI: 1.36 to 2.161) (Table 4.8-4.59 and Appendix Tables B.34- B.35).
Hydroxychloroquine: Currently taking HCQ is strongly associated with an increase in the rate
of severe infection in RA (P value: 0.0001). Taking this medication is associated with 53 times
greater risk for severe infection (CI: 1.23 to 1.91). The effect of taking Hydroxychloroquine in
increasing moderate level of infection is marginal (P value: 0.049) and can reach to 17 times
more risk (Table 4.8-4.59 and Appendix Tables B.34- B.35).
147
Sulphasalazine: Currently taking SAL is marginally associated with an increase in the rate of
severe infection in RA (P value: 0.0689). However, the amount of this increase is ignorable
(Table 4.8-4.59 and Appendix Tables B.34- B.35).
Cyclosporine A: Current use of CYA was found to be associated with an increased propensity
for mild (P value 0.0012) and moderate (P value 0.0064) lung infection. Taking this medication
is associated with 243 times greater risk for mild infection and 105 times greater risk for
moderate infection (Table 4.8-4.59 and Appendix Tables B.34- B.35).
Prednisolone: Currently taking prednisolone was found to be associated with an increased
propensity for all categories of lung infection. Taking prednisolone was associated with a 63
times greater propensity for mild infection and a 33- and 140-times greater propensity for
moderate and severe lung infection,, respectively (Table 4.8-4.59 and Appendix Tables B.34-
B.35).
Methotrexate: Taking methotrexate was also associated with significant increase in the rate
of moderate infection (p‐value < 0. 0.0166). Taking this medication is associated with 4 times
greater risk for moderate infection (CI: 1.313 to 15.218).
IM Gold Injection: No impact of IM Gold injections on lung infection was identified (Table
4.8-4.59 and Appendix Tables B.34- B.35).
In summary, multiple DMARDs were found to be associated with mild or moderate lung
infection, whereas only use of Prednisolone was found to be associated with severe lung
infection.
148
Table 4.8 Analysis of maximum likelihood estimate in lung infection
Analysis of Maximum Likelihood Estimates
Parameter InfLung DF Estimate Standard
Error Wald
Chi-Square Pr > ChiSq Etanercept currently taking Mild 1 -0.1632 0.1350 1.4614 0.2267 Etanercept currently taking Moderate 1 -0.0438 0.0677 0.4200 0.5169 Etanercept currently taking Severe 1 -0.1177 0.1018 1.3382 0.2473 Anakinra currently taking Mild 1 1.1898 1.0395 1.3102 0.2524 Anakinra currently taking Moderate 1 1.0875 0.6364 2.9208 0.0874 Anakinra currently taking Severe 1 -11.1525 348.5 0.0010 0.9745 Abatacept currently taking Mild 1 0.3420 0.2178 2.4657 0.1164 Abatacept currently taking Moderate 1 0.5392 0.1180 20.8696 <.0001 Abatacept currently taking Severe 1 -0.0929 0.2079 0.1996 0.6550
Hydroxychloroquine
currently taking Mild 1 0.2042 0.1458 1.9617 0.1613
Hydroxychloroquine
currently taking Moderate 1 0.1582 0.0804 3.8734 0.0491
Hydroxychloroquine
currently taking Severe 1 0.4302 0.1114 14.9202 0.0001
Sulphasalazine currently taking Mild 1 0.3020 0.1660 3.3084 0.0689 Sulphasalazine currently taking Moderate 1 0.0113 0.1022 0.0122 0.9120 Sulphasalazine currently taking Severe 1 0.00650 0.1447 0.0020 0.9641 Cyclosporine currently taking Mild 1 1.2314 0.3793 10.5374 0.0012 Cyclosporine currently taking Moderate 1 0.7209 0.2642 7.4466 0.0064 Cyclosporine currently taking Severe 1 0.2533 0.4633 0.2990 0.5845 Prednisolone currently taking Mild 1 0.4916 0.1786 7.5773 0.0059 Prednisolone currently taking Moderate 1 0.3192 0.0919 12.0656 0.0005 Prednisolone currently taking Severe 1 0.8943 0.1574 32.2591 <.0001
IM Gold currently taking Mild 1 -1.3342 1.0067 1.7565 0.1851 IM Gold currently taking Moderate 1 0.2764 0.2666 1.0746 0.2999 IM Gold currently taking Severe 1 -0.1061 0.4610 0.0529 0.8181
Conclusion: Differential effects on the propensity to lung infections were observed with
csDMARDs and bDMARDs. Prednisolone, CYA and HCQ all increased this propensity,
whereas SAS and IM Gold did not or there was insufficient data to draw firm conclusions.
Amongst bDMARDs, ABT was significantly associated with an increased frequency of
moderate lung infections, whereas ETA and Anakinra were somewhat surprisingly associated
with possible reduced rates of lung infection. Amongst serious infections in RA, lung infections
are the most common. In patients with chronic lung diseases, such as COPD, bronchiectasis
and in those with a past history of one or more attacks of pneumonia, for example, this new
data could be factored into treatment selection.
149
Table 4.9- Estimation of odds ratios in lung infection
Odds Ratio Estimates
Effect InfLung Point
Estimate 95% Wald
Confidence Limits Etanercept currently taking vs never taking 1 0.849 0.652 1.107 Etanercept currently taking vs never taking 2 0.957 0.838 1.093 Etanercept currently taking vs never taking 3 0.889 0.728 1.085 Anakinra currently taking vs never taking 1 3.286 0.428 25.207 Anakinra currently taking vs never taking 2 2.967 0.852 10.327 Anakinra currently taking vs never taking 3 <0.001 <0.001 >999.99
9 Abatacept currently taking vs never taking 1 1.408 0.919 2.157 Abatacept currently taking vs never taking 2 1.715 1.361 2.161 Abatacept currently taking vs never taking 3 0.911 0.606 1.370
Hydroxychloroquine currently taking vs never taking
1 1.227 0.922 1.632
Hydroxychloroquine currently taking vs never taking
2 1.171 1.001 1.371
Hydroxychloroquine currently taking vs never taking
3 1.538 1.236 1.913
Sulphasalazine currently taking vs never taking 1 1.353 0.977 1.873 Sulphasalazine currently taking vs never taking 2 1.011 0.828 1.236 Sulphasalazine currently taking vs never taking 3 1.007 0.758 1.337 Cyclosporine currently taking vs never taking 1 3.426 1.629 7.206 Cyclosporine currently taking vs never taking 2 2.056 1.225 3.451 Cyclosporine currently taking vs never taking 3 1.288 0.520 3.194 Prednisolone currently taking vs never taking 1 1.635 1.152 2.320 Prednisolone currently taking vs never taking 2 1.376 1.149 1.648 Prednisolone currently taking vs never taking 3 2.446 1.796 3.330
IM Gold currently taking vs never taking 1 0.263 0.037 1.894 IM Gold currently taking vs never taking 2 1.318 0.782 2.223 IM Gold currently taking vs never taking 3 0.899 0.364 2.220
150
3.4. Skin and Nail infection - analysis of Anti-RA medicines
Amongst 21506 patient-visit observations 1253/21506 (5.82 %) self-reported mild infection,
1039/21506 (4.83%) self-reported moderate infection and 361/21506 (1.67%) self-reported
severe infection. In contrast, for 18853/21506 (87.66 %) patient-visits, no infections were
reported. In this model, participants who developed different severities of skin and nail infection
were compared to participants who did not develop this type of infection (Appendix tables C1-
C3). A multinomial logistic regression model was used to evaluate these reports. The reason
for using this model is because the outcome is a non-binary categorical variable. Using pairwise
Chi-square test without using the model can increase potential mistakes because the number of
comparisons is large[20].
The model convergence status table (Appendix Tables C.5-C.7) shows that the test meets the
criteria for accuracy and the variables fit the statistical model. Overall, the test shows that anti-
RA medicines have an effect on the risk of skin and nail infection (lr Chi-square of 386.3201
with a P value of less than 0.0001) [20] (Appendix table C.7).
In the model fit statistics table (Appendix table C.7), the likelihood ratio or lr (difference
between -2 Log L or Deviance in the model which just contains intercept and the one which
contains intercept and covariates) is 386.3201. The P value is highly significant (Appendix table
C.7). This shows that a model with covariates is making the test more robust and that covariates
are actually impacting cofactors in skin and nail infection. Other tests, such as SC and AIC, are
also used to recheck this conclusion [22] (Appendix table C.6).
Wald Chi-square for overall test is also highly significant (0.0015), with a Chi-square of almost
85 among 50 degrees of freedom. In other words, the impact on the skin infection is not the
same in different groups. This Chi-square is almost equivalent to the p-value in the overall
Pearson test. Indeed, the logistic regression result is much the same as the frequency table
(Tables 3.29-3.30) result because it is a large sample. As the model used was a logistic
regression and not a linear regression model, the Chi-square test was used for comparison. [21]
(Appendix table C.7).
151
The Score test (Lagrange multiplier test) requires estimating only a single model. The test
statistic is calculated based on the slope of the likelihood function at the observed values of the
variables in the model. Usually the score tests are compared when parameters are added, which
provides an estimation of how far the accuracy of the test improves by adding new parameters
or deleting existing parameters [21] (Appendix table C.7).
During the backward stepwise procedure in the next part of the model, the effects of the
medications are eliminated, one by one, to see how much change occurs in the Chi-square and
to get an estimation of the amount of impact of that medication in increasing skin and nail
infections [22] (Appendix tables C8-C.31).
3.4.1. Effects of different medications on skin and nail infection
For this section, a backward elimination procedure was preferred, utilising multinominal
logistic regression. Logistic regression is required because the outcome is categorical and
because nail and skin infection have three categories, viz: mild, moderate and severe.
Furthermore, there is a no infection category. Accordingly, multinominal logistic regression is
a more appropriate model (Appendix Table C.4). A backward stepwise procedure is used here
because it is more accurate than a forward procedure and considers the accumulating effect of
all variables and also because it starts with a bigger model. As the model fit statistics (Appendix
Table C.6) show, using this large model is still well fitted to the data, so it can be used
appropriately. Moreover, since there is no collinearity and no two variables are identical, it is
easier to use the backward model [22].
According to the summary of the backward procedure, as shown in Table 4.10,, the least
significant effect is from Certolizumab, followed by Hydroxychloroquine, IM Gold injection,
Abatacept, Tocilizumab, Penicillamine, Golimumab, Anakinra, and Azathioprine. However,
the effect of all these medications was minimal and so they were eliminated from the model
(Table 4.10 and Appendix Table C.32).
152
Table 4.10 Summary of backward elimination of anti-RA medications and risk of skin
and nail infection.
Summary of Backward Elimination
Step Effect
Removed
DF Number
In
Wald
Chi-Square
Pr > ChiSq
1.00 Certolizumab 9.00 17.00 7.14 0.62
2.00 Hydroxychloroquine 9.00 16.00 7.26 0.61
3.00 IM Gold injection 9.00 15.00 9.70 0.38
4.00 Abatacept 9.00 14.00 11.59 0.24
5.00 Tocilizumab 9.00 13.00 10.33 0.32
6.00 Penicillamine 9.00 12.00 12.72 0.18
7.00 Golimumab 6.00 11.00 9.98 0.13
8.00 Anakinra 9.00 10.00 14.79 0.10
9.00 Azathioprine 9.00 9.00 15.79 0.07
10.00 Cyclosporine 9.00 8.00 15.94 0.07
According to the type 3 analysis of effects, as shown in Table 4.11, the following medications
have significant impact on producing or reducing the risk of skin and nail infection in RA.
These medications include: Etanercept, Adalimumab, Infliximab, Rituximab, Methotrexate
(plus Folic acid), Sulphasalazine, Leflunomide, Prednisolone (Table 4.11 and Appendix Table
C.33).
Table 4.11 Medications associated with a propensity to increase skin and nail infections
Type 3 Analysis of Effects
Effect DF Wald Chi-Square Pr > ChiSq
Etanercept 9.00 24.94 0.00
Adalimumab 9.00 21.42 0.01
Infliximab 9.00 29.90 0.00
Rituximab 9.00 24.22 0.00
Methotrexate (plus Folic acid) 3.00 25.61 <.0001
Sulphasalazine 9.00 34.68 <.0001
Leflunomide 9.00 26.58 0.00
Prednisolone 9.00 38.01 <.0001
153
In the analysis of maximum likelihood, the effect of each medication was examined in more
detail:
Etanercept: The use of ETA was associated with a reduced frequency of skin and nail
infections. Moderate infections were observed to occur more than 19-fold less often (CI: 0.679
to 0.971) and severe infections 30 times less often (CI: 0.522 to 0.956). Among all biologics
taken Etanercept was associated with a reduction in moderate and severe skin and nail infection
(Table 4.12-4.13 and Appendix Tables C.34- C.35).
Adalimumab: Currently taking Adalimumab was associated with a slight increase in the
frequency of mild infection (P Value: 0.0076). The amount of this increase is almost 24 times
greater than that for patients who have never taken Adalimumab (CI: 1.061 to 1.468) (Table
4.12-4.13 and Appendix Tables C.34- C.35).
Infliximab: Currently taking Infliximab was associated with an increased frequency for severe
skin and nail infection. (P value of 0.0404). The amount of this increase is up to almost 72 times
more than that for participants who have never taken Infliximab. (CI: 1.024 to 2.911) (Table
4.12-4.13 and Appendix Tables C.34- C.35).
Rituximab: Current use of Rituximab was associated with an increased frequency of skin and
nail infection with increases more than 34 to 40 times in moderate and mild infection, but
among all biologics, the use of Rituximab was associated with a reduced risk in mild (p value
0.0032) and moderate (p value 0.0149) skin and nail infection. (Table 4.12-4.13 and Appendix
Tables C.34- C.35).
Methotrexate (plus Folic acid): Currently taking Methotrexate (plus Folic acid) can reduce
the frequency of both mild and moderate skin and nail infection. The reduction in mild infection
is up to almost 26 times lower than in those who have never taken Methotrexate (plus Folic
acid). In moderate skin and nail infection, it is approximately 20 times lower (Table 4.12-4.13
and Appendix Tables C.34- C.35).
Sulphasalazine: Currently taking SAS reduces the frequency of mild skin and nail infection.
According to points estimate, the frequency of skin and nail infection is 29 times (1/0.710) less
than that for patients who have never taken this agent (CI:0.566 to 0.891) (Table 4.12-4.13 and
Appendix Tables C.34- C.35).
Leflunomide: Currently taking LEF increases the risk of mild and moderate skin and nail
infection. According to points estimate the frequency of skin and nail infection is 35 times
154
greater for mild (CI:1.131 to 1.629) and 25 times greater for moderate infection (CI:1.015 to
1.528) (Table 4.12-4.13 and Appendix Tables C.34- C.35).
Prednisolone: Currently taking Prednisolone increases the frequency of severe infection.
According to points estimate, this infection rate is more than 160 times more than that for
patients who have never taken prednisolone (CI: 1.688 to 4.055) (Table 4.12-4.13 and Appendix
Tables C.34- C.35).
In summary, they use of multiple biologic and conventional synthetic DMARDs was found to
be associated with increased rates of mild and moderate skin and nail infections, whereas
prednisolone use and infliximab were associated with an increased frequency of severe skin
and nail infections.
155
Table 4.12 Analysis of maximum likelihood estimate in skin and nail infection
Analysis of Maximum Likelihood Estimates
Parameter Medication
Status
Skin and
Nail
Infection
DF Estimate Standard
Error
Wald
Chi-
Square
Pr > ChiSq
Etanercept
currently
taking
Mild 1.00 0.001 0.08 0.001 0.99
Moderate 1.00 -0.21 0.09 5.22 0.02
Severe 1.00 -0.35 0.15 5.06 0.02
Adalimumab
currently
taking
Mild 1.00 0.22 0.08 7.13 0.01
Moderate 1.00 0.01 0.09 0.01 0.93
Severe 1.00 -0.08 0.16 0.29 0.59
Infliximab
currently
taking
Mild 1.00 0.22 0.18 1.59 0.21
Moderate 1.00 -0.11 0.21 0.29 0.59
Severe 1.00 0.55 0.27 4.20 0.04
Rituximab
currently
taking
Mild 1.00 -0.50 0.17 8.70 0.001
Moderate 1.00 -0.41 0.17 5.93 0.01
Severe 1.00 -0.45 0.26 3.02 0.08
Methotrexate
(plus Folic
acid)
currently
taking
Mild 1.00 -0.30 0.07 17.08 <.0001
Moderate 1.00 -0.18 0.08 5.37 0.02
Severe 1.00 0.21 0.12 3.27 0.07
Sulphasalazine
currently
taking
Mild 1.00 -0.34 0.12 8.77 0.001
Moderate 1.00 -0.11 0.12 0.86 0.35
Severe 1.00 0.09 0.19 0.21 0.65
Leflunomide
currently
taking
Mild 1.00 0.31 0.09 10.74 0.001
Moderate 1.00 0.22 0.10 4.40 0.04
Severe 1.00 0.18 0.17 1.14 0.29
Prednisolone
currently
taking
Mild 1.00 0.10 0.09 1.31 0.25
Moderate 1.00 0.01 0.10 0.02 0.90
Severe 1.00 0.96 0.22 18.49 <.0001
156
Conclusion: Differential effects on the frequency of skin and nail infections were observed
amongst users of csDMARDs and bDMARDs. Prednisolone substantially increased the
frequency of severe skin and nail infections, whereas less consistent effects were observed with
other non-biologic agents. Amongst bDMARDs, severe skin and nail infections were higher in
recipients of Infliximab, whereas lower skin and nail infection rates were observed with
Etanercept. Taking Leflunomide can also increase the risk of mild and moderate skin and nail
infection.
Table 4.13 Estimation of odds ratios in skin and nail infection
Odds Ratio Estimates Effect Skin and Nail
Infection Point Estimate
95% Wald Confidence Limits
Etanercept - currently taking vs never taken
Mild 1.00 0.85 1.18 Moderate 0.81 0.68 0.97 Severe 0.71 0.52 0.96
Adalimumab - currently taking vs never taken
Mild 1.25 1.06 1.47 Moderate 1.01 0.84 1.21 Severe 0.92 0.68 1.25
Infliximab - currently taking vs never taken
Mild 1.25 0.89 1.76 Moderate 0.89 0.59 1.34 Severe 1.73 1.02 2.91
Rituximab - currently taking vs never taken
Mild 0.61 0.44 0.85 Moderate 0.67 0.48 0.92 Severe 0.64 0.38 1.06
Methotrexate/ Methotrexate (plus Folic acid) - currently taking vs never taken
Mild 0.74 0.64 0.85 Moderate 0.84 0.72 0.97 Severe 1.24 0.98 1.56
Sulphasalazine - currently taking vs never taken
Mild 0.71 0.57 0.89 Moderate 0.90 0.71 1.13 Severe 1.09 0.75 1.60
Leflunomide - currently taking vs never taken
Mild 1.36 1.13 1.63 Moderate 1.25 1.02 1.53 Severe 1.20 0.86 1.67
Prednisolone - currently taking vs never taken
Mild 1.11 0.93 1.33 Moderate 1.01 0.83 1.23 Severe 2.62 1.69 4.06
3.5. Artificial (Prosthetic) Joint infection - analysis of Anti-RA medicines
Amongst 21506 respondent observations, 19/21506 (0.088 %) reported mild prosthetic joint
infection, 39/21506 (0.18%) self-reported moderate prosthetic joint infection, and 78/21506
(0.36%) reported severe prosthetic joint infection. For 21370/21506 (99 %) patient-visits, no
infections were reported.
157
In this model, the different categories of prosthesis infection were compared to the control
group (RA respondents who did not have prosthetic joint infection). A multinomial logistic
regression model was used. The reason for using this model was because the outcome was a
non-binary categorical variable. Using pairwise Chi-square test without using the model could
increase potential error, therefore we use regression model.
The model convergence status table shows that the test meets the criteria for accuracy and the
variables fit the model of statistics. Overall, the test shows that medications have significant
different impacts on causing artificial joint infection in RA (lr Chi-square of 208.9481with a P
value of 0.0018) .
The model convergence status table (Appendix Tables D.5-D.7) shows that the test meets the
criteria for accuracy and the variables fit the statistical model. Overall, the test shows significant
differential effects of anti-RA medications on prosthetic joint infection that (lr Chi-square of
208.9481with a P value of 0.0018) on the Artificial joint infection [20] (Appendix table D.7).
In the model fit statistics (Table 4.14), the likelihood ratio or lr is 208.411 and the P value is
significant. This shows that a model with covariates is making the test more robust and that
covariates are actual impacting cofactors in respect to prosthetic joint infection. Other tests,
such as SC and AIC, are also used to recheck this conclusion [22] (Table 4.14 and Appendix
table D.6).
Table 4.14 Estimation of the impact of confounders
Model Fit Statistics
Criterion Intercept Only Intercept and
Covariates
AIC 1,913.34 2,010.39
SC 1,937.27 3,254.66
-2 Log L 1,907.34 1,698.39
The Wald Chi-square for overall test is also highly significant (0.0001) with a Chi-square of
almost 200.8923 among 153 degrees of freedom. In the other words, the impact on the Artificial
Joint infection is not the same in different groups. This Chi-square P value is almost equivalent
to the p value in the overall Pearson test. Indeed, the logistic regression result is much same as
158
the frequency table (Table 3.53-3.54) result because it is a large sample. As our model is logistic
regression and not a linear regression, we are using Chi-square test for our comparison. [21]
(Appendix D.7).
The Score test (Lagrange multiplier test) requires estimating only a single model. The test
statistic is calculated based on the slope of the likelihood function at the observed values of the
variables in the model. Usually we compare the score tests when we add parameters and it gives
us an estimation of how far the accuracy of test improves by adding new parameters or deleting
existing parameters. If we compare this test in backward regression, the major drop is
happening in Cyclosporine, Azathioprine, Tocilizumab and Prednisolone [21] (Appendix D.7).
During the backward stepwise procedure in the next part of the model, the effects of the
medications are dropped, one by one, to see how much change happen in the Chi-square and to
get an estimation of the association between the medication and changes in the artificial joint
infection [22] (Appendix D.8-D.31).
3.5.1. Wald Chi-square, Likelihood ratio test and Score test to test significance of
differences
As the size of the study population in our study is enough, we can use any of these three tests,
but if the size of the sample is small then we have to check all these three tests to increase
reliability of the conclusions. Among these tests, likelihood ratio test is the most reliable test,
because it stays unchanged even if we reparametrize what we are testing [22] (Appendix D.7).
3.5.2. Effects of different medications on artificial (prosthetic) joint infection
As the medication effects in this model are all qualitative, it is possible to work out the degree
of effect (impact) on prosthetic joint infection by comparing these categorical variables.
For this section, a backward procedure in multinominal logistic regression was preferred (Table
4.15-4.16). Logistic regression was required because the outcome is categorical and as artificial
joint infection has three categories of severity, notably mild, moderate and severe and a no
infection report, multinominal logistic regression is the appropriate model. Also, backward
stepwise is used here because it is more accurate than the forward procedure and considers the
accumulating effect of all variables and starts with a bigger model (Appendix Table D.4). As
the model fit statistics (Appendix Table D.6) show that using this large model is still fitted to
159
the data, it can be used appropriately. Also, there is not any collinearity and none of any two
variables are identical. This makes it easier to use the backward model [22].
Table 4.15 Estimation of fitness of tests in artificial joint infection
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 208.95 153.00 0.001
Score 312.91 153.00 <.0001
Wald 200.89 153.00 0.01
According to the summary of the backward procedure (Table 4.16 and Appendix Table D.32),
the least significant effect is from Certolizumab followed by Golimumab, Anakinra, Abatacept,
Methotrexate (plus Folic acid), Azathioprine, Infliximab, Sulphasalazine, Prednisolone,
Etanercept, Leflunomide, Penicillamine, Hydroxychloroquine, Tocilizumab, and Cyclosporine.
However, the effect of all these medications was found to be minimal and they were dropped
from the model.
160
Table 4.16 Summary of backward elimination of anti-RA medications and risk of artificial joint
infection
Summary of backward elimination
Step Effect
Removed
DF Number
In
Wald
Chi-Square
Pr > ChiSq
1.00 Certolizumab 9.00 17.00 0.001 1.00
2.00 Golimumab 6.00 16.00 0.02 1.00
3.00 Anakinra 9.00 15.00 1.66 1.00
4.00 Abatacept 9.00 14.00 2.96 0.97
5.00 Folic acid plus
Methotrexate
3.00 13.00 0.90 0.82
6.00 Azathioprine 9.00 12.00 5.69 0.77
7.00 Infliximab 9.00 11.00 4.45 0.88
8.00 Sulphasalazine 9.00 10.00 5.63 0.78
9.00 Prednisolone 9.00 9.00 7.02 0.64
10.00 Etanercept 9.00 8.00 8.48 0.49
11.00 Leflunomide 9.00 7.00 7.73 0.56
12.00 Penicillamine 9.00 6.00 9.13 0.43
13.00 Hydroxychloroquine 9.00 5.00 10.32 0.33
14.00 Tocilizumab 9.00 4.00 12.22 0.20
15.00 Cyclosporine 9.00 3.00 16.02 0.07
According to type 3 analysis of effects (Table 4.17), the following medications had significant
association with either increasing or reducing the risk of artificial joint infection in RA. These
medications were: Adalimumab, Rituximab, and IM Gold injection (Table 4.17 and Appendix
Table D.32).
Table 4.17 Medications implicated in the development of artificial (prosthetic) joint infection
Type 3 Analysis of Effects
Effect DF Wald
Chi-Square
Pr > ChiSq
Adalimumab 9.00 17.24 0.05
Rituximab 9.00 17.54 0.04
IM Gold injection 9.00 30.83 0.001
161
In the analysis of maximum likelihood, the effect of each medication was investigated in more
detail:
Adalimumab: Taking Adalimumab is associated with reduction in the risk of moderate
artificial joint infection (P value 0.035). However, if patient does not need to take adalimumab,
he / she will have a lower risk for infection (by up to 70 times) (CI: 0.083 to 0.916). (Table
4.18-4.19 and Appendix Tables D.34- D.35).
Rituximab: Currently taking Rituximab is not associated with either a reduction or increase in
prosthetic joint infection (Table 4.18-4.19 and Appendix Tables D.34- D.35).
IM Gold injection: Currently taking parenteral Gold is not associated with either an increase
or decrease in prosthetic joint infection (Table 4.18-4.19 and Appendix Tables D.34- D.35).
Table 4.18- Analysis of Maximum likelihood estimate in artificial joint infection
Analysis of Maximum Likelihood Estimates Parameter Medication
Status Artificial Joint Infection
DF Estimate Standard Error
Wald Chi-Square
Pr > ChiSq
Adalimumab
currently taking
Mild 1.00 -0.38 0.66 0.34 0.56 Moderate 1.00 -1.29 0.61 4.42 0.04 Severe 1.00 -0.07 0.28 0.06 0.81
Rituximab
currently taking
Mild 1.00 0.54 0.81 0.44 0.51 Moderate 1.00 -0.97 1.03 0.88 0.35 Severe 1.00 -0.46 0.74 0.38 0.54
IM Gold injection
currently taking
Mild 1.00 -11.94 1,057.40 0.001 0.99 Moderate 1.00 -12.03 728.10 0.003 0.99 Severe 1.00 -11.57 520.60 0.002 0.98
Conclusion: According to the above information and analysis, Adalimumab has a significant
reduction impact in comparison with other bDMARDs. In people with risk of artificial joint
infection, Adalimumab is the safest (Table 4.18-4.19 and Appendix Tables D.1- D.35).
162
Table 4.19- Estimation of Odds ratios in Artificial Joint infection
Odds Ratio Estimates Effect Artificial
joint infection
Point Estimate
95% Wald Confidence Limits
Adalimumab currently taking Versus never taken
Mild 0.682 0.187 2.488
Adalimumab currently taking Versus never taken
Moderate 0.276 0.083 0.916
Adalimumab currently taking Versus never taken
Severe 0.937 0.545 1.612
Rituximab currently taking Versus never taken Mild 1.710 0.352 8.307 Rituximab currently taking Versus never taken Moderate 0.378 0.050 2.870 Rituximab currently taking Versus never taken Severe 0.633 0.149 2.690 IM Gold injection currently taking Versus never taken
Mild <0.001 <0.001 >999.999
IM Gold injection currently taking Versus never taken
Moderate <0.001 <0.001 >999.999
IM Gold injection currently taking Versus never taken
Severe <0.001 <0.001 >999.999
3.6. Bone, joint and muscle (BJM) infection - analysis of anti-RA medicines
Amongst 21506 observations, 82/21506 (0.38 %) self-reported mild infection, 213/21506
(0.99%) self-reported moderate infection, 243/21506 (1.12%) self-reported severe infection,
and for 20968/21506 (97.49 %) patient-visits, no infections were reported. In this model,
ARAD participants who self-reported bone, joint and muscle infection of differing severity
were compared with ARAD participants in whom there was no such infection. The statistical
model used is the multinomial logistic regression model (Appendix Tables D1-D4). The reason
for using this model is because the outcome is a non-binary categorical variable. Using pairwise
Chi-square test without using the model can increase potential mistakes because the number of
comparisons is large[20]. The model convergence status table (Appendix Tables D.5-D.7)
shows that the test meets the criteria for accuracy and the variables fit the model of statistics.
Overall, the test shows significant differences (lr Chi-square of 283.1804 with a P value of
<.0001) between variable effects on bone, joint, and muscle infections (Appendix table D.7).
In the model fit statistics table (Appendix table D.7), the likelihood ratio or lr (difference
between -2 Log L or Deviance in the model which just contains the intercept and one which
contains both the intercept and covariates) is 283.1804. The P value is highly significant
(Appendix table D.1-D.7). This shows that a model with covariates is strengthening the test and
demonstrates that covariates are the actual impacting cofactors in BJM bone, joint and muscle
163
infection. Other tests such as SC and AIC are also used to recheck this conclusion (Appendix
table D.6). Wald Chi-square for overall test is also highly significant (0.0001) with a Chi-square
of almost 274 among 153 degrees of freedom. In other words, the impact on BJM bone, joint
and muscle infection is not the same in different groups. This Chi-square P value is almost
equivalent to the p value in the overall Pearson test. Indeed, the logistic regression result is
much the same as in the frequency table (Tables 3.49-3.50) result, because it is a large sample.
The model used is a logistic regression model and not a linear regression, Chi-square test was
used for comparison. [21] (Appendix D.7).
The Score test (Lagrange multiplier test) requires estimating only a single model. The test
statistic is calculated based on the slope of the likelihood function at the observed values of the
variables in the model. Usually the score tests are compared when parameters are added, which
provides an estimation of how far the accuracy of the test improves by adding new parameters
or deleting existing parameters [21] (Appendix D.7). During the backward stepwise procedure
in the next part of the model, the effects of the medications are dropped, one by one, to see how
much there is a change in the Chi-square and to get an estimation of the amount of impact of
that medication in increasing bone, joint and muscle (BJM) infection [22] (Appendix D8-
D.31).
3.6.1. Wald Chi-squared, Likelihood ratio test and Score test to test significance of
differences
As the size of the study population in this study is large enough, any of these three tests can be
used, but if the size of the sample were small, then it would be necessary to check all three tests
to increase the reliability of the conclusions. Among these tests, the likelihood ratio test is the
most reliable test, because it stays unchanged even if the data under analysis is reparametrized
[22] (Appendix D.7).
3.6.2. Effects of different medications on bone, joint and muscle infection
As the medication effects in this model are all qualitative, the degree of effect (impact) on an
infection can be easily determined by comparing these categorical variables [21]. For this
section, a backward procedure in multinominal logistic regression was preferred. Logistic
regression was used because the outcome is categorical. As bone, joint and muscle infection
has three categories of severity, notably: mild, moderate and severe together with a no infection
category, multinominal logistic regression is an appropriate model to use (Appendix Table
164
D.4). Also, backward stepwise is used here because it is more accurate than is a forward
procedure. It also considers the accumulating effect of all variables and starts with a bigger
model. As the model fit statistics (Appendix Table D.6) show that using this large model is still
fitted to the data, it can be appropriately used. Also, there is no collinearity and no two variables
are identical. This makes it easier to use the backward model [22].
According to the summary table in the backward procedure, the least significant effect is from
Certolizumab followed by Azathioprine, Anakinra, Golimumab, Tocilizumab, Cyclosporine,
Methotrexate, Rituximab, Abatacept, Sulphasalazine, Etanercept, Adalimumab and IM Gold
injection. However, the effect of all these medications was found to be minimal, so they were
dropped from the model (Table 4.20 and Appendix Table D.32).
165
Table 4.20- Summary of backward elimination of anti-RA medications and risk of Bone,
Joint and Muscle infections
Summary of Backward Elimination
Step
Effect
Removed DF
Number
In
Wald
Chi-Square Pr > ChiSq
Variable
Label
1 Certolizumab 9 18 1.6031 0.9963 Certolizumab
2 Azathioprine 9 17 3.8457 0.9213 Azathioprine
3 Anakinra 9 16 4.3211 0.8890
4 Golimumab 6 15 3.0321 0.8048 Golimumab
5 Tocilizumab 9 14 5.4188 0.7964 Tocilizumab
6 Cyclosporin 9 13 6.0931 0.7306 Cyclosporin
7 Methotrexate 9 12 8.0079 0.5334 Methotrexate
8 Rituximab 9 11 10.8068 0.2892 Rituximab
9 Abatacept 9 10 11.4004 0.2493 Abatacept
10 Sulphasalazine 9 9 14.0763 0.1196 Sulphasalazin
e
11 Etanercept 9 8 15.3543 0.0817
12 Adalimumab 9 7 15.0322 0.0901
13 IM Gold 9 6 16.1101 0.0646 IM Gold
According to type 3 analysis of effects table, the following medications have significant impact
on either increasing or reducing the risk of bone, joint and muscle (BJM) infection in RA. These
medications include: Infliximab, Methotrexate (plus Folic acid), Hydroxychloroquine,
Leflunomide, Prednisolone and Penicillamine (Table 4.21 and Appendix Table D.33).
166
Table 4.21 Effect of medications in causing bone, joint and muscle infection
Type 3 Analysis of Effects
Effect DF
Wald
Chi-Square Pr > ChiSq
Infliximab 9 24.8305 0.0032
Methotrexate (plus Folic acid) 3 8.3854 0.0387
Hydroxychloroquine 9 25.4841 0.0025
Leflunomide 9 35.2574 <.0001
Prednisolone 9 33.2572 0.0001
Penicillamine 9 28.5823 0.0008
In the analysis of maximum likelihood table, the effect of each medication was investigated in
more detail. The results are outline below:
Infliximab: Currently taking Infliximab was found to be marginally associated with an
increased frequency of severe BJM infection (P value: 0.07). The amount of this increase can
reach 82 times more than patient who were not treated with this medicine at all (Table 4.22-
4.23 and Appendix Tables D.34- D.35).
Methotrexate (plus Folic acid): Currently taking MTX / Folic acid was found to reduce
moderate BJM infection, significantly (P value: 0.02). The amount of this reduction is almost
66% of patient who did not need to take MTX, at all. (CI: 0.46 to 0.94) (Table 4.22-4.23 and
Appendix Tables D.34- D.35).
Hydroxychloroquine: There is no evidence that currently taking this medication can affect
BJM infection (Table 4.22-4.23 and Appendix Tables D.34- D.35).
Leflunomide: Currently taking Leflunomide was associated with an increased frequency of
severe BJM infection (P value: 0.0014). The amount of this increase is 87 times greater than in
patients who have never taken this medication at all (CI: 1.276 to 2.759) (Table 4.22-4.23 and
Appendix Tables D.34- D.35).
167
Prednisolone: Currently taking Prednisolone was associated with significant increased
frequency of moderate and severe bone, joint and muscle infection. The amount of this increase
is in turn 87 and 152 times more than in patients who have never taken prednisolone at all
(Table 4.22-4.23 and Appendix Tables D.34- D.35).
Penicillamine: There is no evidence that currently taking Penicillamine can affect BJM
infection (Table 4.22-4.23 and Appendix Tables D.34- D.35).
In summary, the use of infliximab, leflunomide and prednisolone were associated with
statistically significant increases in the frequency of severe BJM infections.
168
Table 4.22-Analysis of maximum likelihood estimate in bone, joint and muscle (BJM)
infection
Analysis of Maximum Likelihood Estimates
Parameter Bone/Joint/Muscle infection DF Estimate
Standard Error
Wald Chi-Square Pr > ChiSq
Infliximab currently taking 1 1 -0.2314 0.7289 0.1007 0.7510 Infliximab currently taking 2 1 -0.0882 0.4578 0.0372 0.8471 Infliximab currently taking 3 1 0.5992 0.3308 3.2819 0.0700
Methotrexate (plus Folic
acid)
currently taking 1 1 -0.4576 0.2957 2.3950 0.1217
Methotrexate (plus Folic
acid)
currently taking 2 1 -0.4093 0.1788 5.2389 0.0221
Methotrexate (plus Folic
acid)
currently taking 3 1 0.1239 0.1458 0.7217 0.3956
Hydroxychloroquine
currently taking 1 1 -0.2784 0.3205 0.7543 0.3851
Hydroxychloroquine
currently taking 2 1 0.2671 0.1893 1.9918 0.1582
Hydroxychloroquine
currently taking 3 1 0.1313 0.1708 0.5916 0.4418
Arava (Leflunomide)
currently taking 1 1 0.1898 0.3336 0.3236 0.5694
Arava (Leflunomide)
currently taking 2 1 0.1851 0.2077 0.7935 0.3730
Arava (Leflunomide)
currently taking 3 1 0.6292 0.1968 10.2160 0.0014
Prednisolone currently taking 1 1 0.0363 0.3124 0.0135 0.9075 Prednisolone currently taking 2 1 0.6298 0.2394 6.9233 0.0085 Prednisolone currently taking 3 1 0.9273 0.2630 12.4305 0.0004 Penicillamine currently taking 1 1 1.6260 1.0242 2.5204 0.1124 Penicillamine currently taking 2 1 -
13.0777 913.3 0.0002 0.9886
Penicillamine currently taking 3 1 -12.9395
847.3 0.0002 0.9878
169
Conclusion:
Differential effects on the frequency of BJM infections were observed amongst users of
csDMARDs and bDMARDs. Amongst csDMARDs, Prednisolone and leflunomide were
associated with a significant increased frequency of severe BJM infections, whereas
methotrexate was associated with reduced frequency of moderate BJM infections. Amongst
bDMARDs, infliximab was associated with an increased frequency of BJM infections in most
categories and severe infection type was significant. The data eres less clear for
hydroxychloroquine and penicillamine, but low numbers may have limited the capacity for
analysis (Table 4.22-4.23 and Appendix Tables D.1- D.35).
Table 4.23- Estimation of Odds ratios in Bone, Joint and Muscle infection
Odds Ratio Estimates
Effect Bone/Joint/Muscle infection
Point Estimate
95% Wald Confidence Limits
Infliximab currently taking Versus never taking 1 0.793 0.190 3.311 Infliximab currently taking Versus never taking 2 0.916 0.373 2.246 Infliximab currently taking Versus never taking 3 1.821 0.952 3.482 Methotrexate (plus Folic acid) currently taking
Versus never taking Mild 0.633 0.354 1.130
Methotrexate (plus Folic acid) currently taking Versus never taking
Moderate 0.664 0.468 0.943
Methotrexate (plus Folic acid) currently taking Versus never taking
Severe 1.132 0.851 1.506
Hydroxychloroquine currently taking Versus never taking
Mild 0.757 0.404 1.419
Hydroxychloroquine currently taking Versus never taking
Moderate 1.306 0.901 1.893
Hydroxychloroquine currently taking Versus never taking
Severe 1.140 0.816 1.594
Arava (Leflunomide) currently taking Versus never taking
Mild 1.209 0.629 2.325
Arava (Leflunomide) currently taking Versus never taking
Moderate 1.203 0.801 1.808
Arava (Leflunomide) currently taking Versus never taking
Severe 1.876 1.276 2.759
Prednisolone currently taking Versus never taking Mild 1.037 0.562 1.913 Prednisolone currently taking Versus never taking Moderate 1.877 1.174 3.001 Prednisolone currently taking Versus never taking Severe 2.528 1.510 4.232 Penicillamine currently taking Versus never taking Mild 5.084 0.683 37.844 Penicillamine currently taking Versus never taking Moderate <0.001 <0.001 >999.999 Penicillamine currently taking Versus never taking Severe <0.001 <0.001 >999.999
170
3.7. Blood infection - analysis of Anti-RA medicines
Amongst 21506 observations, 21/21506 (0.097 %) self-reported mild infection, 70/21506
(0.32%) self-reported moderate infection, 111/21506 (0.51%) self-reported severe infection,
whereas 21304/21506 (99.06 %) reported no infection. In this model, participants with different
categories of blood infection (presumed sepsis or septicaemia) were compared to those who did
not report any infection (Appendix tables E1-E3). A multinomial logistic regression model was
used. The reason for using this model is that the outcome is a non-binary categorical variable.
Using pairwise Chi-square test without using the model can increase potential mistakes because
the number of comparisons is large [20].
Table 4.24 Estimation of fitness of tests in blood infection
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 2,670.26 2,718.38
SC 2,694.19 3,962.65
-2 Log L 2,664.26 2,406.38
Table 4.25 Estimation of fitness of tests in blood infection
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 257.8845 153 <.0001
Score 284.8579 153 <.0001
Wald 233.2076 153 <.0001
The model convergence status table (Appendix Tables E.5-E.7) shows that the test meets the
criteria for accuracy and the variables fit the model of statistics. Overall the test shows a
significant difference (lr Chi-square of 257.8845 with a P value of <.0001) between variable
effects on blood infection [20] (Appendix table E.7).
In the model fit statistics table (Appendix table E.7), the likelihood ratio or lr (difference
between -2 Log L or Deviance in the model which contains just the intercept and the one which
contains both the intercept and covariates) is 257. The P value is highly significant (Appendix
table E.1-E.7). This shows that in a model with covariates, the test strengthened. Furthermore,
171
the covariates were found to be impacting cofactors in blood infection. Other tests such as SC
and AIC were also used to recheck this conclusion [22] (Appendix table E.6).
Wald Chi-Square for overall test is also highly significant (<.0001) with a Chi-square of almost
233 among 153 degrees of freedom. In other words, the impact on the blood infection is not the
same for different groups. This Chi-square P value is almost equivalent to the p value in the
overall Pearson test. Indeed, the logistic regression result is much the same as the frequency
table (Tables 3.59-3.60) result because it is a large sample. As the model is a logistic regression
and not a linear regression, the Chi-square test has been used to examine the comparison. [21]
(Appendix E.7).
The Score test (Lagrange multiplier test) requires estimating only a single model. The test
statistic is calculated based on the slope of the likelihood function at the observed values of the
variables in the model. Usually the score tests are compared when parameters are added. They
provide an estimation of how far the accuracy of the test improves when new parameters are
added to or removed from existing parameters [21] (Appendix E.7). During the backward
stepwise model in the next part of the model, the effects of the medications are dropped one by
one to see how much change occurs in the chi-square and to get an estimate of the amount of
impact of that medication in increasing blood infection [22] (Appendix E.8-E.31).
3.7.1. Wald Chi-square, Likelihood ratio test and Score test to test the significance
of differences
As the size of the study population in this study was large enough, any of these three tests can
be used, but if the size of the sample were small, then it would be necessary to check all three
tests to increase the reliability of the conclusions. Among these tests, the likelihood ratio test is
the most reliable test, because it stays unchanged even if re-parametrization of the data is
undertaken [22] (Appendix E.7).
3.7.2. Effects of different medications on blood infection
As the medication effects in this model are all qualitative, the degree of effect (impact) on blood
infection can be easily determined by comparing these categorical variables [21].
172
For this section, a backward procedure in multinominal logistic regression was preferred.
Logistic regression is required because the outcome is categorical and also because blood
infection is divided into three categories of severity, viz: mild, moderate and severe. Moreover,
there is a no infection category, (self-reported absence of infection). Accordingly, a multi
nominal logistic regression model was deemed most appropriate (Appendix Table E.4).
Furthermore, a backward stepwise approach is used here too, because it is more accurate than
a forward procedure and considers the accumulating effect of all variables and starts with a
bigger model. As the model fit statistics (Appendix Table E.6) show that using this large model
is still well fitted to the data, it can be used appropriately. Additionally, there is no collinearity
and none of any two variables are identical. This makes it easier to use the backward model
(Tables 4.26-4.29).
According to the summary table in the backward procedure (Table 4.26 and Appendix Table
E.32), the least significant effect is from Anakinra followed by Certolizumab, Infliximab,
Rituximab, Leflunomide, Penicillamine, Golimumab, Cyclosporine, Sulphasalazine,
Azathioprine, Abatacept, Tocilizumab, Methotrexate (plus Folic acid), IM Gold injection,
Adalimumab and Etanercept. However, the effect of all of these medications was minimal and
so they were dropped from the model.
173
Table 4.26 Summary of backward elimination of anti-RA medications in respect to Blood
infection (sepsis or septicaemia)
Summary of Backward Elimination
Step Effect Removed DF Number In Wald Chi-Square Pr > ChiSq
1.00 Anakinra 9.00 17.00 0.28 1.00
2.00 Certolizumab 9.00 16.00 1.68 1.00
3.00 Infliximab 9.00 15.00 3.55 0.94
4.00 Rituximab 9.00 14.00 4.87 0.85
5.00 Leflunomide 9.00 13.00 5.34 0.80
6.00 Penicillamine 9.00 12.00 5.65 0.77
7.00 Golimumab 6.00 11.00 3.51 0.74
8.00 Cyclosporine 9.00 10.00 7.10 0.63
9.00 Sulphasalazine 9.00 9.00 7.13 0.62
10.00 Azathioprine 9.00 8.00 9.52 0.39
11.00 Abatacept 9.00 7.00 12.24 0.20
12.00 Tocilizumab 9.00 6.00 12.21 0.20
13.00 Methotrexate (plus Folic
acid)
3.00 5.00 4.80 0.19
14.00 IM Gold injection 9.00 4.00 15.55 0.08
15.00 Adalimumab 9.00 3.00 16.53 0.06
16.00 Etanercept 9.00 2.00 14.05 0.12
The only medications for which there was evidence of an effect on blood infection were:
Hydroxychloroquine and Prednisolone (Table 4.27 and Appendix Table A.32).
Table 4.27 Effect of medications in causing Blood infection
Type 3 Analysis of Effects
Effect DF Wald
Chi-Square
Pr > ChiSq
Hydroxychloroquine 9.00 18.10 0.03
Prednisolone 9.00 49.54 <.0001
174
Hydroxychloroquine: Taking Hydroxychloroquine was associated with a significant reduction
in reporting blood infection. Although taking this medication (CI: 0.22 to 0.84 P value: 0.03)
(Table 4.27-4.28 and Appendix Tables E.34- E.35).
Prednisolone: Use of prednisolone is strongly associated with an increased frequency of severe
blood infections. These severe blood infections were increased by up to almost 431 times
compared to participants who were never users of prednisolone (CI: 2.147 to 13.142) (Table
4.27-4.28 and Appendix Tables E.34- E.35).
In summary, use of Prednisolone carries considerable risk in respect to septicaemia.
Table 4.28 Analysis of maximum likelihood estimate in blood infection
Analysis of Maximum Likelihood Estimates
Parameter Medicati
on Status
Blood
Infectio
n
DF Estimat
e
Standar
d Error
Wald
Chi-
Squar
e
Pr > ChiS
q
Hydroxychloroquine
currently
taking
Mild 1.0
0
-1.94 1.03 3.51 0.06
Moderat
e
1.0
0
-0.29 0.34 0.72 0.40
Severe 1.0
0
-0.86 0.35 6.07 0.01
Prednisolone
Mild 1.0
0
-0.40 0.56 0.50 0.48
currently
taking
Moderat
e
1.0
0
0.23 0.33 0.47 0.49
Severe 1.0
0
1.67 0.46 13.06 0.001
175
Conclusion: Hydroxychloroquine and especially prednisolone substantially increase rates of
severe blood infection and have a significant association with risk of blood infection. While
hydroxychloroquine is associated with a reduction in this risk, taking prednisolone is
significantly associated with a sharp increase in this risk (Table 4.28-4.29 and Appendix Tables
E.1- E.35).
Table 4.29 Estimation of Odds ratios in blood infection
Odds Ratio Estimates
Effect Blood
Infection
Point
Estimate
95% Wald
Confidence Limits
Hydroxychloroquine - currently taking vs
never taken
1.00 0.14 0.02 1.09
2.00 0.75 0.39 1.45
3.00 0.43 0.22 0.84
Prednisolone - currently taking vs never taken
1.00 0.67 0.22 2.02
2.00 1.25 0.66 2.39
3.00 5.31 2.15 13.14
176
3.8. Gastro-intestinal tract infection - analysis of medication confounders
Among 21506 observations, 118/21506 (0.54 %) self-reported mild infection, 241/21506
(1.12%) self-reported moderate infection and 155/21506 (0.72%) self-reported severe infection,
whereas 20992/21506 (97.6 %) reported no infection. In this model, participants with different
categories of gastrointestinal infection were compared with ARAD participants, who did not
self-report this type of infection (Appendix tables F1-F3). The statistical model used was
Multinomial logistic regression. The reason for using this model is that the outcome is a non-
binary categorical variable. Using pairwise Chi-square test without using the model can
increase potential mistakes because the number of comparisons is very large [20].
The model convergence status table (Appendix Tables F.5-F.7) shows that the test meets the
criteria for accuracy and the variables fit the statistical model. Overall the test shows significant
effects (lr Chi-square of 233.3227 with a P value of <.0001) from anti RA medicines on GIT
infection [20] (Appendix table F.7).
In the model fit statistics table (Appendix table F.7), the likelihood ratio or lr (difference
between -2 Log L or Deviance in the model which contains just the intercept and the one which
contains both the intercept and covariates) is 233.3227. The P value is highly significant (Table
4.30 and Appendix table F.1-F.7). This shows that a model with covariates is strengthening the
test and that the covariates are actually impacting cofactors in GIT infections. Other tests such
as SC and AIC are also used to recheck this conclusion [22] (Table 4.30 and Appendix table
F.6).
Table 4.30 Estimation of the impact of confounders
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 5,944.02 6,016.70
SC 5,967.95 7,260.97
-2 Log L 5,938.02 5,704.70
177
Wald Chi-square for overall test is also highly significant (0.0001) with a Chi-square of almost
233.3227 among 153 degrees of freedom. In other words, the impact on the GIT infection is
not the same in different groups. This Chi-square P value is almost equivalent to the p value in
the overall Pearson test. Indeed, the logistic regression result is much the same as the frequency
table (Tables 3.59-3.60) result because it is a large sample. As a logistic regression and not a
linear regression has been used, the Chi-Square test has been used for comparison. [21] (Table
4.31 and Appendix F.7).
Table 4.31 Estimation of fitness of tests in GIT infection
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 233.3227 153 <.0001
Score 267.8657 153 <.0001
Wald 231.4222 153 <.0001
The Score test (Lagrange multiplier test) requires estimating only a single model. The test
statistic is calculated based on the slope of the likelihood function at the observed values of the
variables in the model. Usually, the score tests are compared when new parameters are added.
It gives an estimate of how far the accuracy of the test improves when new parameters are
added, or existing parameters are deleted [21] (Appendix F.7).
During the backward stepwise phase in the next part of the model, the effects of the medications
are dropped one by one to see how much there is a change in the Chi-square and to get an
estimate of the impact of that medication with regard to increasing or decreasing GIT infection
[22] (Appendix F8-F.31).
178
3.8.1. Wald Chi-square, Likelihood ratio test and Score test
As the size of the study population in this study is large enough, any of these three tests can be
used, but if the size of the sample is small then all three tests need to be used to increase the
reliability of the conclusions. Among these tests, the likelihood ratio test is the most reliable
test, because it remains unchanged even if the data is reparametrized [22] (Appendix F.7).
3.8.2. Effects of different medications on GIT infections
As the medication effects in this model are all qualitative, the degree of effect (impact) on any
infection can be easily worked out by comparing these categorical variables [21].
For this section, a backward procedure in multinominal logistic regression is preferred. Logistic
regression is required because the outcome is categorical and as GIT infection has three
categories, notably mild, moderate and severe as well as a no infection category, multinominal
logistic regression is an appropriate model (Appendix Table F.4). Also, backward stepwise is
used here because it is more accurate than a forward procedure and considers the accumulating
effect of all variables and starts with a bigger model. As the model fit statistics (Appendix Table
F.6) show that using this large model is still fitted to the data, it can be used appropriately. Also,
there is no collinearity and none of any two variables are identical. This makes it easier to use
the backward model [22].
According to the summary of the backward procedure (Table 4.32), the least significant effect
is from Certolizumab followed by Azathioprine, IM Gold injection, Golimumab, Tocilizumab,
Etanercept, Leflunomide, Anakinra, Penicillamine, Abatacept, Methotrexate (plus Folic acid),
Rituximab, Hydroxychloroquine, and Sulphasalazine (Table 4.32 and Appendix Table F.32).
179
Table 4.32 Summary of backward elimination of anti RA medications and risk of GIT infection
Summary of backward elimination
Step Effect Removed DF Number In Wald Chi-Square Pr > ChiSq
1.00 Certolizumab 9.00 17.00 1.04 1.00
2.00 Azathioprine 9.00 16.00 3.60 0.94
3.00 IM Gold injections 9.00 15.00 4.16 0.90
4.00 Golimumab 6.00 14.00 2.60 0.86
5.00 Tocilizumab 9.00 13.00 4.03 0.91
6.00 Etanercept 9.00 12.00 4.74 0.86
7.00 Leflunomide 9.00 11.00 7.01 0.64
8.00 Anakinra 9.00 10.00 8.19 0.52
9.00 Penicillamine 9.00 9.00 9.03 0.44
10.00 Abatacept 9.00 8.00 9.71 0.37
11.00 Folic acid plus Methotrexate 3.00 7.00 3.37 0.34
12.00 Rituximab 9.00 6.00 11.20 0.26
13.00 Hydroxychloroquine 9.00 5.00 12.07 0.21
14.00 Sulphasalazine 9.00 4.00 13.32 0.15
However, the effect of all these medications was minimal, so they were dropped from the
model. The only medications with significant effects were: Adalimumab, Infliximab,
Cyclosporine, and Prednisolone (Table 4.33 and Appendix Table F.33).
Table 4.33 Medications associated with either increased or decreased GIT infection
Type 3 Analysis of Effects
Effect DF Wald
Chi-Square
Pr > ChiSq
Adalimumab 9.00 17.65 0.04
Infliximab 9.00 20.52 0.02
Cyclosporine 9.00 45.78 <.0001
Prednisolone 9.00 21.30 0.01
180
In the analysis of maximum likelihood table, the effect of each medication was examined in
more detail:
Adalimumab: Currently taking Adalimumab was found to be significantly associated with a
reduction in the chance of mild GIT infection. However, if patient does not need to take
Adalimumab at all, the risk could be even less and can get up to 48 times less (CI: 0.314 to
0.888) (Table 4.34-4.35 and Appendix Tables F.34- F.35).
Infliximab: Currently taking Infliximab is associated with a significant increase (P Value:
0.0235) in the risk of moderate infection. The amount of this increase is almost 99 times more
than patients who never took this medication (Table 4.34-4.35 and Appendix Tables F.34-
F.35).
Cyclosporine: Currently taking this medication is associated with a higher risk of mild (P value
of <0.0001) and moderate (P value of <0.0001) GIT infection. This approaches 500 times
compared to patients who don’t take this medication (Table 4.34-4.35 and Appendix Tables
F.34- F.35).
Prednisolone: Currently taking prednisolone is associated with an increase in the risk of severe
GIT infection (P value: 0.0505) and there is marginal evidence (P value 0.065) that it can also
increase moderate GIT infection. This increase is more than 50 times compared to patients who
have never taken Prednisolone (Table 4.34-4.35 and Appendix Tables F.34- F.35).
In summary, use of Prednisolone was associated with an increased frequency of severe GIT
infection and use of both Infliximab and Cyclosporin were associated with increased rates of
moderate GIT infection
181
Table 4.34 Analysis of Maximum likelihood estimates in GIT infection
Analysis of Maximum Likelihood Estimates
Parameter Medication
Status
GIT
Infection
DF Estimate Standard
Error
Wald
Chi-
Square
Pr > ChiSq
Adalimumab
currently
taking
1.00 1.00 -0.64 0.26 5.81 0.02
2.00 1.00 0.13 0.17 0.62 0.43
3.00 1.00 -0.10 0.22 0.22 0.64
Infliximab
currently
taking
1.00 1.00 -0.61 0.72 0.71 0.40
2.00 1.00 0.69 0.31 5.13 0.02
3.00 1.00 0.24 0.46 0.27 0.61
Cyclosporine
currently
taking
1.00 1.00 1.89 0.47 16.32 <.0001
2.00 1.00 1.83 0.36 26.26 <.0001
3.00 1.00 0.75 0.72 1.09 0.30
Prednisolone
currently
taking
1.00 1.00 0.19 0.30 0.39 0.53
2.00 1.00 0.44 0.24 3.39 0.07
3.00 1.00 0.54 0.28 3.83 0.05
Conclusion: Differential effects on the frequency of GIT infections were observed amongst
users of csDMARDs and bDMARDs. Amongst csDMARDs, Cyclosporine was associated with
an increase in mild and moderate self-reported GIT infections, whereas prednisolone was
associated with an increase in severe self-reported GIT infections. Amongst bDMARDs, use of
infliximab was associated with an increase in moderate self-reported GIT infections, whereas
adalimumab was associated with a protective effect for mild, but not moderate or severe GIT
infections. The clinical relevance of this latter finding is uncertain. Once again, the potential
for corticosteroid therapy to confer risk for severe infection in multiple systems was evident.
182
Table 4.35 Estimation of odds ratios in GIT infection
Odds Ratio Estimates
Effect GIT
Infection
Point Estimate 95% Wald
Confidence Limits
Adalimumab - currently taking vs never taking
1.00 0.53 0.31 0.89
2.00 1.14 0.82 1.58
3.00 0.90 0.58 1.40
Infliximab - currently taking vs never taking
1.00 0.55 0.13 2.22
2.00 2.00 1.10 3.64
3.00 1.27 0.51 3.16
Cyclosporine - currently taking vs never taking
1.00 6.64 2.65 16.65
2.00 6.21 3.09 12.48
3.00 2.12 0.52 8.71
Prednisolone - currently taking vs never taking
1.00 1.21 0.67 2.17
2.00 1.55 0.97 2.48
3.00 1.72 1.00 2.97
3.9. Nervous System infection - analysis of medication confounders
Amongst 21506 observations, 9/21506 (0.0418 %) self-reported mild infection, 9/21506
(0.0418%) self-reported moderate infection, 12/21506 (0.055%) self-reported severe infection,
whereas 21476/21506 (99.86 %) reported no infection. In this model, nervous system infections
of different severity were compared to those in participants who did not have such infection
(Appendix tables G1-G3). The model used was a Multinomial logistic regression model. The
reason for using this model is because the outcome is a non-binary categorical variable. Using
pairwise Chi-square test without using the model can increase potential mistakes because the
number of comparisons is very large [20]. The model convergence status table (Appendix
Tables G.5-G.7) shows that the test does not meet the criteria for accuracy and the variables do
not fit the model of statistics.
Table 4.36 Estimation of the impact of confounders.
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 525.79 719.40
SC 549.71 1,963.67
-2 Log L 519.79 407.40
183
Overall, the test does not show any significant effect of anti-RA medicines on nervous system
infections (lr Chi-Square of 112.3885 with a P value of 0.9943) [20] (Table 4.36-4.37 and
Appendix table G.7).
Table 4.37 Estimation of fitness of tests in nervous system infection
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 112.39 153.00 0.99
Score 170.52 153.00 0.16
Wald 83.37 153.00 1.00
This means that the test is not reliable due to several potential reasons, including sample size,
and so it is not possible to draw reliable conclusions from the analysis.
3.10. TB infection - analysis of medication confounders
A multinomial logistic regression model was used to compare different the severities of TB
infection with a control group (respondents who have not had TB infection). The reason for
using this model was because the outcome was a non-binary categorical variable. The results
indicate that amongst 21506 observations 3/21506 (0.013 %) had mild TB infection, 6/21506
(0.027%) had moderate TB infection and 2/21506 (0.0092%) reported severe TB infection,
whereas 21495/21506 (99.94 %) reported no infection at all. The model convergence status
table shows that the test does not meet the criteria for accuracy and the variables do not fit the
model of statistics. Overall the test does not show significant difference (lr Chi-square of
93.7402 with a P value of 1) in between variable effects on the TB infection. This means that
the test is not reliable due to several potential reasons including sample size and we cannot
judge the conclusion out of such analysis.
Table 4.38 Estimation of the impact of confounders
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 216.60 428.86
SC 240.53 1,673.13
-2 Log L 210.60 116.86
184
Overall, the test does not show a significant difference (lr Chi-square of 93.7402 with a P value
of 1) between variable effects on TB infection (Table 4.39 and Appendix table H.7).
Table 4.39 Estimation of fitness of tests in TB infection
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 93.74 153.00 1.00
Score 191.37 153.00 0.02
Wald 26.52 153.00 1.00
This means that the test is not reliable due to several potential reasons including sample size
and so it is not possible to draw reliable conclusions from the analysis.
3.11. Urinary tract infection - analysis of medication confounders
Amongst 21506 observations, 290/21506 (1.34 %) self-reported mild infection, 833/21506
(3.87%) self-reported moderate infection, and 256/21506 (1.19%) self-reported severe
infection, whereas 20127/21506 (93.58 %) reported no infection. In this model, persons with
different categories of urinary tract infection are compared with people who don’t have this
type of infection (Appendix tables I1-I3). A multinomial logistic regression model was
employed to analyse the data. The reason for using this model is because the outcome is a non-
binary categorical variable. Using pairwise Chi-square test without using the model can
increase potential mistakes because the number of comparisons is very large [20]. The model
convergence status table (Appendix Tables I.5-I.7) shows that the test meets the criteria for
accuracy and the variables fit the model of statistics. Overall the test shows a significant
difference (lr Chi-square of 442.0070 with a P value of less than 0.0001) between variable
effects on urinary tract infection [20] (Table 4.40-4.41 and Appendix table I.7).
Table 4.40 Estimation of the impact of confounders
Model Fit Statistics
Criterion Intercept Only Intercept and covariates
AIC 12856.1 12720.09
SC 12880.03 13964.36
-2Log L 12850.1 12408.09
185
In the model fit statistics table (Appendix table I.7), the likelihood ratio or lr (difference
between -2 Log L or Deviance in the model which contains just the intercept and the one which
contains both the intercept and covariates) is 442. The P value is highly significant (Appendix
table I.1-I.7). This shows that a model with covariates strengthens the test and that the
covariates are actually impacting cofactors in urinary tract infection. Other tests such as SC and
AIC are also used to recheck this conclusion [22] (Tables 4.40-4.41 and Appendix table I.6).
Table 4.41 Estimation of fitness of tests in Urinary tract infection
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 442.0070 153 <.0001
Score 494.1448 153 <.0001
Wald 442.5757 153 <.0001
The Wald Chi-Square for overall test is also highly significant (0.0001) with a Chi-square of
almost 442 among 153 degrees of freedom. In other words, the impact on urinary tract infection
is not the same in different groups. This Chi-Square P value is almost equivalent to the p value
in the overall Pearson test. Indeed, the logistic regression result is much the same as in the
frequency table (Table 3.47) result, because it is a large sample. As a logistic regression and
not a linear regression model was used, Chi-square test for comparison. [21] (Appendix table
I.7).
The Score test (Lagrange multiplier test) requires estimating only a single model. The test
statistic is calculated based on the slope of the likelihood function at the observed values of the
variables in the model. Usually, the score tests are compared when new parameters are added.
It gives an estimate of how far the accuracy of the test improves when new parameters are
added, or existing parameters are deleted [21] (Table 4.41 and Appendix table I.7).
During the backward stepwise phase in the next part of the model, the effects of the medications
are dropped one by one to see how much there is a change in the Chi-square and to get an
estimate of the impact of that medication with regard to increasing or decreasing GIT infection
[22] (Appendix tables I8-I.31).
186
3.11.1. Wald Chi-square, Likelihood ratio test and Score test to test significance of
differences
As the size of the study population in this study is enough, we can use any of these three tests,
but if the size of the sample is small then it is necessary to check all three tests to increase the
reliability of the conclusions. Among these tests, the likelihood ratio test is the most reliable
test, because it stays unchanged even if the testing is reparametrized 3.10.1. Effects of different
medications on urinary tract infection [22] (Appendix I.7).
3.11.2. Effects of medications on Urinary tract infection
As the medication effects in this model are all qualitative, the degree of effect (impact) on
urinary tract infections infection can be easily worked out by comparing the categorical
variables [21].
For this section, a backward procedure in multinominal logistic regression was preferred.
Logistic regression is required because the outcome is categorical and as urinary tract infection
has three categories, notably mild, moderate and severe infection as well as a no infection
report, multinominal logistic regression is an appropriate model (Appendix Table I.4). The
backward stepwise procedure is used here because it is more accurate than a forward procedure
and considers the accumulating effect of all variables and starts with a bigger model. As the
model fit statistics (Appendix Table I.6) show that this large model is still fitted to the data, it
is appropriate for use. Also, there is no collinearity and no two variables are identical. This
makes it easier to use the backward model [22].
According to the summary table in the backward procedure (Table 4.42), the least significant
effect is from Abatacept followed by Anakinra, Certolizumab, Golimumab, Tocilizumab,
Sulphasalazine, Adalimumab, Rituximab, and Folic acid plus Methotrexate (plus Folic acid).
However, the effect of all these medications was found to be minimal and they were therefore
dropped from the model. There was evidence for an effect of the following medications in
respect to Urinary tract infection: Etanercept, Infliximab, Hydroxychloroquine, Leflunomide,
Azathioprine, Cyclosporine, Prednisolone, IM Gold Injection, and Penicillamine (Table 4.42
and Appendix Table I.32).
187
Table 4.42 Summary of backward elimination of anti RA medications and risk of urinary tract
infection
Summary of Backward Elimination
Step Effect
Removed
DF Number
In
Wald
Chi-Square
Pr > ChiSq
1.00 Abatacept 9.00 17.00 2.97 0.97
2.00 Anakinra 9.00 16.00 3.68 0.93
3.00 Certolizumab 9.00 15.00 6.06 0.73
4.00 Golimumab 6.00 14.00 4.11 0.66
5.00 Tocilizumab 9.00 13.00 9.34 0.41
6.00 Sulphasalazine 9.00 12.00 10.71 0.30
7.00 Adalimumab 9.00 11.00 13.75 0.13
8.00 Rituximab 9.00 10.00 15.73 0.07
9.00 Folic acid plus
Methotrexate
3.00 9.00 7.47 0.06
In the analysis of maximum likelihood table, the effect of each medication was examined in
more detail.
188
Table 4.43 Analysis of Maximum likelihood estimate in Urinary tract infection
Analysis of Maximum Likelihood Estimates
Parameter Medication
Status
Urinary
system
Infection
DF Estimate Standard
Error
Wald
Chi-
Square
Pr > ChiSq
Infliximab
currently
taking
Mild 1.00 0.33 0.34 0.98 0.32
Moderate 1.00 0.02 0.22 0.01 0.91
Severe 1.00 -1.49 0.74 4.06 0.04
Hydroxychloroquine
currently
taking
Mild 1.00 0.07 0.17 0.16 0.69
Moderate 1.00 0.08 0.11 0.53 0.47
Severe 1.00 -0.18 0.20 0.81 0.37
Leflunomide
currently
taking
Mild 1.00 -0.21 0.19 1.17 0.28
Moderate 1.00 -0.14 0.12 1.19 0.28
Severe 1.00 -0.53 0.24 5.01 0.03
Azathioprine
currently
taking
Mild 1.00 -11.88 269.90 0.001 0.96
Moderate 1.00 -0.80 0.59 1.84 0.17
Severe 1.00 -11.93 256.60 0.001 0.96
Cyclosporine
currently
taking
Mild 1.00 1.52 0.38 16.37 <.0001
Moderate 1.00 1.06 0.29 13.23 0.001
Severe 1.00 0.69 0.53 1.68 0.20
Prednisolone
currently
taking
Mild 1.00 -0.03 0.19 0.03 0.86
Moderate 1.00 0.36 0.12 9.16 0.001
Severe 1.00 0.78 0.24 10.51 0.001
IM Gold injection
currently
taking
Mild 1.00 -0.03 0.72 0.001 0.97
Moderate 1.00 0.48 0.33 2.10 0.15
Severe 1.00 1.11 0.44 6.56 0.01
Penicillamine
currently
taking
Mild 1.00 -12.05 451.70 0.001 0.98
Moderate 1.00 -12.12 270.80 0.001 0.96
Severe 1.00 -12.08 478.60 0.001 0.98
Based on the likelihood estimate table and odd’s ratio the following results are concluded:
Etanercept: There is no evidence that currently taking Etanercept is associated with urinary
tract infection (Table 4.43-4.45 and Appendix Tables I.34- I.35).
189
Infliximab: Currently taking Infliximab is associated with a reduced (P Value: 0.0438)
frequency of Urinary tract infection. However, patients who have never taken Infliximab have
almost 80 times less chance for Urinary tract infection (CI: 0.053 to 0.960) (Table 4.43-4.45
and Appendix Tables I.34- I.35).
Hydroxychloroquine: There is no evidence that currently taking Hydroxychloroquine is
associated with urinary tract infection (Table 4.43-4.45 and Appendix Tables I.34- I.35).
Leflunomide: Currently using Leflunomide is associated with a reduced reports of severe
urinary tract infection. The amount of this reduction was almost 58% compared to non-users of
LEF (CI: 0.367 to 0.936 P value: 0.0252) (Table 4.43-4.45 and Appendix Tables I.34- I.35).
Azathioprine: There is no evidence that currently taking Azathioprine is associated with
urinary tract infection (Table 4.43-4.45 and Appendix Tables I.34- I.35).
Cyclosporine: Currently taking Cyclosporine is associated with an increased frequency of mild
and moderate urinary tract infection. Compared to participants who have never taken
Cyclosporine, the extent of this increase is 358-fold for mild and 187-fold for moderate urinary
tract infection.
Prednisolone: Currently taking Prednisolone is associated with an increased frequency of
moderate and severe urinary tract infection. Compared to participants who have never taken
Prednisolone, the extent of the increase is 43-fold for moderate and 117-fold for severe urinary
tract infection (Table 4.43-4.45 and Appendix Tables I.34- I.35).
IM Gold Injection: Currently receiving parenteral Gold is associated with an increase in the
frequency of severe urinary tract infection (P value: 0.0104). Compared to participants who
have never received parenteral Gold, the extent of the increase is 204-fold (CI: 1.299 to 7.151)
(Table 4.43-4.45 and Appendix Tables I.34- I.35).
Penicillamine: There was no evidence that currently taking Penicillamine is associated with an
increased frequency of urinary tract infection (Table 4.43-4.44 and Appendix Tables I.34- I.35).
Conclusion: In summary use of Prednisolone and Cyclosporin was found to be associated with
moderate and severe UTIs in the case of the former and moderate and mild, but not severe UTIs
in the case of the latter. Use of Infliximab and Leflunomide appeared to have protective effects
in respect to UTI.
190
Table 4.44 Estimation of Odds ratios in participants with urinary tract infection
Odds Ratio Estimates
Effect Urinary system
Infection
Point
Estimate
95% Wald
Confidence Limits
Etanercept - currently taking vs
never taken
Mild 1.39 1.04 1.84
Moderate 0.90 0.75 1.06
Severe 0.91 0.65 1.26
Infliximab - currently taking vs
never taken
Mild 1.39 0.72 2.69
Moderate 1.03 0.67 1.58
Severe 0.23 0.05 0.96
Hydroxychloroquine - currently
taking vs never taken
Mild 1.07 0.76 1.51
Moderate 1.08 0.87 1.34
Severe 0.84 0.57 1.23
Leflunomide - currently taking vs
never taken
Mild 0.81 0.56 1.19
Moderate 0.87 0.68 1.12
Severe 0.59 0.37 0.94
Azathioprine - currently taking vs
never taken
Mild <0.001 <0.001 >999.99
Moderate 0.45 0.14 1.43
Severe <0.001 <0.001 >999.99
Cyclosporine - currently taking vs
never taken
Mild 4.59 2.19 9.60
Moderate 2.88 1.63 5.09
Severe 1.99 0.70 5.66
Prednisolone - currently taking vs
never taken
Mild 0.97 0.67 1.40
Moderate 1.43 1.14 1.81
Severe 2.17 1.36 3.47
IM Gold - currently taking vs never
taken
Mild 0.97 0.24 3.98
Moderate 1.62 0.84 3.11
Severe 3.05 1.30 7.15
Penicillamine - currently taking vs
never taken
Mild <0.001 <0.001 >999.99
Moderate <0.001 <0.001 >999.99
Severe <0.001 <0.001 >999.99
As the anatomy of urinary system is different between two sexes it will be appropriate to assess
if these differences can modify the effect of tablets. In other word if the effect of Anti RA
191
medication on UTI is the same for male and female sexes. In table 4.45 the interaction between
anti RA medication and sex in UTI has been assessed.
Table 4.45 Estimation of interaction between anti RA medication and sex
Type 3 Analysis of Effects
Effect DF Wald
Chi-Square
Pr > ChiSq
Interaction with Etanercept 2 17.4683 0.0002
Interaction with Tocilizumab 2 11.0376 0.0040
Interaction with Hydroxychloroquine 3 8.9707 0.0297
Interaction with Leflunomide 2 9.9919 0.0068
Interaction with Prednisolone 2 15.1467 0.0005
Table 4.45 shows that sex modifies effects of Etanercept, Tocilizumab, Hydroxychloroquine,
Leflunomide and Prednisolone. In order to find the differences in more details, table 4.46
presents the pairwise test in each anti RA medication. Based on the results of this table
Leflunomide is associated with increased rate of infection in male sex, but all other discussed
anti RA medications (Etanercept, Tocilizumab, Hydroxychloroquine and prednisolone) were
associated with less frequent UTI in male than in female (Table 4.46).
With Etanercept (odds ratio 0.10) and Tocilizumab (odds ratio 0.0044) and prednisolone (odds
ratio 0.049), the effect of these medications is stronger in the female sex than the male sex.
However, Leflunomide (odd’s ratio of 0.88) can significantly increase UTI in the male sex.
192
Table 4.46 Significant Sex interactions with anti-RA medications
Analysis of Maximum Likelihood Estimates
Parameter
DF Estimate Standard
Error
Wald
Chi-
Square
Pr > ChiSq
Intercept male 1 -1.2366 0.2185 32.0290 <.0001
Intercept female 1 1.7962 0.2227 65.0781 <.0001
Sex male 1 0.3896 0.4426 0.7748 0.3787
Sex female 0 0 . . .
Interaction with
Etanercept male
currently
taking 1 -1.0522 0.4816 4.7733 0.0289
Interaction with
Tocilizumab male
currently
taking 1 -4.1742 1.3291 9.8643 0.0017
Interaction with
Hydroxychloroquine male
currently
taking 1 -0.5432 0.5263 1.0652 0.3020
Interaction with
Leflunomide male
currently
taking 1 1.1168 0.5347 4.3624 0.0367
Interaction with
Prednisolone male
currently
taking 1 -1.7637 0.5440 10.5125 0.0012
Conclusion:
Differential effects on the frequency of urinary tract infections were observed amongst users of
csDMARDs and bDMARDs. Cyclosporine, IM Gold and Prednisolone increased the risk for
urinary tract infections, whereas Leflunomide protected against severe urinary tract infections
mainly in female sex but can significantly increase risk of UTI in male sex. None of the
evaluated biologic agents increased the frequency of UTIs in both sexes and Infliximab had an
unequivocal protective effect, the clinical significance of which is uncertain.
193
3.12. Viral infection - analysis of medication confounders
Amongst 21506 observations, 435/21506 (2.022 %) self-reported mild infection, 837/21506
(3.89%) self-reported moderate viral infection; 305/21506 (1.41%) self-reported severe viral
infection, whereas for 19929/21506 (92.66 %) patient-visits, no infections were reported. In
this model different categories of viral infection were compared with people who did not self-
report such infection (Table 4.47) (Appendix tables I1-I3). A multinomial logistic regression
model was used to analyse the data. The reason for using this model is because the outcome is
a non-binary categorical variable. Using pairwise Chi-square test without using the model can
increase potential mistakes because the number of comparisons is very large [20].
Table 4.47 Estimation of the impact of confounders
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 14,465.34 14,470.56
SC 14,489.27 15,714.83
-2 Log L 14,459.34 14,158.56
The model convergence status table (Appendix Tables I.5-I.7) shows that the test meets the
criteria for accuracy and the variables fit the model of statistics. Overall the test shows
significant difference (lr Chi-square of 300.7784 with a P value of less than 0.0001) between
variable effects on VIRAL infections [20] (Table 4.47) (Appendix table I.7).
Table 4.48 Estimation of fitness of tests in viral infection
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 300.7784 153 <.0001
Score 331.3978 153 <.0001
Wald 311.3192 153 <.0001
In the model fit statistics table (Appendix table I.7), the likelihood ratio or lr (difference
between -2 Log L or deviance in the model (which just contains intercept and the one which
contains intercept and covariates) is 300.7784 (Table 4.48). The P value is highly significant
(Table 4.48) (Appendix table I.1-I.7). This shows that a model with covariates is making the
test more significant and covariates are actually impacting cofactors in respect to VIRAL
194
infections. Other tests such as SC and AIC are also used to recheck this conclusion
[22](Appendix table I.6).
Wald Chi-square for overall test is also highly significant (0.0001) with a Chi-square of almost
300 among 153 degrees of freedom. In other words, the impact on VIRAL infection is not the
same in different groups. This Chi-square P value is almost equivalent to the p value in the
overall Pearson test. Indeed, the logistic regression result is much the same as it is in the
frequency table (Tables 3.63-3.64) because it is a large sample. As the model is a logistic
regression and not a linear regression model, the Chi-square test has been used for comparison.
[21] (Table 4.48) (Appendix table I.7).
The Score test (Lagrange multiplier test) requires estimating only a single model. The test
statistic is calculated based on the slope of the likelihood function at the observed values of the
variables in the model. Usually, the score tests are compared when new parameters are added.
It gives an estimate of how far the accuracy of the test improves when new parameters are
added, or existing parameters are deleted [21] (Table 4.48) (Appendix table I.7). During the
backward stepwise phase in the next part of the model, the effects of the medications are
dropped one by one to see how much there is a change in the Chi-square and to get an estimate
of the impact of that medication with regard to increasing or decreasing VIRAL infection [22]
(Appendix table I.8-I.31).
3.12.1. Selection between Wald Chi-square, Likelihood ratio test and Score test to test
significance of differences
As the size of the study population in this study is large enough, any of these three tests can be
used, but if the size of the sample is small then it is necessary to check all three tests to increase
the reliability of the conclusions. Among these tests, the likelihood ratio test is the most reliable
test, because it stays unchanged even if the data being tested is reparametrized what we are
testing [22] (Table 4.49) (Appendix table I.7).
3.12.2. Effects of different medications on the frequency of viral infections
As the variables are all categorical variables, the effects of each variable on viral infection can
be examined by studying its coefficient, directly [21].
195
For this section, a backward procedure in multinominal logistic regression was preferred.
Logistic regression is required because the outcome is categorical and as viral infection has
three categories, notably: mild, moderate and severe infection as well as no infection report,
multinominal logistic regression is an appropriate model (Table 4.49) (Appendix Table I.4).
Also, a backward stepwise procedure is used here because it is more accurate than a forward
procedure and considers the accumulating effect of all variables and starts with a bigger model.
As the model fit statistics (Appendix Table I.6) show that this large model is still fitted to the
data, it is appropriate for use. Furthermore, there is no collinearity and no two variables are
identical. This makes it easier to use the backward model [22].
As can be seen in the summary table for the backward procedure analysis (Table 4.49 and
Appendix Table I.32), the least significant effect is from Penicillamine, Certolizumab,
Golimumab, Leflunomide, Abatacept, IM Gold injection, Azathioprine, Sulphasalazine,
Anakinra, Tocilizumab, Adalimumab, Rituximab, and Infliximab.
Table 4.49 Summary of backward elimination of anti-RA medications and risk of viral
infection
Step Effect
Removed
DF Number
In
Wald Chi-Square Pr > ChiSq
1 Penicillamine 9 17 4.95 0.84
2 Certolizumab 9 16 8.19 0.51
3 Golimumab 6 15 5.47 0.48
4 Leflunomide 9 14 7.71 0.56
5 Abatacept 9 13 10.03 0.35
6 IM Gold injection 9 12 10.95 0.28
7 Azathioprine 9 11 11.21 0.26
8 Sulphasalazine 9 10 9.91 0.36
9 Anakinra 9 9 10.14 0.34
10 Tocilizumab 9 8 11.97 0.21
11 Adalimumab 9 7 15.06 0.09
12 Rituximab 9 6 16.70 0.05
13 Infliximab 9 5 13.14 0.15
196
However, the effect of all these medications was minimal and they were, therefore, dropped
from the model. According to type 3 analysis of effects (Table 4.32), medications which
significantly affected the frequency of viral infection in RA include: Etanercept, Methotrexate
(plus Folic acid), Hydroxychloroquine, Cyclosporine, and Prednisolone (Table 4.51 and
Appendix Table 4.32).
Table 4.50Medications which increase the frequency of viral infection
In the analysis of maximum likelihood table, the effect of each medication is examined in more
detail:
Etanercept: There is marginal evidence that currently taking Etanercept is associated
with an increase in mild viral infection (P value 0.0573). This increase is almost 24
times more than in people who never taken this medication (CI: 0.993 to 1.550) (Table
4.51-4.52 and Appendix Tables I.34- I.35).
Methotrexate (plus Folic acid): Currently taking Methotrexate (plus Folic acid) is
associated with a reduction in mild and moderate viral infection. However, if the patient
does not take this medication at all, the risk will be almost 25 times less (Table 4.51-
4.52 and Appendix Tables I.34- I.35).
Hydroxychloroquine: Currently taking Hydroxychloroquine is associated with an
increase in moderate viral infection (P value 0.0117). This risk is almost 30 times more
than in people who never took this medication (Table 4.51-4.52 and Appendix Tables
I.34- I.35).
Cyclosporine: Currently taking Cyclosporine is strongly associated with an increase in
the risk of moderate viral infection (P value: 0.0008). The amount of this increase is 167
Type 3 Analysis of Effects
Effect DF Wald
Chi-Square
Pr > ChiSq
Etanercept 9.00 43.98 <.0001
Folic acid plus Methotrexate 3.00 13.78 0.001
Hydroxychloroquine 9.00 29.30 0.001
Cyclosporine 9.00 29.31 0.001
Prednisolone 9.00 25.64 0.001
197
times more than in patients who have never taken Cyclosporine (CI 1.503 to 4.777)
(Table 4.51-4.52 and Appendix Tables I.34- I.35).
Prednisolone: Currently taking Prednisolone is associated with increased rates of
moderate and severe viral infection. The extent of this increase is 41-fold greater in the
case of moderate and 108-fold greater in the case of severe viral infection respectively
(Table 4.51-4.52 and Appendix Tables I.34- I.35).
Table 4.51 Analysis of maximum likelihood estimate in Viral infection
Analysis of Maximum Likelihood Estimates
Parameter Medication
Status
Viral
Infection
DF Estimate Standard
Error
Wald Chi-
Square
Pr > C
hiSq
Etanercept
currently
taking
Mild 1.00 0.22 0.11 3.61 0.06
Moderate 1.00 -0.07 0.09 0.66 0.42
Severe 1.00 0.02 0.14 0.01 0.91
Methotrexate (plus
Folic acid)
currently
taking
Mild 1.00 -0.28 0.12 5.25 0.02
Moderate 1.00 -0.25 0.09 8.16 0.001
Severe 1.00 -0.13 0.14 0.88 0.35
Hydroxychloroquine
currently
taking
Mild 1.00 0.22 0.14 2.55 0.11
Moderate 1.00 0.26 0.11 6.35 0.01
Severe 1.00 0.14 0.17 0.66 0.42
Cyclosporine
currently
taking
Mild 1.00 0.001 0.59 0.001 1.00
Moderate 1.00 0.99 0.30 11.16 0.001
Severe 1.00 0.06 0.72 0.01 0.94
Prednisolone
currently
taking
Mild 1.00 0.001 0.15 0.001 0.98
Moderate 1.00 0.35 0.12 7.81 0.01
Severe 1.00 0.74 0.22 11.18 0.001
Conclusion:
Differential effects on the frequency of viral infections were observed amongst users of
csDMARDs and bDMARDs. Amongst csDMARDs recipients, Cyclosporine and Prednisolone
substantially increased the risk for viral infections, whereas only a modest increase was
198
observed with Methotrexate (plus Folic acid) and Hydroxychloroquine. Amongst bDMARDs
recipients, Etanercept increased viral infections slightly. Overall no major effect was observed
amongst bDMARDs users (Table 4.51-4.52 and Appendix Tables I.34- I.35).
Table 4.52 Estimation of odds ratios in viral infection
Odds Ratio Estimates
Effect Viral
Infection
Point
Estimate
95% Wald
Confidence Limits
Hydroxychloroquine - currently
taking vs never taken
Mild 1.25 0.95 1.64
Moderate 1.30 1.06 1.60
Severe 1.15 0.82 1.60
Cyclosporine - currently taking vs
never taken
Mild 1.00 0.32 3.16
Moderate 2.68 1.50 4.78
Severe 1.06 0.26 4.31
Prednisolone - currently taking vs
never taken
Mild 1.00 0.74 1.34
Moderate 1.41 1.11 1.80
Severe 2.09 1.36 3.22
3.12.3 Chapter Conclusion
Infections of diverse severity were observed commonly in the ARAD cohort, in keeping with
the high rates expected for active RA in a rheumatoid population biased toward the higher end
of the age spectrum, where moderately high levels of functional impairment are operative and
comorbidities are common. Based on ARAD data from 2001 to 2014, the highest to lowest
rates of major organ infections reported by questionnaire respondents receiving diverse
therapies were: EENT infection, skin and nail infection, lung infection, viral infection, kidney
and urinary tract infection (Figure 4.1). As might be expected, the frequency of use of one or
more than one anti-RA medications of various types was high amongst RA participants in
ARAD. This accords with the targeting of patients who were about to commence a biologic
therapy and in whom prior usage of csDMARDs was government-mandated, thereby ensuring
use of multiple therapies not only in the quest for disease control, but also to satisfy prescribing
restrictions.
199
Anti-RA medications were found to be used with divergent frequencies across the full range of
RA respondents. From the most frequent to the least frequent medication, anti-RA medications
used were Etanercept, Adalimumab, Methotrexate, Hydroxychloroquine, Sulphasalazine,
Rituximab, Abatacept, Prednisolone, Tocilizumab, Infliximab and Leflunomide respectively
(Table 4.1). Overall, although a number of differences exist between current RA treatment
guidelines, there are some general principles. Remission or low disease activity is the preferred
target. csDMARDs should be started soon after diagnosis and, usually, methotrexate is in the
first line. It is important to monitor disease activity regularly and, if disease remains active
persistently, biologics therapies should be used, as well[15].
Strengths of this study include large numbers of participants derived from real-world
experience in community clinical practice and the considerable number of sequential visits.
Substantial confidence concerning the primary diagnosis of RA is a further strength, since
subsets of patients have been classified under ACR criteria with strong diagnostic
concordance apparent. The large numbers aid in statistical analysis and the robustness of the
statistical modelling strengthens the analysis.
This study has several limitations. Importantly, infections were self-reported and unvalidated,
so their veracity cannot be substantiated. No microbiological reports or family physician
corroborations were available. A different form of categorisation, whilst readily understood
(notably: mild or moderate or severe), was, on the one hand, helpful but, on the other hand,
not, since it precluded comparison with the more widely utilised categories of (SI, frequently
found in publications and meta-analyses. Furthermore, SIs could not be easily deduced, since
hospitalisation data was not available across the full duration of the study period, notably
2001 to 2014. A further pitfall was the lack of comparability between users of bDMARDs and
those who had not taken such medication. Those who did not progress to bDMARDs had less
severe disease that was, for the most part, amenable to simpler therapy and, thus, differences
in respect to type and severity of infection may relate to differences in disease severity and
not the type of medications taken. The agents used for treatment varied considerably and
reflected the timing of introduction for clinical use and also prescriber preferences. Thus, the
number of bDMARDs for different categories is skewed toward TNF inhibitors, which in a
number of cases limited comparability with other bDMARDs due to uneven numbers of users
200
and sometimes very small numbers of recipients. Furthermore, this study was carried out in
the pre-orally active csDMARDs era.
All in all, the reports for infection in RA came to a total of 757/1947 (38.88 %) in comparison
to 1190/1947 (61.11%) in whom there was no infection.
• In EENT infection, most participants reported infection of moderate severity.
• In lung infection, most participants reported infections in a moderate (6.41%) or severe
category (2.9%).
• In skin and nail infection, most participants reported either mild (5.82%) or moderate
(4.83%) infection.
• In artificial joint infection, most participants reported infections in either a severe
(0.36%) or moderate (0.18%) category.
• In bone, joint and muscle infections, most participants reported severe (1.12%) or
moderate (0.99%) infections.
• In blood infection, most participants reported infections of either severe (0.51%) or
moderate (0.31%) severity.
• In GIT infection, most participants reported infections in either a moderate (1.12%) or
severe (0.72%) category.
• In urinary tract infection, most participants reported infections in either a mild (1.34%)
or moderate (3.87%) category.
• In viral infection, most participants reported infections in either a mild (2.022%) or
moderate (3.89%) category.
• Nervous system and mycobacterium tuberculosis (TB) infections were very rare.
Based on the findings in this study, the csDMARDs and bDMARDs drugs that either protect
against or predispose to infection in RA can be tabulated as shown in the table below (Table
4.53). Details are provided for different organ systems. Prednisolone was found to strongly
predispose to moderate or severe infections in multiple organ systems, notably: EENT; lung;
skin and nail; bone, joint and muscle; blood; GIT; urinary tract and in respect to viral infections.
This finding accords with clinical experience and the well documented capacity of
corticosteroids to predispose to infection in many systems. Other csDMARDs associated
strongly with moderate or severe infection were cyclosporine for multiple systems (EENT,
201
lung, GIT, urinary tract and viral infections), hydroxychloroquine for blood infection alone and
parenteral Gold for urinary tract infection alone. As cyclosporine has potent
immunosuppressive properties, it is not surprising that it is implicated in infections in multiple
organ systems. Whether the hydroxychloroquine and parenteral gold observations are clinically
important is uncertain.
Amongst bDMARDs, only Infliximab was associated with moderate or severe infections in
multiple organ systems (EENT, skin and nail, GIT). Adalimumab was associated with moderate
or severe infection in the skin and nails alone. Etanercept, Certolizumab, Golimumab,
Abatacept, Tocilizumab and Rituximab were not associated with moderate or severe infection
in any system. The possibility that these agents do predispose to moderate or severe infection
when used in combination with other agents cannot be discounted. Since, for some of these
infection,s the numbers of patients who contracted such infections was less than 100 (Blood
infection, GIT infection, Nervous system infection) and the number of observations over time
correspondingly smaller, there is a need to exercise caution in applying these sample results to
the wider population.
Several csDMARDs appear to protect against infections in selected systems. For example,
methotrexate was associated with a protective effect in EENT and viral infections, leflunomide
was associated with a protective effect in urinary tract infection alone and hydroxychloroquine
against viral infection alone. Whether such effects are clinically meaningful is also uncertain,
but somewhat doubtful. Amongst bDMARDs, etanercept appeared to protect against EENT,
lung and viral infection, whereas adalimumab protected against lung, artificial joint and GIT
infection. Infliximab was associated with a protective effect in urinary tract infection. Again,
such effects are of uncertain significance and need to be independently validated.
202
Table 4.53 Summary of anti RA medication impacts on different types of infection
Type of infection Safest Medications Medications associated with higher
rates of moderate or severe infections
EENT Etanercept, Methotrexate Cyclosporine, Prednisolone, Infliximab
Lung Etanercept and Adalimumab Cyclosporine and Prednisolone
Skin and nail Leflunomide Prednisolone, Infliximab, Adalimumab
Artificial Joint Adalimumab Not enough information
Bone, Joint and muscle Not enough information Leflunomide and Prednisolone
Blood infection Not enough information Hydroxychloroquine and Prednisolone
GIT Adalimumab Cyclosporine, Prednisolone,
Infliximab.
Nervous system Not enough information Not enough information
TB Not enough information Not enough information
Urinary tract infection Leflunomide and Infliximab Cyclosporine, Prednisolone and IM
Gold
Viral Infection Etanercept, Methotrexate
and Hydroxychloroquine
Cyclosporine and Prednisolone
These findings need to be validated in independent studies. However, they do provide new
insights into the likely differential effects of csDMARDs and bDMARDs on diverse infections.
They confirm known and suspected risks associated with corticosteroid use and potent TNF
inhibitors, such as infliximab. These different effects were observed across multiple anatomical
systems. Some protective effects were observed which, if confirmed in further studies, might
allow an opportunity for selection of one agent over another, particularly where a high risk for
infections is known to apply, based on comorbidities or previous infection history. Thus, it may
be possible, taking all factors into consideration, to make an informed choice, rather than one
more arbitrary, thereby enhancing patient safety without compromising clinical outcomes. This
application of a personalised medicine approach has the potential to reduce the morbidity and
mortality associated with non-serious and very importantly with serious infections of diverse
aetiologies.
203
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CHAPTER 5
Serious Infections in Rheumatoid Arthritis
207
Abstract
Objective: The main goal of this study is to evaluate self-reported infections in patients
suffering from rheumatoid arthritis and to determine the level of impact from different
csDMARDs and bDMARDs as potential risk factors.
Method: ARAD reports were collected from 2001 to 2014 and cleaned by deleting all
duplicated answers, single answers and faulty/incomplete patients reports. Overall 27,709 visits
from 3110 patients during 2001 to 2014 were collected. Based on our definition for serious
infection, patients’ reports were searched for evidence of hospitalisation or IV infusion for
infection.
Results: Out of 27,709 visits during 2001 to 2014, 811 patients had reported serious infection,
a prevalence of almost 3 %. Also, among all patient who took bDMARDs, adalimumab and
etanercept were the most common medications with association with serious infection. Other
factors such as age and gender, alcohol consumption, biologics, prednisolone, diseases such as
diabetes, kidney disease, liver disease, heart attack and sometimes previous coronary artery
bypass grafting (CABG) were all shown to have contribution in the development of SIs. These
risk factors have been used to generate an equation which assists in predicting the development
of SIs due to a range of risk factors.
Conclusion: There is clearly an increasing trend for serious infection (SI) among patients who
were treated with biologics.
208
1. Introduction
Rheumatoid arthritis (RA) is a chronic multisystem, immuno-inflammatory disease, the
cardinal features of which are joint deformity and damage in most, but not all cases. Destructive
polyarthritis is common and can be severely disabling and diminish quality of life. The major
clinical manifestation of RA is persistent and progressive synovitis, mostly in the peripheral
joints, leading to resorption of cartilage and subchondral bone. Joint disease in RA is usually
symmetrical, polyarticular and when destructive the joint damage is usually irreversible [1].
The prevalence of the disease increases with increasing age, but it may happen at any age, with
the peak incidence between the fourth and sixth decades. RA may be diagnosed as early as 3
months from onset up to 2 years when the disease is established. Depending on the diagnosis
the prevalence of RA is up to 0.5–1% of the world’s population. The female sex is usually up
to three times more susceptible to the disease than the male sex [1][2].
RA can cause chronic pain and joint destruction, premature mortality, and elevated risk of
disability, with high costs for victims and for society. It is a heterogeneous disease comprising
several subsets of patients with variations in pathogenesis, but it usually stems from due to a
sustained specific immune response directed against unknown self-antigens. The characteristic
of this autoimmune reaction is a cellular infiltration and synovial inflammation resulting in
tissue damage. The major pathophysiological events in RA include mononuclear cell
infiltration in the sub intimal layer, hyperplastic changes in synovial lining cells and formation
of a destructive type of synovial tissue known as pannus that invades the interface between
cartilage and bone. Chronic synovitis can progress to the destruction of adjacent bone and
cartilage, leading to joint deformity and disability [1] [3].
All aspects of RA treatment have changed in the past 25 years. The pathogenic basis of RA also
plays a role in its treatment. As early onset of structural damages is usual in RA, late treatment
can cause more than 50% disability in this disease. Therefore, early treatment of the disease is
an important objective [4]. Treatment in RA usually has three main goals including elimination
of pain, prevention of joint damage and improvement of joint function. Usually treatment plans
change depending on the disease activity, severity of symptoms, signs and prognosis [5].
209
Disease-modifying anti-rheumatic drugs (DMARDs) are medicines which are generally used
to control RA. They interfere with the immune system to suppress the overactive immune
system in RA, decreasing inflammation and progression of the disease process. These drugs are
categorized as biologics and non-biologics, where non-biologics are indirect and nonspecific
immune suppressants and biologics interfere with a specific aspect of the immune system
(Chiurchiù & Maccarrone, 2011)[6]. [7].
Non-biologic medicines suppress the immune system indirectly, while biologics suppress
immune system directly by interfering with a specific mediator. For example, methotrexate acts
as a folic acid analogue to inhibit different pathways in the immune system, but the inhibition
of tumor necrosis factor (TNF) ) by biologic DMARDs is achieved with a monoclonal antibody
such as infliximab (Remicade), adalimumab (Humira), certolizumab pegol (Cimzia), and
golimumab (Simponi), or with a circulating receptor fusion protein such as etanercept
(Etanercept or Brenzys)[8] [6][7].
All the currently available conventional synthetic DMARDs are associated with limited
efficacy and many of them cause important side effects. Due to these side effects, the majority
of patients have to stop non biologic DMARDs within 1-2 years. Aletaha and Smolen found
that, among 593 patients with RA, comprising 1319 courses of DMARD therapy over 2378
patient-years of treatment, retention rates were less than 24 months in most cases and treatment
courses were terminated mostly due to adverse effects and toxicity (42%) and sometimes lack
of sufficient efficacy (37%) [6][7].
1.1. Aims
The aims of this study were to:
• determine the frequency of serious infections (SIs) amongst ARAD participants who
were mostly taking bDMARDs;
• Investigate for apparent differences, if present, amongst patients taking bDMARDs
and those taking csDMARDs;
• Identify and evaluate potential clinical risk factors for infections e.g. age,
comorbidities, use of corticosteroids, synthetic DMARDs and biologic DMARDs,
and assess ARAD data to investigate possible roles of behavioural, environmental
and genetic risk factors in the development of serious infections in RA patients.
210
1.2. Hypothesis
The aims are based on the following hypotheses:
• Infections and specifically serious infections are common in RA and likely modulated
by medication use;
• It is possible to predict the approximate risk of infection based on risk factors that a
patient possesses; and
• Type of medication, duration of medication usage and dosage of the medication can all
impact the frequency of infection.
In order to assess these points, the ARAD data has been statistically analysed for the rate of
serious infections (SIs). This study includes the rate, as well as the type of infection in more
than 3000 RA patients.
The following points will be discussed:
(i) Descriptive analysis of infection in RA including the comparison of the SIs between
patients on csDMARDs and bDMARDs;
(ii) Comparison of the frequency of use of different biologics in serious infection;
(iii) Assessing the status of prednisolone and methotrexate in producing serious
infection;
(iv) Discussing the different potential risk factors for serious infection in more details;
and
(v) Predicting risk of serious infection based on the impacts from different risk factors.
2. Methods
2.1. Data Collection
The data were collected from the ARAD, in which a cohort of 3569 RA patients (960 males
and 2609 females) who had completed related questionnaires 28176 times (during 2001-2014)
were investigated for the development of SIs associated with a range of risk factors. Among the
3569 patients, 459 patients were eliminated because they had filled out the questionnaire only
once. We were therefore left with 3110 patients. After deducing eight duplications, at the end
we came up to 27709 visits and, amongst these visits, 811 were classified as having developed
211
SIs and these were used to calculate SIs/100 patient-years. Using the ARAD database, RA
patients with serious infections were identified, and their age, sex, type of infection, and type
of medications used were all extracted for further statistical analysis.
2.2. Statistical Analysis
This data was subjected to a series of descriptive and inferential statistical analyses, including
the determination of summaries of the frequencies, ratios, proportions, incidence rates, and the
rate of possible complications, as well as age and gender. In addition, a series of descriptions,
including comparison of central location in frequencies and dispersion in both intervention and
control variables, were undertaken. To facilitate the discussion around possible clinical risk
factors for the development of serious infections in RA patients, relative risks and odds ratios
were also calculated. In appropriate circumstances, these calculations can examine the
association between variables and side effects.
We calculated patient-years of treatment for diverse treatment categories by dividing the
number of SIs associated with each therapeutic by the sum of the lengths of time or cumulative
exposure time during which each patient was taking that medicine. This enables assessment of
the association between different bDMARDs and SIs while considering the number of patients
and the duration of the period that they were taking these therapies. Furthermore, categorical
statistical analysis using Chi-squared test between medications such as bDMARDs and a range
of potential risk factors were calculated. The results of the Chi-squared tests helped determine
the true association between intervention factors and SIs in this research. In this study we
reviewed the following risk factors: gender, alcohol intake, prednisolone, diabetes (non-insulin-
dependent diabetes mellitus (T2DM) as well as insulin dependent diabetes mellitus (T1DM),
lung, kidney, and liver disease, heart attack, coronary artery bypass grafting (CABG), and
stenting status. From these chi-squared values, the relevant p-values were calculated.
A generalized linear mixed model was applied to the data from the 28679 visits by the 3110
patients who visited more than once. Based on that, a model was constructed which expressed
the natural logarithm of the odds for SI in terms of five predictor variables: age (in years), sex
(M or F), alcohol, biologics (currently on any biologic? yes/no) and prednisolone (currently
taking prednisolone? yes/no).
212
A histogram was created to demonstrate the age (in years) of each participant on his/her first
visit. A series of statistical analyses was carried out to determine the number and the percentage
of male/female patients who had self-reported their use of various biologics as (i) Never taken
or Don't know, (ii) Currently taking, or (iii) Stopped taking.
Wherever DMARDs are discussed in this study, DMARDs is divided into csDMARDs and
bDMARDs. csDMARDs or conventional synthetic DMARDs include: 1- Methotrexate – oral
or parenteral, 2- Hydroxychloroquine (Hydroxychloroquine), 3- Sulphasalazine, 4-
Leflunomide, 5- Azathioprine, 6- Cyclosporine. bDMARDs or Biologics or biological
DMARDs include: 1- Humira/Adalimumab, 2- Etanercept/Etanercept, 3- Kineret/Anakinra, 4-
Remicade/Infliximab, 5- Mabthera/Rituximab, 6- Orencia/Abatacept, 7- Actemra/Tocilizumab,
8- Simponi/Golimumab, 9- Cimzia/Certolizumab Pegol.
Prednisolone, IM gold and Penicillamine do not belong to any group and are studied separately.
3.0 Results and discussion
Amongst the 27709 visits made by RA patients who had taken part in the study and had filled
in the questionnaire more than once, 811 visits were confirmed to relate to patients who had
developed serious infections, a prevalence of 2.92 %. Our data were examined for the impact
of several predictor variables: medications, age, gender and length of time in program. Some
combinations of these variables were also considered. This is described below.
3.1. Analysis of Rheumatoid Arthritis (RA) and Serious Infections (SIs) in Australia
Analysis of the association between the frequency of taking different anti RA medications and
development of serious infection. Among all different anti RA medications, it seems that taking
most of the medication between RA and RA with SIs are same, except for Adalimumab (Arrow
in the table) which is the most frequent medication in Sis followed by Etanercept (figure 5.1).
213
Duration in years
Figure 5.1 Differences in biologics between Rheumatoid Arthritis and Rheumatoid Arthritis
with Serious infection. Adalimumab is indicated by arrow
Figure 5.2 shows the absolute number of self-reported SIs amongst recipients of prednisolone.
The three coloured lines depict the categories of prednisolone usage, notably current, previous
and never used. Here, we are comparing frequency of taking prednisolone in patients with
serious infection compare to RA group. As it is apparent in the serious infection group,
‘currently taking prednisolone’ has the highest rate, followed by’ stopped taking’. The
frequency of ‘Never taking’ prednisolone is almost same in both group and its graph has
become flat (Figure 5.2). This indicates a significant role for prednisolone in serious infection.
214
Duration in years
Figure 5.2 Serious infection and prednisolone usage
The situation changes with methotrexate. Figure 5.3 is comparing the frequency of taking
methotrexate in patients with serious infection compare to RA group. As it is apparent in serious
infection group never taken methotrexate has the highest rate followed by patients who were
taking Methoteraxate and recently stopped taking. The frequency of “currently taking”
Methotrexate is almost same in both groups and its graph has become almost flat (Figure 5.3).
This indicates a small role for methotrexate in serious infection (Figure 5).
Figure 5.3 Methotrexate status in Serious infection compare to RA
215
3.2. Age and gender
In the database, there were 3569 participants (960 males and 2609 females) who completed a
relevant questionnaire 28168 times. A boxplot of the ages at first visit for the two sexes is shown
in Figure 5.4.
Figure 5.4 Boxplot showing the ages of patients at their first visit, broken down by gender
Sex1= Male Sex 2= Female
Figure 5. 5 Sex distribution in RA, Sample size: 3111
216
The mean and standard deviation of the ages of the males were 59.3 and 11.7 years respectively
and the mean and standard deviation of the ages of the females were 56.1 and 12.9 years
respectively. The differences were statistically significant (P value <0.0001). A boxplot of the ages
of the two genders appears in Figure 5.6. As can be seen in the boxplot, the age distribution was
very similar in the two sexes. However, disease occurs slightly earlier and with a wider age
dispersion among females.
Figure 5.6. Boxplot of the ages (in years) at the time of entry to the registry, broken down by the
two sexes
According to Figure 5.7, disease onset in most patients was reported between the ages of 50 and
60 years.
217
Figure 5.7 Histogram of frequency of age groups for RA
3.3. Length of time in the program
A histogram showing the number of months during which participants reported is shown in Figure
5.8. The longest report was for almost 150 months.
Sex (1= Male, 2= Female)
Figure 5.8. Boxplot of participation Time in the ARAD Program, broken down by gender
218
3.4. Time in the program as a function of Gender
The mean and standard deviation of the times in the program for males were 52.3 and 32.3 months,
respectively, and the mean and standard deviation for the duration of participation for ages of
females were 53.9 and 38.9 months, respectively. A boxplot of participation times in the program
for the two genders appears in Figure 5.8. According to this boxplot, with almost similar standard
deviation between both sexes, on average, in this study, females participated for a longer duration.
3.5. Distribution of age groups
In this section, the mean and standard deviation in respect to age for csDMARDs with bDMARDs
recipients and among three main participant groups of bDMARDs are compared.
Among three main participant groups of bDMARDs 64.5 and 14.0 for bDMARDs treated
participants, 59.1 and 17.4 for biologic 2, and finally 59.35 and 15.35 for biologic 3. A boxplot of
the age upon entry and the biologic status at the last visit appears in Figure 5.9. According to this
boxplot there are small differences in the age distribution among patients who take biologics. These
differences can potentially increase risk of type 1 error in this study.
Figure 5.9 Boxplot of distribution of ages for the main three biologic groups
219
3.6. Incidence and rate of SIs.
A cohort of 3569 RA patients (960 males and 2609 females), who had completed related
questionnaires 28176 times (during 2001-2014), were investigated for the development of SIs
associated with previously identified risk factors. After eliminating eight duplicate visit records,
the records of 28168 visits remained. The data were studied from two perspectives; the incidence
of SIs and the rate of SIs.
Visits were classified as indicating the presence of an SI or not. An investigation of the incidence
of SIs examines each individual visit and so, potentially, it looked at all 28168 visits (for the 3569
patients). However, to look at the rate of SIs per 100 patient years requires a count of the number
of SIs over a period that is not instantaneous, so records from at least two visits by a patient are
needed to measure the passage of time. As 459 patients had completed the questionnaire only once,
they were eliminated from the analysis of the rate of SIs. This left 3110 patients (2275 females and
835 males) and the records of 27709 visits. In these records, 811 visits were identified where the
patient had an SI. (Figure 5.10)
Sex1= Male Sex 2= Female
Figure 5. 10 Sex distribution in RA with serious infection
220
3.7. Incidence of SIs
According to the registries from other countries, such as Britain, the rate of SIs in bDMARDs
recipients is highest in the first year. Therefore, in ARAD we are separating the first year’s visit to
assess bDMARDs in the first year.
There were 9087 visits, overall. Each visit was classified based on the presence of SI and
descriptions for that infection including infected organ, type of DMARD and statistical significance
of the difference. The following table shows the various combinations of SI/no SI with ever/never
having taken bDMARDs (Table 5.1).
Table 5.1 Relationship between bDMARDs and Serious infections
Biologics
Yes No Total
Yes 191 98 289
No 6308 2490 8798
Total 6499 2588 9087
A Pearson’s chi-squared test of independence was performed to assess the data. The null
hypothesis that the two variables are independent is rejected at the 5% level of significance (X-
squared = 4.0495, df = 1, p-value = 0.04418). There is slight evidence of an association between
whether a visit is classified as “SI” and whether the patient has ever had a biologic.
In addition, the statistics for taking bDMARDs based on the gender of the patient has been
calculated in table 5.2. Based on the large Chi square result and insignificant p-value (0.38) at level
of 0.05, the frequency of taking bDMARDs is similar in between male and female sex (Table 5.2).
Table 5.2 Biologic status of the patients in the study
Pearson's Chi-squared test data: X-squared = 1.9067, df = 2, p-value = 0.3854
Biologic status Never Taken
or Don't Know
Currently
Using
Stopped
taking
Total
Number and % of
patients
521
(16.75%)
2160
(69.45%)
429
(13.8%)
3110
Gender
Male 151 565 119 835
Female 370 1595 310 2275
Serious Infection
221
3.7.1. Rates of serious infections
When considering rates of SI, at least two visits by a patient are required; among the 3569 patients,
459 patients were eliminated because they had completed a questionnaire only once. We were
therefore left with 3110 patients (2275 females and 835 males). Amongst these visits, 811 RA
patients with serious infections were identified. The SI patients did not differ appreciably from the
overall group with regard to gender or distribution of ages at the first visit. In the table 5.5 the status
of bDMARDs treatment and status of Sis has been demonstrated. In order to calculate 100 patient
year, the number of patients was divided by duration in years multiplied by 100. The reason for
using patient 100 year is to provide more accurate comparisons among groups when follow-up time
(i.e., patient exposure time) is not the same in all groups (Table 5.3).
Table 5.3 Numbers of SIs, total elapsed time (in months) between first and final visits for the
patients, and the corresponding rate of SIs per 100 patient-years
The risk of SI in ARAD is 26 % or 811 SI out of 3110 patients with RA. In the following table
we review some of the differences between these two populations (Table 5.4).
bDMARDs status Never Taken or
Don't Know
Currently Using Stopped taking
No. of SIs 89 585 137
Total time (in months) 27600 115764 22821
Rate of SIs/100 patient years 3.870 6.06 7.20
222
Table 5.4 Demographic characteristics of participants who self-reported an SI and participants who did not (Data collected from ARAD)
Variable Mean SD Median
Age in non-SI 61.48 12.31 63.00
Age in SI reporters 59.73 12.22 61.00
Number of Cigarettes / D in non-SI 14.89 13.23 15.00
Number of Cigarettes /D in SI
reporters
19.20 15.63 15.00
Duration of Smoking in non-SI 17.26 13.95 16.00
Duration of Smoking in SI reporters 21.41 12.28 20.00
ALCOHOL CONSUMPTION >2 or <2 in non-SI
1.32 0.47 1.00
ALCOHOL CONSUMPTION >2 or <2 in SI reporters
0.66 0.47 1.00
3.7.2. Predictor variables
The predictor variables considered possibly to influence the rate of SIs per patient were age (in
years) at first visit, gender, alcohol (ever taken/never taken), bDMARDs use (ever/never taken any
of Anakinra, Etanercept, Adalimumab, Infliximab, Certolizumab, Golimumab, Rituximab,
Abatacept or Tocilizumab), prednisolone (ever/never taken), diabetes (ever/never had non-insulin-
dependent diabetes mellitus (T2DM) or insulin dependent diabetes mellitus (T1DM)), lung disease
(ever/never suffered), kidney disease (ever/never), liver disease (ever/never), heart attack
(ever/never), coronary artery bypass grafting (CABG) (ever/never), and stenting status
(ever/never).
One patient’s records had to be removed from this analysis, as there were contradictory answers to
alcohol status over her various visits. This left 3109 patients (2274 females and 835 males).
The overall SI rate among these 3109 patients was 5.8597 SIs per 100 patient years (PYs). For
those who had ever taken a bDMARDs, the rate was 6.2610 SIs per 100 years. For those who had
never taken bDMARDs, the rate was 3.8386 SIs per 100 PYs.
223
It is reasonable to assume that the number of SIs experienced by a patient in the time of observation
might follow a Poisson distribution, with the rate of incidence varying from patient to patient as a
function of the potential risk factors described in the previous paragraph. A Generalized Linear
Model with a logarithmic link was used to model the number of SIs in terms of total time observed
and the predictor variables listed in the previous paragraph. The number of SIs per patient was
found to be slightly less variable than would be expected of data from a Poisson distribution, so
appropriate adjustments were made to the analysis when testing for significance of terms in the
model. The variable (ever had CABG) was found not to have a significant effect on the number of
SIs, either alone or in conjunction with other variables, and was deleted from the model.
The following model was selected as providing the best fit to the data:
log(rate) = 0.5562 + 0.003984 × (age at first visit) – 0.9394 (if male) + 0.6160 (if ever taken a
biologic) + 0.3044 (if ever taken prednisolone) + 0.08837 (if ever had diabetes) + 0.2388 (if ever
had lung disease) + 0.2926 (if ever had liver disease) + 0.5205 (if ever had heart attack) + 0.4225
(if ever had stenting) + 0.01517 × (age at first visit) (if male) + 0.2727 (if male and had ever had
lung disease) – 0.5768 (if male, and had ever had liver disease) – 0.4945 (if ever taken biologic
and ever had heart attack) + 0.5872 (if ever taken a biologic and ever had heart attack) + 0.5872 (if
ever had diabetes and ever had a heart attack) – 1.0865 (if ever had diabetes and ever had stenting).
(eq. 1)
The equation is an expression for the natural logarithm of the rate of SI per 100 PYs. It can be
converted to an expression for the rate by taking antilogarithms[9]:
Rate = e0.5562 × e0.003984 × (age at first visit) × e–0.9394 (if male) × e0.6160 (if ever taken a biologic) × e0.3044
(if ever taken prednisolone) × e0.08837 (if ever had diabetes) × e0.2388 (if ever had lung disease) ×
e0.2926 (if ever had liver disease) × e0.5205 (if ever had heart attack) × e0.4225 (if ever had stenting) ×
e0.01517 × (age at first visit) (if male) × e0.2727 (if male and had ever had lung disease) × e–0.5768 (if male,
and had ever had liver disease) × e–0.4945 (if ever taken biologic and ever had heart attack) × e0.5872
(if ever had diabetes and ever had a heart attack) × e–1.0865 (if ever had diabetes and ever had
stenting). (eq. 2)
Note that, except for the first two terms on the right-hand side, all other terms on the right-hand
side appear only if one or two conditions are satisfied (simultaneously). For example, “-0.9394 (if
male)” means that 0.9394 is subtracted from the right-hand side if the patient is male; if the patient
is female, nothing is done. The expression “+ 0.2727 (if male and had ever had lung disease)”
224
means that 0.2727 is added to the right-hand side if the patient is male and had ever had lung
disease; if the patient is female or had never had lung disease, nothing is done.
In order to predict the rate of serious infection in each patient we need to have an estimation of the
risk factors for serious infection. In the following table we summarize the connection between
different risk factors (predictors) and biologic medication.
3.8. Prediction of Serious infection
A generalised linear model (GLM) was utilised to calculate the frequency of SI based on the
estimated impact from each risk factor. For visits up to 12 months, there were 9087 visits overall.
The following equation was used in the model:
In (P/1-P) = 〆+B1X1+B2X2+B3X3+B4X4 where P stands for prevalence.
log(p/(1-p)) = - 4.293 – 0.042 × initial age - 0.358 (if patient is male) – 3.284 (if patient has ever
taken a biologic) + 0.263 (if patient drinks alcohol every day) + 2.626 (if patient has ever taken
prednisone) – 0.909 (if patient has ever had diabetes) + 4.877 (if patient has ever had lung disease)
+ 9.341 (if patient has ever had kidney disease) + 2.885 (if patient has ever had liver disease) –
6.317 (if patient has ever had heart attack) – 2.673 (if patient has ever had angioplasty) + 0.039 ×
initial age (if patient has ever had a bDMARDs) – 0.089 × initial age (if patient has ever had lung
disease) – 0.156 × initial age (if patient has ever had kidney disease) + 0.118 × initial age (if patient
has ever had a heart attack) + 2.083 (if patient is male and has ever had lung disease) + 2.091 (if
patient is male and has ever had kidney disease) + 3.465 (if patient is male and has ever had a heart
attack) – 3.075 (if patient is male and has ever had angioplasty) – 1.494 (if patient has ever had a
biologic and has ever had prednisone) – 0.932 (if patient has ever had a biologic and has ever had
lung disease) + 1.076 (if patient has ever had a biologic and has ever had kidney disease) + 2.641
(if patient has ever had a biologic and has ever had liver disease) – 4.076 (if patient has ever had a
biologic and has ever had a heart attack) + 6.305 (if patient has ever had a bDMARD and has ever
had angioplasty) + 1.368 (if patient drinks alcohol every day and has ever had lung disease) – 1.542
(if patient has ever had prednisone and has ever had kidney disease) – 5.710 (if patient has ever
had prednisone and has ever had liver disease) – 4.055 (if patient has ever had diabetes and has
ever had a heart attack) + 1.605 (if patient has ever had lung disease and has ever had kidney
disease) – 5.113 (if patient has ever had lung disease and has ever had a heart attack) + 5.009 (if
patient has ever had liver disease and has ever had a heart attack).
225
The model does not contain anything involving “if the patient has ever had a graft”, because no
term involving this predictor variable was found to be statistically significant. For example, a male
patient who was 62 years on his first visit, drinks alcohol every day, has taken a bDMARD but has
never taken prednisolone, has had lung disease but none of the other diseases considered and has
had a heart attack, but has not had angioplasty.
Then we have:
log(p/(1-p)) = - 4.293 – 0.042 × 62 - 0.358 – 3.284 + 0.263 + 4.877 – 6.317 + 0.039 × 62 – 0.089
× 62 + 0.118 × 62 + 2.083 + 3.465 – 0.932 – 4.076 + 1.368 – 5.113 = -10.705,
and so, odds = p/(1-p) = e-10.705 = 2.24 × 10-5. P=e/1+e= 2.24 × 10-5/1+2.24 × 10-5
Here is a table that shows the actual SI status on each visit, and the predicted status (odds less than
1 implies “Not SI”, odds greater than 1 implies “SI”).
Predicted
Yes No
SI Yes 3 286 289
No 14 8784 8798
17 9070 9087
The model was very reliable in predicting a “non-SI” when the patient did not have an SI but was
virtually useless in predicting an SI when the patient had an SI.
This is a plot of the predicted probability that a visit will be an “SI” vs the actual result of the visit.
Probabilities less than 0.5 correspond to odds of less than 1, while probabilities greater than 1
correspond to odds of more than 1. We can see that the probabilities cover a very wide range even
though we would like them to be very close to 0 or 1 (for “non-SI and “SI” respectively). For visits
after 12 months, the following table of SI/not SI vs biologic ever/never can be constructed:
bDMARD
SI yes no
yes 480 69 549
no 15105 3426 18531
15585 3495 19080
226
There is a significant association between SI and bDMARD use (X-squared = 12.095, df = 1, p-
value = 0.0005055). Of the people who have ever taken a bDMARD, the proportion whose visit is
associated with an SI is 480/15585 = 3.08%, whereas, of the people who have never taken a
biologic, the proportion whose visit is associated with an SI is 69/3495 = 1.83%. There is a greater
proportion of visits associated with an SI amongst those who have ever taken a biologic than
amongst those who have never taken a biologic.
When the full statistical analysis involving all predictor variables was performed, the best model
was found to be log(p/(1-p)) = - 7.968 + 0.025 × initial age - 2.043 (if patient is male) + 0.579 (if
patient has ever taken a biologic) – 1.062 (if patient drinks alcohol every day) + 0.719 (if patient
has ever taken prednisone) – 2.747 (if patient has ever had diabetes) + 0.978 (if patient has ever
had lung disease) + 1.772 (if patient has ever had kidney disease) - 0.258 (if patient has ever had
liver disease) + 1.711 (if patient has ever had a heart attack) – 0.813 (if patient has ever had a graft)
+ 3.692 (if patient has ever had angioplasty) + 0.033 × initial age (if patient is male) + 0.043 ×
initial age (if patient has ever had diabetes) – 0.060 × initial age (if patient has ever had angioplasty)
– 1.987 (if patient is male and has ever had kidney disease) -2.653 (if patient is male and has ever
had a graft) + 0.893 (if patient is male and has ever had angioplasty) + 1.057 (if patient has ever
had a biologic and drinks alcohol every day) + 1.004 (if patient has ever had a biologic and has
ever had diabetes) - 1.850 (if patient has ever had a biologic and has ever had a heart attack) –
0.651 (if patient drinks alcohol every day and has ever had lung disease) – 1.432 (if patient drinks
alcohol every day and has ever had kidney disease) + 1.657 (if patient drinks alcohol every day and
has ever had liver disease) + 0.937 (if patient drinks alcohol every day and has ever had a heart
attack) + 2.691 (if patient drinks alcohol every day and has ever had a graft) – 1.383 (if patient
drinks alcohol every day and has ever had angioplasty) + 3.464 (if patient has ever had prednisone
and has ever had a graft) – 1.154 (if patient has ever had diabetes and has ever had lung disease) +
1.299 (if patient has ever had diabetes and has ever had liver disease) – 0.880 (if patient has ever
had diabetes and has ever had a heart attack) + 0.695 (if patient has ever had lung disease and has
ever had a heart attack) - 1.806 (if patient has ever had lung disease and has ever had a graft) +
0.983 (if patient has ever had a heart attack and has ever had angioplasty).
227
The following is the table of predictions made by the model for each visit:
Predicted
Yes No
SI Yes 1 548 549
No 2 18529 18531
Total
3 19077 19080
The model predicted virtually every visit to be a “non-SI”, whether an SI was present.
Fig. 5.12 displays a plot of predicted probabilities for a given visit being an “SI” vs the actual
result of the visit. It may seem that the models for predicting that a visit is associated with an SI,
due to small amount of available data are not of much value, but as SI is potentially fatal and has
dangerous consequences, it is worthwhile to try and predict it.
4. Discussion
The model for those 2966 patients who had at least two visits in the first 12 months, for the rate (in
SIs per 100 PYs) is:
log(rate) = 0.1583 + 0.0244 × initial age - 0.5759 (if patient is male) + 0.2543 (if has ever taken a
biologic) + (1.8543 - 0.02356 × initial age) (if has ever taken prednisone) + 0.7903 (if male and
has ever taken a biologic).
We could take antilog and rewrite this equation as:
rate = e 0.1583 × e 0.0244 × initial age × e- 0.5759 (if patient is male) × e 0.2543 (if has ever taken a biologic)
× e (1.8543 - 0.02356 × initial age) (if has ever taken prednisone) + e 0.7903 (if male and has ever taken a
biologic)
In this model, the rate increases with increasing age with or without taking prednisolone, it
increases if the patient has ever taken a bDMARD, and it increases further if the patient is a male
who has ever taken a biologic (Figure 5.11).
228
Figure 5. 11 A plot of the number of SIs predicted by the model for the first 12 months against
the observed (actual) number of SIs
Guide Table for figure 5.11 Frequency of predicted serious infections
No. of SIs 0 1 2 3 Total Frequency 2726 225 14 1 2966
In figure 5.12 the highest individual predicted number is about 0.14, whereas one person had three
SIs. (That patient was female, aged 70 at the initial visit, and had taken biologics and prednisolone
(Figure 5.12).
Figure 5.12 Rates of SIs per 100 patient years vs age at initial visit (in years) for males and females based on infections in the first 12 months after the initial visit
229
The effects of bDMARDs on serious infections may vary according to duration of bDMARD use.
Therefore, predicted risk has been studied in the first year and then also after the first year. In
Figure 5.13, the plot of the predicted number of SIs for the patients versus the observed numbers
of SIs, after the first year of exposure to bDMARD medication has been demonstrated (Figure
5.13).
Figure 5.13 A plot of the number of SIs predicted by the model for more than 12 months exposure
against the observed (actual) number of SIs.
Guide Table for figure 5.13 - Frequency of predicted serious infections.
No. of SIs 0 1 2 3 4 5 Total
Frequency 2283 346 74 14 2 1 2720
As can be seen in figure 5.14, the shape of the graph for the frequency of serious infection in
females rises to only a modest extent with age, whereas in males it tends to increase sharply with
age (Figure 5.13).
230
Figure 5.14 Rates of SIs per 100 patient years vs age at initial visit (in years) for males and females based
on infections more than 12 months after the initial visit
These rates of SIs during the first year and then after the first-year show that in the first 12 months
of treatment with bDMARDs, the rates are about twice (10-12 per 100PYs) those observed for the
whole period of exposure (approximately 6 per 100PYs). From those 2720 patients who had at
least one visit after the first 12 months, the rate (in SIs per 100 patient years) is:
log(rate) = 0.3939 + 0.0075 × initial age + (-1.2990 + 0.0228 × initial age) (if patient is male) +
0.4017 (if has ever taken a bDMARD) + 0.4619 (if has ever taken prednisone).
If we take antilog, we can rewrite this equation as:
rate = e 0.3939 × e 0.0075 × initial age × e (-1.2990 + 0.0228 × initial age) (if patient is male) × e 0.4017 (if has ever
taken a bDMARD) × e 0.4619 (if has ever taken prednisolone),
5. Chapter conclusion
The risk of serious infections among patients in ARAD during 2001 to 2014 is about 3%. In this
chapter the level of impact from different biologics as potential risk factors for serious infection
was studied. Data suggest that the rates of SI increases with increasing age with or without taking
prednisolone, it increases if the patient has ever taken a biologic, and it increases further if the
patient is a male who has ever taken a biologic. However, medication is not the only risk for SI
and other risks, such as having chronic diseases or other factors which compromise immune
system, can play a potential role and change the results, as well.
231
In compare to other registries, the rate of SI in Australia is higher. For instance, in south
American countries according to a study which published in Aug. 2019 almost 2591 out of 13380
patient/years were taking bDMARDs and 1126 treated with csDMARDs. The SI IR was 30.54
(CI 27.18-34.30) for all bDMARDs and 5.15 (CI 3.36-7.89) for csDMARDs. In this study the
aIRR between the two groups was 2.03 ([1.05, 3.9] p = 0.034) for the first 6 months of treatment
but subsequently increased to 8.26 ([4.32, 15.76] p < 0.001). The SI IR for bDMARDs decreased
over time in both registries, dropping from 36.59 (28.41-47.12) in 2012 to 7.27 (4.79-11.05) in
2016.[10]
In another study from British Society for Rheumatology Biologics Register - Rheumatoid Arthritis
in total, 5289 subjects 19 431 patient-years had at least one SI. The baseline annual rate of first SI
was 4.6% (95% CI: 4.5, 4.7), increasing to 14.1% (95% CI: 13.5, 14.8) following an index
infection. Respiratory infections were the most frequent (44% of all events). Recurrent infections
mirrored the organ class of the index infection. Sepsis, increasing age and polypharmacy were
significant predictors of infection recurrence in a fully adjusted model. The system class of index
infection was associated with the risk of a recurrent event; subjects who experienced sepsis had the
highest risk of subsequent SI within 12 months, 19.7% (95% CI: 15.1, 25.7). [11]
Finally, in a study from five different registries (USA, Sweden, UK, Japan, and CORRONA
International (multiple countries)) from 2000 to 2017 the results showed that age/sex-standardised
rates of hospitalised infection were quite consistent across registries (range 1.14-1.62 per 100
patient-years). Higher and more consistent rates were observed when adding standardisation for
HAQ score (registry range 1.86-2.18, trials rate 2.92) or restricting to a treatment initiation sub
cohort followed for 18 months (registry range 0.99-2.84, trials rate 2.74).[12]
232
THESIS SUMMARY AND REMARKS
233
Summary of main findings
In this model, the rate of SIs increases with increasing age, but at a greater rate (from a smaller
start) if the patient is male. It also increases if the patient has ever taken a bDMARD and increases
if the patient has ever taken prednisolone. Figure 5.12 shows a plot of the predicted individual rate
of SIs versus the observed individual rate of SIs. Once again, we have a very large number of
individuals who had no SIs is observed, so the model tends to predict a low rate[13].
A Generalized Linear Mixed Model was applied to the data from the 28168 visits by the 3569
patients in the study. A model was constructed which expressed the natural logarithm of the odds
for SI in terms of x predictor variables. The number of SIs is greatest for Etanercept, Adalimumab
and Abatacept respectively, but when the rate per 100 PYs is considered, this order changes to
Adalimumab, Etanercept and Anakinra[14].
It may mean that Etanercept is potentially more dangerous than Etanercept, but in practice the
dosage of medicine also plays a role. If a bDMARDs is prescribed for a shorter time at a lower
dosage, it puts the patient potentially at lower risk and is safer. However, even one SI report in this
situation is statistically more significant than the same report for another medicine with a longer
duration and higher dosage. Also, doctors’ preferences in prescribing one medication and the
selection of patients for these biologics all can contribute to the results.
In addition, for Anakinra there was only a limited number of patients available and this can reduce
the reliability of the results[15]. For Anakinra, there was only a small number of patients exposed
to the drug (n= ZZ), which calls into question the validity of the findings for this agent. Finally,
medication is not the only risk for SI in most of the reports and other risks such as having chronic
diseases or other factors which compromise immune system can play a potential role and change
the results (Table 5.5)[16]. Around 83% of the patients have received a biologic. There are more
women than men in each biologic status category, and the ratios of women to men are not
significantly different across the three categories (Table 5.5)[16].
The results for alcohol consumption are likely relevant. Patients who were using a bDMARD
treatment tended to consume alcohol more often (Table 5.5, P = QQ). Possible reasons for this
observation include the use of alcohol to combat pain in those with more active disease, who are
more likely to progress to bDMARDs.
234
A further possibility is that disease activity may be greater in alcohol users and so they may
progress to bDMARDs more quickly or more often. Liver enzyme induction in higher consumers
of alcohol may diminish responsiveness to csDMARDs and lead to more frequent progression to
bDMARDs. Germane to this possible explanation is the tendency for anti-TB drugs, such as
Rifampicin to induce liver enzymes and reduce responsiveness to corticosteroids, which in turn can
lead to more active RA in patients who were previously stable[17].
There is also a statistically significant association between taking bDMARDs and taking
prednisolone. Most of the patients who were taking bDMARDs were also taking prednisolone
concurrently or had taken prednisolone previously. This is most likely explained on the basis of
rheumatoid disease severity. RA patients not well controlled on multiple or sequential csDMARDs
are more likely to have received adjunctive corticosteroids prior to qualifying for a bDMARD.
Furthermore, depending on the clinical response to the bDMARD, they may or may not have been
able to discontinue prednisolone. In any event, since this parameter includes previous usage, the
associated use would have been captured[17].
Increased disease severity in RA is a risk factor for SIs but this risk cannot be easily disentangled
from other risk factors. In other conditions, such as insulin dependent diabetes mellitus (T1DM)
and non-insulin dependent diabetes mellitus (T2DM), lung disease and in patients with previous
stent operations for coronary heart disease, the frequency with which bDMARDs were used is
lower (Table 5.5). Any association between bDMARD status and the other factors is not
statistically significant. One possible reason for this lack of association is the relatively small
numbers of participants with these conditions in this RA cohort. However, the possibility of
confounding by indication also exists, since prescribers may have avoided bDMARD usage in
certain patients with concerning comorbidities. For example, the known propensity for diabetics to
develop infections might have led to treating Rheumatologists exercising restraint in respect to
prescribing bDMARDs in the context of T2DM and T1DM[17].
Page 235 of 577
Table 5. 5- Relationship between bDMARD use and potential cofactors
Predictor Variables bDMARDs Never taken Taken at some time Total Alcohol
Never/Don’t know 135 785 920 current 297 1501 1789 past 89 303 392 total 521 2589 3110
Prednisolone
Never/Don’t know 180 308 488 current 242 99 521 past 1766 515 2589 total 2008 614 3110
T1DM
Never/Don’t know 494 2431 2925 current 26 148 174 past 1 10 11 total 521 2589 3110
T2DM
Never/Don’t know 475 2290 2765 current 41 256 299 past 46 299 46 total 521 2589 3110
Lung Disease
Never/Don’t know 373 1792 2165 current 112 536 648 past 36 261 2589 total 521 2589 3110
Kidney Disease
Never/Don’t know 477 2365 2842 current 21 111 132 past 23 113 136 total 521 2589 3110
Liver Disease Never/Don’t know 497 2386 2883 current 9 93 102 past 15 110 125 total 521 2589 3110
MI Never/Don’t know 464 2394 2858 current 16 56 72 past 41 139 180 total 521 2589 3110
CABG Never/Don’t know 499 2528 3027 current 8 14 22 past 14 47 61 total 521 2589 3110
C Stenting Never/Don’t know 480 2448 2928 current 20 69 89 past 21 72 93 total 3110
Pearson's Chi-squared test X-squared = 96.663, df = 2, p-value < 2.2e-16X-squared = 173.82, df = 2, p-value < 2.2e-16 A
large number of people, who are taking biologics, are simultaneously taking prednisolone Insulin dependent Diabetes
(T1DM) X-squared = 0.91075, df = 2, p-value = 0.6342None Insulin dependent Diabetes (T2DM) X-squared = 5.0186, df =
2, p-value = 0.08132Lung Disease X-squared = 5.051, df = 2, p-value = 0.08002 Kidney Disease X-squared = 0.071819, df =
2, p-value = 0.9647
Liver disease X-squared = 7.1119, df = 2, p-value = 0.02855
MI: Myocardial infarction X-squared = 6.7789, df = 2, p-value = 0.03373
Coronary artery bypass grafting (CABG) X-squared = 7.9029, df = 2, p-value = 0.01923
Page 236 of 577
Coronary stenting; X-squared = 4.6234, df = 2, p-value = 0.0
Concluding remarks
In the descriptive statistics, there is always a possibility that people who contribute to the
research are not randomly selected or one sex contributes more than the other sex in answering
the questions. In some studies, the number of patients who contribute to the study also may
cause limitations. However, in this project, there was access to an adequate number of
participants. Furthermore, there were no selection criteria applied by ARAD designers that
might have led to preferential selection of some patients instead of others. Still, criteria such as
the contribution of one gender more than the other or the ability of patients to answer
questionnaires may have contributed to some bias[11].
RA in Australia is predominantly a female disease. The mean and median age for male patients
is greater than the corresponding ages for female patients, which means that RA in females
tends to begin at a younger age than in males. The prevalence of RA peaks in the 60s or seventh
decade of life. The relevance of RA among those in the population younger than 60 is greater
than in the population over 60. Many patients are diagnosed with RA when they are in their
50s[11].
Most of the patients with RA who participated in the ARA database were already taking or
were about to begin bDMARDs, however, this is because the database was conceived as a
bDMARD registry and non-bDMARD recipients were recruited later to supplement the cohort.
The prevalence of serious infection in ARAD participants was 2.92 %. Rates were appreciably
higher in the first year of treatment at around 12 per 100PYs in this study. Similar increased
rates in the first year have been observed in other registries. Thereafter, rates were about half
that in the first year at approximately 6 per 100PYs, which again accords with that reported in
other registries. Males had higher rates of SI and this increased sharply with age, whereas in
females there was a modest, but steady increase with age and even in advanced age, the rates
in females did not approach those in males over 65 years. The major risk factors contributing
to high rates of SI were advancing age, use of bDMARDs and use of Prednisolone.
Comorbidities were not found to be major contributors to SIs[17].
As indicated previously, it is difficult to determine the rate of SIs in RA, since it is a function
of disease severity as well as other factors. Within ARAD at least, the csDMARDs group was
recruited post-hoc and likely consists of patients unmatched for disease activity, since they did
Page 237 of 577
not require bDMARDs. Moreover, there may have been different rates of corticosteroid use
in csDMARDs users. Nevertheless, serious infections in this group were in the order of 3-4 per
100PYs, which is clearly lower than that in the first year of bDMARD therapy and lower than
that in long term bDMARD recipients. These differences cannot be taken as proof of a
substantial difference, but they are consistent with an increased propensity for SIs in bDMARD
users[12].
Strengths of this analysis include the large size of the database and the randomness or lack of
bias in recruitment, the opportunity to assess participants over a relatively long period of time
(2001-2014) and the capture of events that might have been overlooked if reporting were not
done by the participants, but rather by busy clinicians, whose reporting compliance may have
been suboptimal.
Limitations include the unmatched nature of csDMARDs and bDMARD participants, thus
confounding valid comparisons, the inability to verify self-reported infections of any severity
(no input from family practitioners, no hospital records available, no microbiological
confirmation of infections), but particularly SIs and the inability to capture SIs that resulted in
death or severe disability that precluded further reporting.
Future studies both within and without ARAD have the potential to verify the findings reported
here and to extend them. For example, the extent to which SIs increase in participants who
transition from csDMARDs to bDMARDs within ARAD could be compared as there will have
been an adequate period of observation during the pre-bDMARD era in these patients. Such a
study would have the added benefit that the participants could be their own control, which in
turn would provide greater rigor. Whether SI rates decline in bDMARD recipients after 5-10
years could also be examined. Deeper analysis of newly discovered clinical risk factors might
also be possible. With linkage in time to biobanks, it may also become possible to examine the
role of genetic and acquired immunity[17].
Page 238 of 577
References:
[1] D. H et al., “Clonal V alpha 12.1+ T cell expansions in the peripheral blood of
rheumatoid arthritis patients.,” J Exp Med, vol. 177, no. 6, pp. 1623–1631, Jun. 1993,
doi: 10.1084/jem.177.6.1623.
[2] D. F. McWilliams, P. D. W. Kiely, A. Young, and D. A. Walsh, “Baseline factors
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Page 241 of 577
APPENDICES
Page 242 of 577
Appendices 241
Description of data in appendix ...................................................................................... 243
Taking different medication levels ............................................................................................................ 243 Response levels ......................................................................................................................................... 243
Appendix A: Output of SAS for EENT Infection 244
Appendix B: OUTPUT of SAS for Lung Infection 268
Appendix C: Output of SAS for Nail and skin infection 301
Appendix D: Output of SAS for artificial joint infection 328
Appendix E: Output of SAS for bone muscle joint infection 351
Appendix F: Output of SAS for blood infection 385
Appendix G: Output of SAS for GIT Infection 411
Appendix H: Output of SAS for Nervous system infection 433
Appendix I: Output of SAS for TB infection 461
Appendix J: Output of SAS for Urinary Tract Infection 485
Appendix K: Output of SAS for viral infection 509
Appendix L: Ethical approval for the thesis 535
APPENDIX M: Sample of ARAD questionnaire 536
Page 243 of 577
Description of data in appendix
Taking different medication levels
1=Never taking 2=Currently taking 3=Stopped taking 4=Don’t know
Response levels
1=Mild 2=Moderate 3=Severe 4=Missing
Page 244 of 577
APPENDIX A: OUTPUT OF SAS FOR
EENT INFECTION
Table A.1- Model information for EENT infection
Model Information
Data Set WORK.IMPORT2
Response Variable InfEent InfEent
Number of Response Levels 4
Model generalized logit
Optimization Technique Newton-Raphson
Table A.2- Observation status for EENT infection
Number of Observations Read 27711
Number of Observations Used 21506
Table A.3- response value for EENT infection
Response Profile
Ordered
Value InfEent
Total
Frequency
Mild 1 1050
Moderate 2 1829
Severe 3 406
Missing 4 18221
Logits modelled use InfEent='4' as the reference category.
Note: 6205 observations were deleted due to missing values for
the response or explanatory variables.
Page 245 of 577
Table A.4- Backward Elimination Procedure for EENT infection
Backward Elimination Procedure
Class Level Information Class Value Design Variables Etanercept 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1
Adalimumab 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1
Anakinra 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1
Infliximab 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1
Rituximab 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1
Abatacept 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1
Tocilizumab 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1
Golimumab 3 1 0 0 currently taking 0 1 0 never taking 0 0 1
Certolizumab 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1
Folic Acid currently taking 1 0 never taking 0 1
Hydroxychloroquine 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1
Sulphasalazine 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1
Arava (Leflunomide) 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1
Page 246 of 577
Class Level Information Class Value Design Variables Azathioprine 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1
Cyclosporin 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1
Prednisolone 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1
IM Gold injection 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1
Penicillamine 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1
Step 0. The following effects were entered:
Intercept Etanercept Adalimumab Anakinra Infliximab Rituximab Abatacept Tocilizumab
Golimumab Certolizumab Folic Acid Hydroxychloroquine Sulphasalazine Arava
(Leflunomide) Azathioprine Cyclosporin Prednisolone IM Gold injection Penicillamine
Table A.5- Model Convergence status for EENT infection
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Page 247 of 577
Table A.6- Model Fit statistics for EENT infection
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 24626.355 24501.128
SC 24650.284 25745.398
-2 Log L 24620.355 24189.128
Page 248 of 577
Table A.7- Testing null hypothesis for EENT infection
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio
431.2272
153
<.0001
Score
463.0664
153
<.0001
Wald
419.5882
153
<.0001
Table A.8- Model Fit statistics for removing covariant step 1
Step 1. Effect Azathioprine is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table A.9- Model Fit statistics for removing covariant step 1
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 24626.355 24488.566
SC 24650.284 25661.051
-2 Log L 24620.355 24194.566
Page 249 of 577
Table A.10- Testing Null hypothesis after removing covariant step 1
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 425.7897 144 <.0001
Score 457.8861 144 <.0001
Wald 415.1007 144 <.0001
Table A.11- Residual removing covariant step 1
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
5.0524 9 0.8297
Table A.12- Model Fit statistics for removing covariant step 2
Step 2. Effect Certolizumab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Page 250 of 577
Table A.13- Model Fit statistics after removing covariant step 2
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 24626.355 24476.658
SC 24650.284 25577.358
-2 Log L 24620.355 24200.658
Table A.14- Testing Null hypothesis after removing covariant step 2
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 419.6974 135 <.0001
Score 450.9468 135 <.0001
Wald 408.7712 135 <.0001
Table A.15- Residual removing covariant step 2
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
10.9161 18 0.8979
Page 251 of 577
Table A.16- Model Fit statistics for removing covariant step 3
Step 3. Effect Penicillamine is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table A.17- Model Fit statistics after removing covariant step 3
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 24626.35 24473.673
SC 24650.28 25502.589
-2 Log L 24620.35 24215.673
Table A.18- Testing Null hypothesis after removing covariant step 3
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 404.6821 126 <.0001
Score 440.0301 126 <.0001
Wald 401.9656 126 <.0001
Page 252 of 577
Table A.19- Residual removing covariant step 3
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
22.0077 27 0.7370
Table A.20- Model Fit statistics for removing covariant step 4
Step 4. Effect IM Gold injection is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table A.21- Model Fit statistics after removing covariant step 4
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 24626.355 24465.880
SC 24650.284 25423.011
-2 Log L 24620.355 24225.880
Page 253 of 577
Table A.22- Testing Null hypothesis after removing covariant step 4
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 394.4751 117 <.0001
Score 430.4313 117 <.0001
Wald 392.2553 117 <.0001
Table A.23- Residual removing covariant step 4
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
31.2787 36 0.6926
Table A.24- Model Fit statistics for removing covariant step 5
Step 5. Effect Rituximab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Page 254 of 577
Table A.25- Model Fit statistics after removing covariant step 5
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 24626.355 24461.305
SC 24650.284 25346.650
-2 Log L 24620.355 24239.305
Table A.26- Testing Null hypothesis after removing covariant step 5
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 381.0509 108 <.0001
Score 415.9895 108 <.0001
Wald 378.4582 108 <.0001
Table A.27- Residual removing covariant step 5
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
44.9975 45 0.4721
Page 255 of 577
Table A.28- Model Fit statistics for removing covariant step 6
Step 6. Effect Golimumab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table A.29- Model Fit statistics after removing covariant step 6
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 24626.355 24462.511
SC 24650.284 25300.000
-2 Log L 24620.355 24252.511
Table A.30- Testing Null hypothesis after removing covariant step 6
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 367.8443 102 <.0001
Score 403.4935 102 <.0001
Wald 366.8141 102 <.0001
Page 256 of 577
Table A.31- Residual removing covariant step 7
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
57.2672 51 0.2539
Table A.32- Summary of backward elimination in EENT
Note: No (additional) effects met the 0.05 significance level for removal from the model.
Summary of Backward Elimination
Step Effect Removed
DF Number In
Wald Chi-Square
Pr > ChiSq Variable Label
1 Azathioprine 9 17 4.9893 0.8352 Azathioprine
2 Certolizumab 9 16 5.4537 0.7931 Certolizumab
3 Penicillamine 9 15 7.1956 0.6168 Penicillamine
4 IM Gold injection 9 14 9.1915 0.4198 IM Gold injection
5 Rituximab 9 13 13.6536 0.1352 Rituximab
6 Golimumab 6 12 11.2165 0.0819 Golimumab
Page 257 of 577
Table A.33- Type 3 analysis of effects in EENT
Type 3 Analysis of Effects
Effect DF
Wald
Chi-Square Pr > ChiSq
Etanercept 9 52.1431 <.0001
Adalimumab 9 22.4139 0.0077
Anakinra 9 18.2690 0.0322
Infliximab 9 31.0160 0.0003
Abatacept 9 18.0153 0.0350
Tocilizumab 9 18.1032 0.0340
Folic Acid 3 9.4165 0.0242
Hydroxychloroquine 9 23.3663 0.0054
Sulphasalazine 9 26.7402 0.0015
Arava (Leflunomide) 9 17.5339 0.0410
Cyclosporin 9 47.3358 <.0001
Prednisolone 9 29.4764 0.0005
Table A.34- Analysis of maximum likelihood estimates in EENT
Analysis of Maximum Likelihood Estimates
Parameter
Eent
Infection DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Intercept Mild 1 -3.4872 0.1190 859.2759 <.0001
Intercept Mod 1 -2.9786 0.0928 1031.0501 <.0001
Intercept Severe 1 -4.3609 0.1917 517.2695 <.0001
Abatacept 3 Mild 1 0.5166 0.1769 8.5260 0.0035
Abatacept 3 Mod 1 0.1147 0.1534 0.5587 0.4548
Abatacept 3 Severe 1 -0.4339 0.3573 1.4747 0.2246
Abatacept 4 Mild 1 0.2751 0.8455 0.1059 0.7449
Abatacept 4 Mod 1 -0.6022 0.6388 0.8887 0.3458
Abatacept 4 Severe 1 1.3251 1.0615 1.5582 0.2119
Abatacept currently
taking
Mild 1 0.3362 0.1582 4.5176 0.0335
Page 258 of 577
Analysis of Maximum Likelihood Estimates
Parameter
Eent
Infection DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Abatacept currently
taking
Mod 1 0.1491 0.1240 1.4460 0.2292
Abatacept currently
taking
Severe 1 -0.2016 0.2673 0.5686 0.4508
Abatacept never taking Mild 0 0 . . .
Abatacept never taking Mod 0 0 . . .
Abatacept never taking Severe 0 0 . . .
Adalimumab 3 Mild 1 0.0104 0.0914 0.0129 0.9094
Adalimumab 3 Mod 1 0.1823 0.0686 7.0504 0.0079
Adalimumab 3 Severe 1 0.1418 0.1403 1.0222 0.3120
Adalimumab 4 Mild 1 -0.5402 0.6756 0.6394 0.4239
Adalimumab 4 Mod 1 -
0.00090
0.4440 0.0000 0.9984
Adalimumab 4 Severe 1 -
10.2462
147.6 0.0048 0.9447
Adalimumab currently
taking
Mild 1 0.2887 0.0941 9.4206 0.0021
Adalimumab currently
taking
Mod 1 0.1847 0.0737 6.2813 0.0122
Adalimumab currently
taking
Severe 1 -0.0798 0.1470 0.2946 0.5873
Adalimumab never taking Mild 0 0 . . .
Adalimumab never taking Mod 0 0 . . .
Adalimumab never taking Severe 0 0 . . .
Anakinra 3 Mild 1 0.1448 0.2523 0.3295 0.5659
Anakinra 3 Mod 1 -0.0761 0.2191 0.1205 0.7285
Anakinra 3 Severe 1 0.4597 0.3413 1.8146 0.1780
Anakinra 4 Mild 1 -0.7187 0.6297 1.3026 0.2537
Anakinra 4 Mod 1 0.0275 0.4047 0.0046 0.9459
Anakinra 4 Severe 1 -0.4484 1.0513 0.1819 0.6697
Page 259 of 577
Analysis of Maximum Likelihood Estimates
Parameter
Eent
Infection DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Anakinra currently
taking
Mild 1 -
11.9697
512.1 0.0005 0.9814
Anakinra currently
taking
Mod 1 1.7999 0.4745 14.3901 0.0001
Anakinra currently
taking
Severe 1 -
12.2422
809.5 0.0002 0.9879
Anakinra never taking Mild 0 0 . . .
Anakinra never taking Mod 0 0 . . .
Anakinra never taking Severe 0 0 . . .
Arava (Leflunomide) 3 Mild 1 0.1098 0.0935 1.3804 0.2400
Arava (Leflunomide) 3 Mod 1 0.1933 0.0729 7.0343 0.0080
Arava (Leflunomide) 3 Severe 1 0.1484 0.1434 1.0712 0.3007
Arava (Leflunomide) 4 Mild 1 -0.2856 0.5428 0.2768 0.5988
Arava (Leflunomide) 4 Mod 1 0.4582 0.3091 2.1967 0.1383
Arava (Leflunomide) 4 Severe 1 -0.7134 1.0581 0.4546 0.5002
Arava (Leflunomide) currently
taking
Mild 1 0.2705 0.1060 6.5075 0.0107
Arava (Leflunomide) currently
taking
Mod 1 0.1492 0.0858 3.0250 0.0820
Arava (Leflunomide) currently
taking
Severe 1 0.00639 0.1726 0.0014 0.9705
Arava (Leflunomide) never taking Mild 0 0 . . .
Arava (Leflunomide) never taking Mod 0 0 . . .
Arava (Leflunomide) never taking Severe 0 0 . . .
Cyclosporin 3 Mild 1 0.0263 0.0937 0.0789 0.7788
Cyclosporin 3 Mod 1 0.2042 0.0692 8.7084 0.0032
Cyclosporin 3 Severe 1 0.4662 0.1325 12.3789 0.0004
Cyclosporin 4 Mild 1 -0.2418 0.3214 0.5662 0.4518
Cyclosporin 4 Mod 1 0.0673 0.2205 0.0931 0.7603
Cyclosporin 4 Severe 1 -1.0526 0.7303 2.0770 0.1495
Page 260 of 577
Analysis of Maximum Likelihood Estimates
Parameter
Eent
Infection DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Cyclosporin currently
taking
Mild 1 0.5290 0.3373 2.4603 0.1168
Cyclosporin currently
taking
Mod 1 1.0439 0.2236 21.7983 <.0001
Cyclosporin currently
taking
Severe 1 1.0216 0.4398 5.3965 0.0202
Cyclosporin never taking Mild 0 0 . . .
Cyclosporin never taking Mod 0 0 . . .
Cyclosporin never taking Severe 0 0 . . .
Etanercept 3 Mild 1 -0.0509 0.0911 0.3118 0.5766
Etanercept 3 Mod 1 -0.0713 0.0705 1.0220 0.3120
Etanercept 3 Severe 1 -0.3981 0.1457 7.4653 0.0063
Etanercept 4 Mild 1 1.3033 0.5444 5.7307 0.0167
Etanercept 4 Mod 1 1.9227 0.3431 31.3968 <.0001
Etanercept 4 Severe 1 1.3439 0.8633 2.4234 0.1195
Etanercept currently
taking
Mild 1 0.1730 0.0941 3.3831 0.0659
Etanercept currently
taking
Mod 1 0.0891 0.0722 1.5232 0.2171
Etanercept currently
taking
Severe 1 -0.3383 0.1446 5.4736 0.0193
Etanercept never taking Mild 0 0 . . .
Etanercept never taking Mod 0 0 . . .
Etanercept never taking Severe 0 0 . . .
Folic Acid and
Methotrexate
currently
taking
Mild 1 -0.1059 0.0761 1.9365 0.1641
Folic Acid and
Methotrexate
currently
taking
Mod 1 -0.1683 0.0598 7.9220 0.0049
Folic Acid and
Methotrexate
currently
taking
Severe 1 -0.0493 0.1190 0.1713 0.6789
Page 261 of 577
Analysis of Maximum Likelihood Estimates
Parameter
Eent
Infection DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Folic Acid and
Methotrexate
never taking Mild 0 0 . . .
Folic Acid and
Methotrexate
never taking Mod 0 0 . . .
Folic Acid and
Methotrexate
never taking Severe 0 0 . . .
Hydroxychloroquine 3 Mild 1 0.1431 0.0736 3.7794 0.0519
Hydroxychloroquine 3 Mod 1 0.2299 0.0575 15.9873 <.0001
Hydroxychloroquine 3 Severe 1 0.1860 0.1175 2.5057 0.1134
Hydroxychloroquine 4 Mild 1 -0.0695 0.4165 0.0278 0.8676
Hydroxychloroquine 4 Mod 1 -0.0273 0.3338 0.0067 0.9348
Hydroxychloroquine 4 Severe 1 0.6305 0.5074 1.5444 0.2140
Hydroxychloroquine currently
taking
Mild 1 0.0100 0.0960 0.0109 0.9168
Hydroxychloroquine currently
taking
Mod 1 0.0789 0.0753 1.0991 0.2945
Hydroxychloroquine currently
taking
Severe 1 0.0332 0.1546 0.0463 0.8297
Hydroxychloroquine never taking Mild 0 0 . . .
Hydroxychloroquine never taking Mod 0 0 . . .
Hydroxychloroquine never taking Severe 0 0 . . .
Infliximab 3 Mild 1 0.0552 0.1337 0.1707 0.6795
Infliximab 3 Mod 1 -0.2055 0.1098 3.5047 0.0612
Infliximab 3 Severe 1 0.0478 0.1974 0.0585 0.8088
Infliximab 4 Mild 1 0.4422 0.4291 1.0621 0.3027
Infliximab 4 Mod 1 -0.1974 0.3745 0.2779 0.5981
Infliximab 4 Severe 1 -0.8062 0.8952 0.8110 0.3678
Infliximab currently
taking
Mild 1 0.6440 0.1747 13.5909 0.0002
Infliximab currently
taking
Mod 1 0.4727 0.1396 11.4614 0.0007
Page 262 of 577
Analysis of Maximum Likelihood Estimates
Parameter
Eent
Infection DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Infliximab currently
taking
Severe 1 -0.3706 0.3711 0.9971 0.3180
Infliximab never taking Mild 0 0 . . .
Infliximab never taking Mod 0 0 . . .
Infliximab never taking Severe 0 0 . . .
Prednisolone 3 Mild 1 0.3310 0.1083 9.3359 0.0022
Prednisolone 3 Mod 1 0.2552 0.0838 9.2693 0.0023
Prednisolone 3 Severe 1 0.4980 0.1834 7.3738 0.0066
Prednisolone 4 Mild 1 0.7838 0.5610 1.9520 0.1624
Prednisolone 4 Mod 1 0.5466 0.4327 1.5961 0.2065
Prednisolone 4 Severe 1 0.7162 1.0389 0.4753 0.4906
Prednisolone currently
taking
Mild 1 0.1671 0.1087 2.3642 0.1241
Prednisolone currently
taking
Mod 1 0.1308 0.0838 2.4345 0.1187
Prednisolone currently
taking
Severe 1 0.3911 0.1833 4.5509 0.0329
Prednisolone never taking Mild 0 0 . . .
Prednisolone never taking Mod 0 0 . . .
Prednisolone never taking Severe 0 0 . . .
Sulphasalazine 3 Mild 1 0.0933 0.0714 1.7093 0.1911
Sulphasalazine 3 Mod 1 0.2229 0.0554 16.1883 <.0001
Sulphasalazine 3 Severe 1 0.1577 0.1136 1.9284 0.1649
Sulphasalazine 4 Mild 1 0.1273 0.3002 0.1799 0.6714
Sulphasalazine 4 Mod 1 -0.2181 0.2582 0.7132 0.3984
Sulphasalazine 4 Severe 1 0.6855 0.3912 3.0697 0.0798
Sulphasalazine currently
taking
Mild 1 0.1470 0.1112 1.7468 0.1863
Sulphasalazine currently
taking
Mod 1 0.00403 0.0926 0.0019 0.9653
Page 263 of 577
Analysis of Maximum Likelihood Estimates
Parameter
Eent
Infection DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Sulphasalazine currently
taking
Severe 1 -0.0445 0.1896 0.0551 0.8144
Sulphasalazine never taking Mild 0 0 . . .
Sulphasalazine never taking Mod 0 0 . . .
Sulphasalazine never taking Severe 0 0 . . .
Tocilizumab 3 Mild 1 0.1595 0.2454 0.4224 0.5157
Tocilizumab 3 Mod 1 0.1835 0.1951 0.8847 0.3469
Tocilizumab 3 Severe 1 0.7127 0.3269 4.7534 0.0292
Tocilizumab 4 Mild 1 -
11.4739
529.0 0.0005 0.9827
Tocilizumab 4 Mod 1 -
11.5154
222.6 0.0027 0.9587
Tocilizumab 4 Severe 1 -
10.9097
820.5 0.0002 0.9894
Tocilizumab currently
taking
Mild 1 0.4933 0.1695 8.4682 0.0036
Tocilizumab currently
taking
Mod 1 0.3301 0.1348 5.9962 0.0143
Tocilizumab currently
taking
Severe 1 0.1795 0.2814 0.4069 0.5236
Tocilizumab never taking Mild 0 0 . . .
Tocilizumab never taking Mod 0 0 . . .
Tocilizumab never taking Severe 0 0 . . .
Page 264 of 577
Table A.35- Odds ratio estimates in EENT
Odds Ratio Estimates
Effect InfEent Point Estimate
95% Wald
Confidence Limits
Etanercept 3 vs never taking 1 0.950 0.795 1.136
Etanercept 3 vs never taking 2 0.931 0.811 1.069
Etanercept 3 vs never taking 3 0.672 0.505 0.894
Etanercept 4 vs never taking 1 3.682 1.266 10.702
Etanercept 4 vs never taking 2 6.840 3.491 13.400
Etanercept 4 vs never taking 3 3.834 0.706 20.819
Etanercept currently taking vs never taking 1 1.189 0.989 1.430
Etanercept currently taking vs never taking 2 1.093 0.949 1.259
Etanercept currently taking vs never taking 3 0.713 0.537 0.947
Adalimumab 3 vs never taking 1 1.010 0.845 1.209
Adalimumab 3 vs never taking 2 1.200 1.049 1.373
Adalimumab 3 vs never taking 3 1.152 0.875 1.517
Adalimumab 4 vs never taking 1 0.583 0.155 2.190
Adalimumab 4 vs never taking 2 0.999 0.419 2.385
Adalimumab 4 vs never taking 3 <0.001 <0.001 >999.999
Adalimumab currently taking vs never taking 1 1.335 1.110 1.605
Adalimumab currently taking vs never taking 2 1.203 1.041 1.390
Adalimumab currently taking vs never taking 3 0.923 0.692 1.232
Anakinra 3 vs never taking 1 1.156 0.705 1.895
Anakinra 3 vs never taking 2 0.927 0.603 1.424
Anakinra 3 vs never taking 3 1.584 0.811 3.091
Anakinra 4 vs never taking 1 0.487 0.142 1.675
Anakinra 4 vs never taking 2 1.028 0.465 2.272
Anakinra 4 vs never taking 3 0.639 0.081 5.013
Anakinra currently taking vs never taking 1 <0.001 <0.001 >999.999
Anakinra currently taking vs never taking 2 6.049 2.387 15.330
Anakinra currently taking vs never taking 3 <0.001 <0.001 >999.999
Infliximab 3 vs never taking 1 1.057 0.813 1.373
Infliximab 3 vs never taking 2 0.814 0.657 1.010
Page 265 of 577
Odds Ratio Estimates
Effect InfEent Point Estimate
95% Wald
Confidence Limits
Infliximab 3 vs never taking 3 1.049 0.712 1.544
Infliximab 4 vs never taking 1 1.556 0.671 3.608
Infliximab 4 vs never taking 2 0.821 0.394 1.710
Infliximab 4 vs never taking 3 0.447 0.077 2.582
Infliximab currently taking vs never taking 1 1.904 1.352 2.682
Infliximab currently taking vs never taking 2 1.604 1.220 2.109
Infliximab currently taking vs never taking 3 0.690 0.334 1.429
Abatacept 3 vs never taking 1 1.676 1.185 2.371
Abatacept 3 vs never taking 2 1.122 0.830 1.515
Abatacept 3 vs never taking 3 0.648 0.322 1.305
Abatacept 4 vs never taking 1 1.317 0.251 6.905
Abatacept 4 vs never taking 2 0.548 0.157 1.915
Abatacept 4 vs never taking 3 3.763 0.470 30.134
Abatacept currently taking vs never taking 1 1.400 1.027 1.908
Abatacept currently taking vs never taking 2 1.161 0.910 1.480
Abatacept currently taking vs never taking 3 0.817 0.484 1.380
Tocilizumab 3 vs never taking 1 1.173 0.725 1.897
Tocilizumab 3 vs never taking 2 1.201 0.820 1.761
Tocilizumab 3 vs never taking 3 2.039 1.075 3.870
Tocilizumab 4 vs never taking 1 <0.001 <0.001 >999.999
Tocilizumab 4 vs never taking 2 <0.001 <0.001 >999.999
Tocilizumab 4 vs never taking 3 <0.001 <0.001 >999.999
Tocilizumab currently taking vs never taking 1 1.638 1.175 2.283
Tocilizumab currently taking vs never taking 2 1.391 1.068 1.812
Tocilizumab currently taking vs never taking 3 1.197 0.689 2.077
Folic Acid currently taking vs never taking 1 0.899 0.775 1.044
Folic Acid currently taking vs never taking 2 0.845 0.752 0.950
Folic Acid currently taking vs never taking 3 0.952 0.754 1.202
Hydroxychloroquine 3 vs never taking 1 1.154 0.999 1.333
Hydroxychloroquine 3 vs never taking 2 1.259 1.124 1.409
Hydroxychloroquine 3 vs never taking 3 1.204 0.957 1.516
Hydroxychloroquine 4 vs never taking 1 0.933 0.412 2.110
Page 266 of 577
Odds Ratio Estimates
Effect InfEent Point Estimate
95% Wald
Confidence Limits
Hydroxychloroquine 4 vs never taking 2 0.973 0.506 1.872
Hydroxychloroquine 4 vs never taking 3 1.879 0.695 5.078
Hydroxychloroquine currently taking vs never taking 1 1.010 0.837 1.219
Hydroxychloroquine currently taking vs never taking 2 1.082 0.934 1.254
Hydroxychloroquine currently taking vs never taking 3 1.034 0.764 1.400
Sulphasalazine 3 vs never taking 1 1.098 0.955 1.263
Sulphasalazine 3 vs never taking 2 1.250 1.121 1.393
Sulphasalazine 3 vs never taking 3 1.171 0.937 1.463
Sulphasalazine 4 vs never taking 1 1.136 0.631 2.046
Sulphasalazine 4 vs never taking 2 0.804 0.485 1.334
Sulphasalazine 4 vs never taking 3 1.985 0.922 4.273
Sulphasalazine currently taking vs never taking 1 1.158 0.931 1.440
Sulphasalazine currently taking vs never taking 2 1.004 0.837 1.204
Sulphasalazine currently taking vs never taking 3 0.956 0.660 1.387
Arava (Leflunomide) 3 vs never taking 1 1.116 0.929 1.341
Arava (Leflunomide) 3 vs never taking 2 1.213 1.052 1.399
Arava (Leflunomide) 3 vs never taking 3 1.160 0.876 1.536
Arava (Leflunomide) 4 vs never taking 1 0.752 0.259 2.178
Arava (Leflunomide) 4 vs never taking 2 1.581 0.863 2.898
Arava (Leflunomide) 4 vs never taking 3 0.490 0.062 3.898
Arava (Leflunomide) currently taking vs never taking 1 1.311 1.065 1.613
Arava (Leflunomide) currently taking vs never taking 2 1.161 0.981 1.374
Arava (Leflunomide) currently taking vs never taking 3 1.006 0.717 1.412
Cyclosporin 3 vs never taking 1 1.027 0.854 1.234
Cyclosporin 3 vs never taking 2 1.227 1.071 1.405
Cyclosporin 3 vs never taking 3 1.594 1.229 2.066
Cyclosporin 4 vs never taking 1 0.785 0.418 1.474
Cyclosporin 4 vs never taking 2 1.070 0.694 1.648
Cyclosporin 4 vs never taking 3 0.349 0.083 1.461
Cyclosporin currently taking vs never taking 1 1.697 0.876 3.287
Cyclosporin currently taking vs never taking 2 2.840 1.833 4.403
Cyclosporin currently taking vs never taking 3 2.778 1.173 6.577
Page 267 of 577
Odds Ratio Estimates
Effect InfEent Point Estimate
95% Wald
Confidence Limits
Prednisolone 3 vs never taking 1 1.392 1.126 1.722
Prednisolone 3 vs never taking 2 1.291 1.095 1.521
Prednisolone 3 vs never taking 3 1.645 1.149 2.357
Prednisolone 4 vs never taking 1 2.190 0.729 6.576
Prednisolone 4 vs never taking 2 1.727 0.740 4.034
Prednisolone 4 vs never taking 3 2.047 0.267 15.680
Prednisolone currently taking vs never taking 1 1.182 0.955 1.462
Prednisolone currently taking vs never taking 2 1.140 0.967 1.343
Prednisolone currently taking vs never taking 3 1.479 1.032 2.118
Page 268 of 577
APPENDIX B: OUTPUT OF SAS FOR
LUNG INFECTION
Table B.1- Complete statistics for Lung infection
Model Information
Data Set WORK.IMPORT2
Response Variable InfLung InfLung
Number of Response Levels 4
Model generalized logit
Optimization Technique Newton-Raphson
Table B.2- Observation status for Lung infection
Number of Observations Read 27711
Number of Observations Used 21506
Table B.3- response value for Lung infection
Response Profile
Ordered
Value InfLung
Total
Frequency
1 1 371
2 2 1379
3 3 624
4 4 19132
Logits modelled use InfLung='4' as the reference category.
Note: 6205 observations were deleted due to missing values for the response or explanatory
variables.
Table B.4- Backward Elimination Procedure for Lung infection
Page 269 of 577
Backward Elimination Procedure
Class Level Information
Class Value Design Variables
Etanercept 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
Adalimumab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
Anakinra 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
Infliximab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
Rituximab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
Abatacept 3 1 0 0 0
4 0 1 0 0
Page 270 of 577
currently taking 0 0 1 0
b never taking 0 0 0 1
Tocilizumab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
Golimumab 3 1 0 0
currently taking 0 1 0
b never taking 0 0 1
Methotrexate 1 1 0 0 0
2 0 1 0 0
3 0 0 1 0
4 0 0 0 1
Certolizumab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
Methotrexate (plus Folic acid) currently taking 1 0
b never taking 0 1
Hydroxychloroquine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
Sulphasalazine 3 1 0 0 0
Page 271 of 577
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
Arava (Leflunomide) 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
Azathioprine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
Cyclosporine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
Prednisolone 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
IM Gold 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
Penicillamine 3 1 0 0 0
Page 272 of 577
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
Step 0. The following effects were entered:
Etanercept Adalimumab Anakinra Infliximab Rituximab Abatacept Tocilizumab
Golimumab Methotrexate Certolizumab Methotrexate (plus Folic acid)
Hydroxychloroquine Sulphasalazine Arava (Leflunomide) Azathioprine Cyclosporine
Prednisolone IM Gold Penicillamine
Table B.5- Model Convergence status for Lung infection
Model Convergence Status
Quasi-complete separation of data points detected.
Page 273 of 577
Table B.6- Model Fit statistics for Lung infection
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 19488.155 19401.988
SC 19512.083 20718.042
-2 Log L 19482.155 19071.988
Table B.7- Testing null hypothesis for Lung infection
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 410.1672 162 <.0001
Score 433.5687 162 <.0001
Wald 397.0385 162 <.0001
Table B.8- Model Fit statistics for removing covariant step 1
Step 1. Effect Certolizumab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Page 274 of 577
Table B.9- Model Fit statistics for removing covariant step 1
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 19488.155 19396.072
SC 19512.083 20640.342
-2 Log L 19482.155 19084.072
Table B.10- Testing Null hypothesis after removing covariant step 1
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 398.0824 153 <.0001
Score 413.5994 153 <.0001
Wald 391.7407 153 <.0001
Table B.11- Residual removing covariant step 1
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
13.3285 9 0.1483
Page 275 of 577
Table B.12- Model Fit statistics for removing covariant step 2
Step 2. Effect Penicillamine is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table B.13- Model Fit statistics after removing covariant step 2
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 19488.155 19393.250
SC 19512.083 20565.735
-2 Log L 19482.155 19099.250
Table B.14- Testing Null hypothesis after removing covariant step 2
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 382.9044 144 <.0001
Score 400.4674 144 <.0001
Wald 382.3359 144 <.0001
Page 276 of 577
Table B.15- Residual removing covariant step 2
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
25.5437 18 0.1107
Table B.16- Model Fit statistics for removing covariant step 3
Step 3. Effect Methotrexate and Folic acid is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table B.17- Model Fit statistics after removing covariant step 3
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 19488.155 19390.300
SC 19512.083 20538.856
-2 Log L 19482.155 19102.300
Page 277 of 577
Table B.18- Testing Null hypothesis after removing covariant step 3
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 379.8552 141 <.0001
Score 397.4990 141 <.0001
Wald 379.3969 141 <.0001
Table B.19- Residual removing covariant step 3
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
28.2177 21 0.1341
Table B.20- Model Fit statistics for removing covariant step 4
Step 4. Effect Azathioprine is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Page 278 of 577
Table B.21- Model Fit statistics after removing covariant step 4
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 19488.155 19383.751
SC 19512.083 20460.523
-2 Log L 19482.155 19113.751
Table B.22- Testing Null hypothesis after removing covariant step 4
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 368.4040 132 <.0001
Score 387.3219 132 <.0001
Wald 368.8331 132 <.0001
Table B.23- Residual removing covariant step 4
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
38.6702 30 0.1333
Page 279 of 577
Table B.24- Model Fit statistics for removing covariant step 5
Step 5. Effect Rituximab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table B.25- Model Fit statistics after removing covariant step 5
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 19488.155 19376.800
SC 19512.083 20381.787
-2 Log L 19482.155 19124.800
Table B.26- Testing Null hypothesis after removing covariant step 5
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 357.3544 123 <.0001
Score 375.9798 123 <.0001
Wald 357.3572 123 <.0001
Page 280 of 577
Table B.27- Residual removing covariant step 5
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
49.5870 39 0.1192
Table B.28- Model Fit statistics for removing covariant step 6
Step 6. Effect Infliximab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table B.29- Model Fit statistics after removing covariant step 6
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 19488.155 19371.000
SC 19512.083 20304.202
-2 Log L 19482.155 19137.000
Page 281 of 577
Table B.30- Testing Null hypothesis after removing covariant step 6
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 345.1549 114 <.0001
Score 362.5786 114 <.0001
Wald 344.7899 114 <.0001
Table B.31- Residual removing covariant step 7
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
62.0449 48 0.0838
Step 7. Effect Tocilizumab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Page 282 of 577
Table B.32- Model Fit statistics after removing covariant step 6
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 19488.155 19367.574
SC 19512.083 20228.992
-2 Log L 19482.155 19151.574
Table B.33- Testing Null hypothesis after removing covariant step 6
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 330.5805 105 <.0001
Score 344.9075 105 <.0001
Wald 328.9232 105 <.0001
Table B.34- Residual removing covariant step 7
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
81.8928 57 0.0170
Page 283 of 577
Step 8. Effect Golimumab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table B.35- Model Fit statistics after removing covariant step 6
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 19488.155 19368.130
SC 19512.083 20181.691
-2 Log L 19482.155 19164.130
Table B.36- Testing Null hypothesis after removing covariant step 6
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 318.0249 99 <.0001
Score 331.5066 99 <.0001
Wald 316.0759 99 <.0001
Page 284 of 577
Table B.37- Residual removing covariant step 7
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
94.1491 63 0.0067
Step 9. Effect Arava (Leflunomide) is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table B.38- Model Fit statistics after removing covariant step 6
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 19488.155 19366.491
SC 19512.083 20108.268
-2 Log L 19482.155 19180.491
Page 285 of 577
Table B.39- Testing Null hypothesis after removing covariant step 6
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 301.6634 90 <.0001
Score 316.5298 90 <.0001
Wald 300.9549 90 <.0001
Table B.40- Residual removing covariant step 7
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
111.8224 72 0.0018
Step 10. Effect Adalimumab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table B.41- Model Fit statistics after removing covariant step 6
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 19488.155 19366.037
SC 19512.083 20036.028
-2 Log L 19482.155 19198.037
Page 286 of 577
Table B.42- Testing Null hypothesis after removing covariant step 6
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 284.1184 81 <.0001
Score 299.1489 81 <.0001
Wald 283.8184 81 <.0001
Table B.43- Residual removing covariant step 7
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
128.1890 81 0.0007
Page 287 of 577
Table B.44- Summary of backward elimination in Lung
Summary of Backward Elimination
Step
Effect
Removed DF
Number
In
Wald
Chi-
Square Pr > ChiSq
Variable
Label
1 Certolizumab 9 18 5.2822 0.8090 Certolizumab
2 Penicillamine 9 17 8.9767 0.4394 Penicillamine
3 Methotrexate (plus
Folic acid)
3 16 3.0046 0.3909 Methotrexate (plus Folic
acid)
4 Azathioprine 9 15 10.4127 0.3181 Azathioprine
5 Rituximab 9 14 10.6214 0.3026 Rituximab
6 Infliximab 9 13 12.4421 0.1895
7 Tocilizumab 9 12 14.5987 0.1026 Tocilizumab
8 Golimumab 6 11 11.7349 0.0682 Golimumab
9 Arava (Leflunomide) 9 10 16.1280 0.0643 Arava (Leflunomide)
10 Adalimumab 9 9 16.1807 0.0632
Page 288 of 577
Summary of Backward Elimination
Step
Effect
Removed DF
Number
In
Wald
Chi-
Square Pr > ChiSq
Variable
Label
1 Certolizumab 9 18 5.2822 0.8090 Certolizumab
2 Penicillamine 9 17 8.9767 0.4394 Penicillamine
3 Methotrexate (plus
Folic acid)
3 16 3.0046 0.3909 Methotrexate (plus Folic
acid)
4 Azathioprine 9 15 10.4127 0.3181 Azathioprine
5 Rituximab 9 14 10.6214 0.3026 Rituximab
6 Infliximab 9 13 12.4421 0.1895
7 Tocilizumab 9 12 14.5987 0.1026 Tocilizumab
8 Golimumab 6 11 11.7349 0.0682 Golimumab
9 Arava (Leflunomide) 9 10 16.1280 0.0643 Arava (Leflunomide)
10 Adalimumab 9 9 16.1807 0.0632
Page 289 of 577
Table B.45- Type 3 analysis of effects in Lung
Type 3 Analysis of Effects
Effect DF
Wald
Chi-Square Pr > ChiSq
Etanercept 9 31.4874 0.0002
Anakinra 9 20.0990 0.0173
Abatacept 9 34.9246 <.0001
Methotrexate 9 20.5746 0.0147
Hydroxychloroquine 9 24.4648 0.0036
Sulphasalazine 9 20.8255 0.0134
Cyclosporine 9 20.6307 0.0144
Prednisolone 9 67.5034 <.0001
IM Gold 9 19.8810 0.0187
Table B.46- Analysis of maximum likelihood estimates in Lung
Analysis of Maximum Likelihood Estimates
Parameter InfLung DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Intercept Mild 1 -
15.2999
132.2 0.0134 0.9079
Intercept Mod 1 -4.3102 0.6251 47.5499 <.0001
Intercept Severe 1 -4.2292 0.5184 66.5431 <.0001
Abatacept 3 Mild 1 -0.0779 0.3188 0.0598 0.8068
Abatacept 3 Mod 1 0.3341 0.1584 4.4525 0.0349
Abatacept 3 Severe 1 0.5698 0.1984 8.2437 0.0041
Abatacept 4 Mild 1 1.0112 1.0300 0.9638 0.3262
Page 290 of 577
Analysis of Maximum Likelihood Estimates
Parameter InfLung DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Abatacept 4 Mod 1 -0.0585 0.7462 0.0062 0.9375
Abatacept 4 Severe 1 0.0807 0.9976 0.0065 0.9355
Abatacept currently
taking
Mild 1 0.3420 0.2178 2.4657 0.1164
Abatacept currently
taking
Mod 1 0.5392 0.1180 20.8696 <.0001
Abatacept currently
taking
Severe 1 -0.0929 0.2079 0.1996 0.6550
Abatacept b never
taking
Mild 0 0 . . .
Abatacept b never
taking
Mod 0 0 . . .
Abatacept b never
taking
Severe 0 0 . . .
Anakinra 3 Mild 1 0.5311 0.3487 2.3189 0.1278
Anakinra 3 Mod 1 0.6433 0.1815 12.5635 0.0004
Anakinra 3 Severe 1 -
0.00489
0.3302 0.0002 0.9882
Anakinra 4 Mild 1 0.1512 0.6315 0.0573 0.8108
Anakinra 4 Mod 1 -0.0709 0.3858 0.0337 0.8543
Anakinra 4 Severe 1 -0.9695 0.6978 1.9308 0.1647
Anakinra currently
taking
Mild 1 1.1898 1.0395 1.3102 0.2524
Anakinra currently
taking
Mod 1 1.0875 0.6364 2.9208 0.0874
Page 291 of 577
Analysis of Maximum Likelihood Estimates
Parameter InfLung DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Anakinra currently
taking
Severe 1 -
11.1525
348.5 0.0010 0.9745
Anakinra b never
taking
Mild 0 0 . . .
Anakinra b never
taking
Mod 0 0 . . .
Anakinra b never
taking
Severe 0 0 . . .
Cyclosporine 3 Mild 1 -0.1304 0.1673 0.6076 0.4357
Cyclosporine 3 Mod 1 0.0167 0.0825 0.0411 0.8393
Cyclosporine 3 Severe 1 -0.1421 0.1218 1.3629 0.2430
Cyclosporine 4 Mild 1 -0.6137 0.6260 0.9613 0.3269
Cyclosporine 4 Mod 1 -0.1896 0.2827 0.4496 0.5025
Cyclosporine 4 Severe 1 -0.1456 0.3622 0.1615 0.6878
Cyclosporine currently
taking
Mild 1 1.2314 0.3793 10.5374 0.0012
Cyclosporine currently
taking
Mod 1 0.7209 0.2642 7.4466 0.0064
Cyclosporine currently
taking
Severe 1 0.2533 0.4633 0.2990 0.5845
Cyclosporine b never
taking
Mild 0 0 . . .
Cyclosporine b never
taking
Mod 0 0 . . .
Page 292 of 577
Analysis of Maximum Likelihood Estimates
Parameter InfLung DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Cyclosporine b never
taking
Severe 0 0 . . .
Etanercept 3 Mild 1 0.2222 0.1357 2.6825 0.1015
Etanercept 3 Mod 1 -0.1754 0.0777 5.1023 0.0239
Etanercept 3 Severe 1 0.00414 0.1087 0.0015 0.9696
Etanercept 4 Mild 1 0.9834 0.9443 1.0845 0.2977
Etanercept 4 Mod 1 1.1483 0.4726 5.9037 0.0151
Etanercept 4 Severe 1 1.7757 0.4978 12.7241 0.0004
Etanercept currently
taking
Mild 1 -0.1632 0.1350 1.4614 0.2267
Etanercept currently
taking
Mod 1 -0.0438 0.0677 0.4200 0.5169
Etanercept currently
taking
Severe 1 -0.1177 0.1018 1.3382 0.2473
Etanercept b never
taking
Mild 0 0 . . .
Etanercept b never
taking
Mod 0 0 . . .
Etanercept b never
taking
Severe 0 0 . . .
Hydroxychloroquine 3 Mild 1 0.0478 0.1222 0.1529 0.6958
Hydroxychloroquine 3 Mod 1 0.0739 0.0647 1.3065 0.2530
Hydroxychloroquine 3 Severe 1 0.0384 0.0974 0.1554 0.6934
Hydroxychloroquine 4 Mild 1 -0.9431 1.0241 0.8481 0.3571
Page 293 of 577
Analysis of Maximum Likelihood Estimates
Parameter InfLung DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Hydroxychloroquine 4 Mod 1 0.1702 0.3488 0.2382 0.6255
Hydroxychloroquine 4 Severe 1 0.5359 0.3995 1.7995 0.1798
Hydroxychloroquine currently
taking
Mild 1 0.2042 0.1458 1.9617 0.1613
Hydroxychloroquine currently
taking
Mod 1 0.1582 0.0804 3.8734 0.0491
Hydroxychloroquine currently
taking
Severe 1 0.4302 0.1114 14.9202 0.0001
Hydroxychloroquine b never
taking
Mild 0 0 . . .
Hydroxychloroquine b never
taking
Mod 0 0 . . .
Hydroxychloroquine b never
taking
Severe 0 0 . . .
IM Gold 3 Mild 1 -0.3051 0.1356 5.0582 0.0245
IM Gold 3 Mod 1 -0.0313 0.0676 0.2144 0.6433
IM Gold 3 Severe 1 0.1995 0.0945 4.4514 0.0349
IM Gold 4 Mild 1 0.7848 0.6264 1.5698 0.2102
IM Gold 4 Mod 1 0.6082 0.3463 3.0852 0.0790
IM Gold 4 Severe 1 0.7700 0.4271 3.2496 0.0714
IM Gold currently
taking
Mild 1 -1.3342 1.0067 1.7565 0.1851
IM Gold currently
taking
Mod 1 0.2764 0.2666 1.0746 0.2999
Page 294 of 577
Analysis of Maximum Likelihood Estimates
Parameter InfLung DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
IM Gold currently
taking
Severe 1 -0.1061 0.4610 0.0529 0.8181
IM Gold b never
taking
Mild 0 0 . . .
IM Gold b never
taking
Mod 0 0 . . .
IM Gold b never
taking
Severe 0 0 . . .
Methotrexate 1 Mild 1 10.9145 132.2 0.0068 0.9342
Methotrexate 1 Mod 1 1.2418 0.6189 4.0258 0.0448
Methotrexate 1 Severe 1 -0.0534 0.4945 0.0117 0.9140
Methotrexate Currently
taking
Mild 1 11.1219 132.2 0.0071 0.9329
Methotrexate Currently
taking
Mod 1 1.4974 0.6251 5.7387 0.0166
Methotrexate Currently
taking
Severe 1 -0.0981 0.5177 0.0359 0.8497
Methotrexate 3 Mild 1 10.9202 132.2 0.0068 0.9342
Methotrexate 3 Mod 1 1.4460 0.6211 5.4198 0.0199
Methotrexate 3 Severe 1 0.1561 0.5001 0.0974 0.7549
Methotrexate 4 Mild 0 0 . . .
Methotrexate 4 Mod 0 0 . . .
Methotrexate 4 Severe 0 0 . . .
Prednisolone 3 Mild 1 0.3453 0.1830 3.5622 0.0591
Page 295 of 577
Analysis of Maximum Likelihood Estimates
Parameter InfLung DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Prednisolone 3 Mod 1 0.1782 0.0939 3.6004 0.0578
Prednisolone 3 Severe 1 0.4537 0.1637 7.6796 0.0056
Prednisolone 4 Mild 1 -
10.5110
219.0 0.0023 0.9617
Prednisolone 4 Mod 1 -1.1526 1.0341 1.2422 0.2650
Prednisolone 4 Severe 1 -0.6292 1.0771 0.3413 0.5591
Prednisolone currently
taking
Mild 1 0.4916 0.1786 7.5773 0.0059
Prednisolone currently
taking
Mod 1 0.3192 0.0919 12.0656 0.0005
Prednisolone currently
taking
Severe 1 0.8943 0.1574 32.2591 <.0001
Prednisolone b never
taking
Mild 0 0 . . .
Prednisolone b never
taking
Mod 0 0 . . .
Prednisolone b never
taking
Severe 0 0 . . .
Sulphasalazine 3 Mild 1 0.00994 0.1188 0.0070 0.9333
Sulphasalazine 3 Mod 1 0.2041 0.0627 10.6112 0.0011
Sulphasalazine 3 Severe 1 0.1018 0.0918 1.2279 0.2678
Sulphasalazine 4 Mild 1 0.1048 0.4945 0.0449 0.8322
Sulphasalazine 4 Mod 1 -0.5335 0.3316 2.5882 0.1077
Sulphasalazine 4 Severe 1 0.1219 0.3492 0.1219 0.7270
Page 296 of 577
Analysis of Maximum Likelihood Estimates
Parameter InfLung DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Sulphasalazine currently
taking
Mild 1 0.3020 0.1660 3.3084 0.0689
Sulphasalazine currently
taking
Mod 1 0.0113 0.1022 0.0122 0.9120
Sulphasalazine currently
taking
Severe 1 0.00650 0.1447 0.0020 0.9641
Sulphasalazine b never
taking
Mild 0 0 . . .
Sulphasalazine b never
taking
Mod 0 0 . . .
Sulphasalazine b never
taking
Severe 0 0 . . .
Table B.47- Odds ratio estimates in Lung
Odds Ratio Estimates
Effect InfLung
Point
Estimate
95% Wald
Confidence Limits
Etanercept 3 Versus never taking Mild 1.249 0.957 1.629
Etanercept 3 Versus never taking Mod 0.839 0.721 0.977
Etanercept 3 Versus never taking Severe 1.004 0.812 1.243
Etanercept 4 Versus never taking Mild 2.674 0.420 17.018
Etanercept 4 Versus never taking Mod 3.153 1.249 7.961
Etanercept 4 Versus never taking Severe 5.905 2.226 15.664
Etanercept currently taking Versus never taking Mild 0.849 0.652 1.107
Etanercept currently taking Versus never taking Mod 0.957 0.838 1.093
Page 297 of 577
Odds Ratio Estimates
Effect InfLung
Point
Estimate
95% Wald
Confidence Limits
Etanercept currently taking Versus never taking Severe 0.889 0.728 1.085
Anakinra 3 Versus never taking Mild 1.701 0.859 3.369
Anakinra 3 Versus never taking Mod 1.903 1.333 2.716
Anakinra 3 Versus never taking Severe 0.995 0.521 1.901
Anakinra 4 Versus never taking Mild 1.163 0.337 4.010
Anakinra 4 Versus never taking Mod 0.932 0.437 1.985
Anakinra 4 Versus never taking Severe 0.379 0.097 1.489
Anakinra currently taking Versus never taking Mild 3.286 0.428 25.207
Anakinra currently taking Versus never taking Mod 2.967 0.852 10.327
Anakinra currently taking Versus never taking Severe <0.001 <0.001 >999.999
Abatacept 3 Versus never taking Mild 0.925 0.495 1.728
Abatacept 3 Versus never taking Mod 1.397 1.024 1.905
Abatacept 3 Versus never taking Severe 1.768 1.198 2.608
Abatacept 4 Versus never taking Mild 2.749 0.365 20.695
Abatacept 4 Versus never taking Mod 0.943 0.218 4.071
Abatacept 4 Versus never taking Severe 1.084 0.153 7.660
Abatacept currently taking Versus never taking Mild 1.408 0.919 2.157
Abatacept currently taking Versus never taking Mod 1.715 1.361 2.161
Abatacept currently taking Versus never taking Severe 0.911 0.606 1.370
Methotrexate 1 vs 4 Mild >999.999 <0.001 >999.999
Methotrexate 1 Versus never taking Mod 3.462 1.029 11.643
Methotrexate 1 Versus never taking Severe 0.948 0.360 2.499
Page 298 of 577
Odds Ratio Estimates
Effect InfLung
Point
Estimate
95% Wald
Confidence Limits
Methotrexate currently taking Versus never
taking
Mild >999.999 <0.001 >999.999
Methotrexate currently taking Versus never
taking
Mod 4.470 1.313 15.218
Methotrexate currently taking Versus never
taking
Severe 0.907 0.329 2.501
Methotrexate 3 vs 4 Mild >999.999 <0.001 >999.999
Methotrexate 3 vs 4 Mod 4.246 1.257 14.344
Methotrexate 3 vs 4 Severe 1.169 0.439 3.115
Hydroxychloroquine 3 Versus never
taking
Mild 1.049 0.826 1.333
Hydroxychloroquine 3 Versus never
taking
Mod 1.077 0.949 1.222
Hydroxychloroquine 3 Versus never
taking
Severe 1.039 0.858 1.258
Hydroxychloroquine 4 Versus never
taking
Mild 0.389 0.052 2.898
Hydroxychloroquine 4 Versus never
taking
Mod 1.186 0.598 2.349
Hydroxychloroquine 4 Versus never
taking
Severe 1.709 0.781 3.739
Hydroxychloroquine currently taking Versus
never taking
Mild 1.227 0.922 1.632
Hydroxychloroquine currently taking Versus
never taking
Mod 1.171 1.001 1.371
Hydroxychloroquine currently taking Versus
never taking
Severe 1.538 1.236 1.913
Page 299 of 577
Odds Ratio Estimates
Effect InfLung
Point
Estimate
95% Wald
Confidence Limits
Sulphasalazine 3 Versus never taking Mild 1.010 0.800 1.275
Sulphasalazine 3 Versus never taking Mod 1.226 1.085 1.387
Sulphasalazine 3 Versus never taking Severe 1.107 0.925 1.325
Sulphasalazine 4 Versus never taking Mild 1.110 0.421 2.927
Sulphasalazine 4 Versus never taking Mod 0.587 0.306 1.124
Sulphasalazine 4 Versus never taking Severe 1.130 0.570 2.240
Sulphasalazine currently taking Versus never
taking
Mild 1.353 0.977 1.873
Sulphasalazine currently taking Versus never
taking
Mod 1.011 0.828 1.236
Sulphasalazine currently taking Versus never
taking
Severe 1.007 0.758 1.337
Cyclosporine 3 Versus never taking Mild 0.878 0.632 1.218
Cyclosporine 3 Versus never taking Mod 1.017 0.865 1.195
Cyclosporine 3 Versus never taking Severe 0.868 0.683 1.101
Cyclosporine 4 Versus never taking Mild 0.541 0.159 1.846
Cyclosporine 4 Versus never taking Mod 0.827 0.475 1.440
Cyclosporine 4 Versus never taking Severe 0.865 0.425 1.758
Cyclosporine currently taking Versus never
taking
Mild 3.426 1.629 7.206
Cyclosporine currently taking Versus never
taking
Mod 2.056 1.225 3.451
Cyclosporine currently taking Versus never
taking
Severe 1.288 0.520 3.194
Prednisolone 3 Versus never taking Mild 1.412 0.987 2.022
Page 300 of 577
Odds Ratio Estimates
Effect InfLung
Point
Estimate
95% Wald
Confidence Limits
Prednisolone 3 Versus never taking Mod 1.195 0.994 1.437
Prednisolone 3 Versus never taking Severe 1.574 1.142 2.170
Prednisolone 4 Versus never taking Mild <0.001 <0.001 >999.999
Prednisolone 4 Versus never taking Mod 0.316 0.042 2.397
Prednisolone 4 Versus never taking Severe 0.533 0.065 4.401
Prednisolone currently taking Versus never
taking
Mild 1.635 1.152 2.320
Prednisolone currently taking Versus never
taking
Mod 1.376 1.149 1.648
Prednisolone currently taking Versus never
taking
Severe 2.446 1.796 3.330
IM Gold 3 Versus never taking Mild 0.737 0.565 0.962
IM Gold 3 Versus never taking Mod 0.969 0.849 1.106
IM Gold 3 Versus never taking Severe 1.221 1.014 1.469
IM Gold 4 Versus never taking Mild 2.192 0.642 7.482
IM Gold 4 Versus never taking Mod 1.837 0.932 3.621
IM Gold 4 Versus never taking Severe 2.160 0.935 4.989
IM Gold currently taking Versus never taking Mild 0.263 0.037 1.894
IM Gold currently taking Versus never taking Mod 1.318 0.782 2.223
IM Gold currently taking Versus never taking Severe 0.899 0.364 2.220
Page 301 of 577
APPENDIX C: OUTPUT OF SAS FOR
NAIL AND SKIN INFECTION
Table C.1- Complete statistics for Nail and skin infection
Model Information
Data Set WORK.IMPORT2
Response Variable Nail and skin infection InfSkin
Number of Response Levels 4
Model generalized logit
Optimization Technique Newton-Raphson
Table C.2- Observation status for Nail and skin infection
Number of Observations Read 27711
Number of Observations Used 21506
Page 302 of 577
Table C.3- response value for Nail and skin infection
Response Profile
Ordered
Value Skin and nail infection
Total
Frequency
1 Mild 1253
2 Moderate 1039
3 Severe 361
4 No report 18853
Logits modelled use InfSkin='4' as the reference category.
Note: 6205 observations were deleted due to missing values for the response or explanatory
variables.
3= Never taken
4= Don’t know
Page 303 of 577
Table C.4- Backward Elimination Procedure for Nail and skin infection
Backward Elimination Procedure
Class Level Information
Class Value Design Variables
Etanercept 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Adalimumab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Anakinra 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Infliximab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Rituximab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Abatacept 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Tocilizumab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Golimumab 3 1 0 0
currently taking 0 1 0
Page 304 of 577
never taking 0 0 1
Certolizumab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Folic Acid currently taking 1 0
never taking 0 1
Hydroxychloroquine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Sulphasalazine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Arava (Leflunomide) 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Azathioprine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Azathioprine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Prednisolone 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
IM Gold injection 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
Page 305 of 577
never taking 0 0 0 1
Penicillamine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Step 0. The following effects were entered:
Intercept Etanercept Adalimumab Anakinra Infliximab Rituximab Abatacept Tocilizumab
Golimumab Certolizumab Folic Acid Hydroxychloroquine Sulphasalazine Arava
(Leflunomide) Azathioprine Prednisolone IM Gold injection Penicillamine
Table C.5- Model Convergence status for Nail and skin infection
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table C.6- Model Fit statistics for Nail and skin infection
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 21341.861 21261.541
SC 21365.789 22505.811
-2 Log L 21335.861 20949.541
Table C.7- Testing null hypothesis for Nail and skin infection
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 386.3201 153 <.0001
Score 425.2533 153 <.0001
Wald 382.0017 153 <.0001
Page 306 of 577
Table C.8- Model Fit statistics for removing covariant step 1
Step 1. Effect Certolizumab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table C.9- Model Fit statistics for removing covariant step 1
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 21341.861 21252.373
SC 21365.789 22424.858
-2 Log L 21335.861 20958.373
Table C.10- Testing Null hypothesis after removing covariant step 1
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 377.4876 144 <.0001
Score 417.3618 144 <.0001
Page 307 of 577
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Wald 374.6182 144 <.0001
Table C.11- Residual removing covariant step 1
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
7.5708 9 0.5779
Table C.12- Model Fit statistics for removing covariant step 2
Step 2. Effect Hydroxychloroquine is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table C.13- Model Fit statistics after removing covariant step 2
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 21341.861 21241.218
SC 21365.789 22341.919
-2 Log L 21335.861 20965.218
Table C.14- Testing Null hypothesis after removing covariant step 2
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 370.6425 135 <.0001
Page 308 of 577
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Score 410.3001 135 <.0001
Wald 367.1567 135 <.0001
Table C.15- Residual removing covariant step 2
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
14.9868 18 0.6629
Table C.16- Model Fit statistics for removing covariant step 3
Step 3. Effect IM Gold injection is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table C.17- Model Fit statistics after removing covariant step 3
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 21341.861 21232.298
SC 21365.789 22261.213
-2 Log L 21335.861 20974.298
Page 309 of 577
Table C.18- Testing Null hypothesis after removing covariant step 3
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 361.5629 126 <.0001
Score 400.6553 126 <.0001
Wald 357.7151 126 <.0001
Table C.19- Residual removing covariant step 3
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
25.6149 27 0.5400
Page 310 of 577
Table C.20- Model Fit statistics for removing covariant step 4
Step 4. Effect Abatacept is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table C.21- Model Fit statistics after removing covariant step 4
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 21341.861 21231.336
SC 21365.789 22188.467
-2 Log L 21335.861 20991.336
Table C.22- Testing Null hypothesis after removing covariant step 4
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 344.5246 117 <.0001
Score 385.9767 117 <.0001
Wald 345.3642 117 <.0001
Page 311 of 577
Table C.23- Residual removing covariant step 4
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
40.5589 36 0.2763
Table C.24- Model Fit statistics for removing covariant step 5
Step 5. Effect Tocilizumab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table C.25- Model Fit statistics after removing covariant step 5
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 21341.861 21224.235
SC 21365.789 22109.581
-2 Log L 21335.861 21002.235
Page 312 of 577
Table C.26- Testing Null hypothesis after removing covariant step 5
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 333.6261 108 <.0001
Score 374.4255 108 <.0001
Wald 334.8860 108 <.0001
Table C.27- Residual removing covariant step 5
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
52.5641 45 0.2044
Table C.28- Model Fit statistics for removing covariant step 6
Step 6. Effect Penicillamine is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Page 313 of 577
Table C.29- Model Fit statistics after removing covariant step 6
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 21341.861 21226.325
SC 21365.789 22039.885
-2 Log L 21335.861 21022.325
Table C.30- Testing Null hypothesis after removing covariant step 6
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 313.5364 99 <.0001
Score 353.7825 99 <.0001
Wald 320.6062 99 <.0001
Table C.31- Residual removing covariant step 6
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
67.5116 54 0.1024
Page 314 of 577
Table C.32- Model Fit statistics for removing covariant step 7
Step 7. Effect Golimumab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table C.33- Model Fit statistics after removing covariant step 7
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 21341.861 21223.598
SC 21365.789 21989.302
-2 Log L 21335.861 21031.598
Table C.34- Testing Null hypothesis after removing covariant step 7
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 304.2632 93 <.0001
Score 343.8880 93 <.0001
Wald 311.0294 93 <.0001
Page 315 of 577
Table C.35- Residual removing covariant step 8
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
77.4503 60 0.0643
Table C.36- Model Fit statistics for removing covariant step 8
Step 8. Effect Anakinra is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table C.37- Model Fit statistics after removing covariant step 8
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 21341.861 21220.330
SC 21365.789 21914.249
-2 Log L 21335.861 21046.330
Page 316 of 577
Table C.38- Testing Null hypothesis after removing covariant step 8
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 289.5314 84 <.0001
Score 325.8222 84 <.0001
Wald 295.3844 84 <.0001
Table C.39- Residual removing covariant step 8
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
94.3919 69 0.0229
Table C.40- Model Fit statistics for removing covariant step 9
Step 9. Effect Azathioprine is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Page 317 of 577
Table C.41- Model Fit statistics after removing covariant step 9
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 21341.861 21224.705
SC 21365.789 21846.840
-2 Log L 21335.861 21068.705
Table C.42- Testing Null hypothesis after removing covariant step 9
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 267.1562 75 <.0001
Score 306.6692 75 <.0001
Wald 278.4445 75 <.0001
Table C.43- Residual removing covariant step 9
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
112.3163 78 0.0066
Table C.44- Model Fit statistics for removing covariant step 10
Step 10. Effect Azathioprine is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table C.45- Model Fit statistics after removing covariant step 10
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 21341.861 21220.298
SC 21365.789 21770.648
Page 318 of 577
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
-2 Log L 21335.861 21082.298
Table C.46- Testing Null hypothesis after removing covariant step 10
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 253.5629 66 <.0001
Score 289.6632 66 <.0001
Wald 262.6972 66 <.0001
Table C.47- Residual removing covariant step 10
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
129.4888 87 0.0021
Note: No (additional) effects met the 0.05 significance level for removal from the model.
Page 319 of 577
Table C.48- Summary of backward elimination in Nail and skin infection
Summary of Backward Elimination
Step
Effect
Removed DF
Number
In
Wald
Chi-Square Pr > ChiSq
Variable
Label
1 Certolizumab 9 17 7.1370 0.6229 Certolizumab
2 Hydroxychloroquine 9 16 7.2627 0.6098 Hydroxychloroquine
3 IM Gold injection 9 15 9.6964 0.3756 IM Gold injection
4 Abatacept 9 14 11.5852 0.2377 Abatacept
5 Tocilizumab 9 13 10.3331 0.3242 Tocilizumab
6 Penicillamine 9 12 12.7175 0.1758 Penicillamine
7 Golimumab 6 11 9.9769 0.1256 Golimumab
8 Anakinra 9 10 14.7879 0.0969
9 Azathioprine 9 9 15.7929 0.0713 Azathioprine
10 Azathioprine 9 8 15.9375 0.0682 Azathioprine
Table C.49- Type 3 analysis of effects in Nail and skin infection
Type 3 Analysis of Effects
Effect DF
Wald
Chi-Square Pr > ChiSq
Etanercept 9 24.9423 0.0030
Page 320 of 577
Type 3 Analysis of Effects
Effect DF
Wald
Chi-Square Pr > ChiSq
Adalimumab 9 21.4151 0.0109
Infliximab 9 29.8970 0.0005
Rituximab 9 24.2231 0.0040
Folic Acid 3 25.6065 <.0001
Sulphasalazine 9 34.6779 <.0001
Arava (Leflunomide) 9 26.5839 0.0016
Prednisolone 9 38.0131 <.0001
Before studying the following tables please use the following codes:
Skin and nail infection level (Infskin) are 1= mild, 2= moderate, 3= severe, 4= not reported
Taking medication level just currently taking medication and never taken medication is
important for us, but we have also a few reports for 3= stopped taking medication, 4= don’t
know if patient took the medication or not.
Table C.50- Analysis of maximum likelihood estimates in Skin and nail infection
Analysis of Maximum Likelihood Estimates
Parameter
Taking
medication
status
Skin and nail
infection DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Intercept Mild 1 -2.8266 0.0946 893.5678 <.0001
Intercept Mod 1 -2.9696 0.1016 853.9240 <.0001
Intercept Severe 1 -4.9152 0.2294 458.9436 <.0001
Adalimumab 3 Mild 1 0.1667 0.0834 3.9963 0.0456
Adalimumab 3 Mod 1 0.1512 0.0894 2.8596 0.0908
Adalimumab 3 Severe 1 0.3663 0.1402 6.8214 0.0090
Adalimumab 4 Mild 1 -0.3524 0.6067 0.3375 0.5613
Adalimumab 4 Mod 1 -0.2622 0.5666 0.2142 0.6435
Adalimumab 4 Severe 1 0.2124 0.6303 0.1136 0.7361
Adalimumab currently taking Mild 1 0.2213 0.0829 7.1270 0.0076
Adalimumab currently taking Mod 1 0.00778 0.0910 0.0073 0.9319
Adalimumab currently taking Severe 1 -0.0846 0.1565 0.2922 0.5888
Page 321 of 577
Analysis of Maximum Likelihood Estimates
Parameter
Taking
medication
status
Skin and nail
infection DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Adalimumab never taking Mild 0 0 . . .
Adalimumab never taking Mod 0 0 . . .
Adalimumab never taking Severe 0 0 . . .
Arava
(Leflunomide)
3 Mild 1 0.0482 0.0837 0.3325 0.5642
Arava
(Leflunomide)
3 Mod 1 0.1231 0.0913 1.8168 0.1777
Arava
(Leflunomide)
3 Severe 1 -0.1156 0.1497 0.5969 0.4398
Arava
(Leflunomide)
4 Mild 1 -0.9108 0.6082 2.2430 0.1342
Arava
(Leflunomide)
4 Mod 1 0.2157 0.4031 0.2864 0.5926
Arava
(Leflunomide)
4 Severe 1 -0.9201 0.7794 1.3938 0.2378
Arava
(Leflunomide)
currently taking Mild 1 0.3055 0.0932 10.7435 0.0010
Arava
(Leflunomide)
currently taking Mod 1 0.2191 0.1044 4.4011 0.0359
Arava
(Leflunomide)
currently taking Severe 1 0.1804 0.1689 1.1416 0.2853
Arava
(Leflunomide)
never taking Mild 0 0 . . .
Arava
(Leflunomide)
never taking Mod 0 0 . . .
Arava
(Leflunomide)
never taking Severe 0 0 . . .
Etanercept 3 Mild 1 -0.0175 0.0818 0.0459 0.8304
Etanercept 3 Mod 1 -0.0827 0.0890 0.8645 0.3525
Etanercept 3 Severe 1 -0.1033 0.1422 0.5275 0.4677
Etanercept 4 Mild 1 0.4892 0.5332 0.8418 0.3589
Page 322 of 577
Analysis of Maximum Likelihood Estimates
Parameter
Taking
medication
status
Skin and nail
infection DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Etanercept 4 Mod 1 1.1722 0.4277 7.5118 0.0061
Etanercept 4 Severe 1 1.4312 0.5565 6.6133 0.0101
Etanercept currently taking Mild 1 0.00155 0.0838 0.0003 0.9852
Etanercept currently taking Mod 1 -0.2081 0.0910 5.2237 0.0223
Etanercept currently taking Severe 1 -0.3476 0.1545 5.0621 0.0245
Etanercept never taking Mild 0 0 . . .
Etanercept never taking Mod 0 0 . . .
Etanercept never taking Severe 0 0 . . .
Infliximab 3 Mild 1 -0.1862 0.1367 1.8550 0.1732
Infliximab 3 Mod 1 0.2063 0.1283 2.5871 0.1077
Infliximab 3 Severe 1 0.4813 0.1875 6.5890 0.0103
Infliximab 4 Mild 1 0.4947 0.3630 1.8573 0.1729
Infliximab 4 Mod 1 0.8282 0.3319 6.2268 0.0126
Infliximab 4 Severe 1 1.2836 0.4326 8.8057 0.0030
Infliximab currently taking Mild 1 0.2213 0.1753 1.5940 0.2067
Infliximab currently taking Mod 1 -0.1129 0.2084 0.2935 0.5880
Infliximab currently taking Severe 1 0.5461 0.2665 4.1992 0.0404
Infliximab never taking Mild 0 0 . . .
Infliximab never taking Mod 0 0 . . .
Infliximab never taking Severe 0 0 . . .
Methotrexate and
Folic Acid
currently taking Mild 1 -0.3023 0.0732 17.0813 <.0001
Methotrexate and
Folic Acid
currently taking Mod 1 -0.1809 0.0780 5.3701 0.0205
Methotrexate and
Folic Acid
currently taking Severe 1 0.2144 0.1185 3.2733 0.0704
Methotrexate and
Folic Acid
never taking Mild 0 0 . . .
Methotrexate and
Folic Acid
never taking Mod 0 0 . . .
Page 323 of 577
Analysis of Maximum Likelihood Estimates
Parameter
Taking
medication
status
Skin and nail
infection DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Methotrexate and
Folic Acid
never taking Severe 0 0 . . .
Prednisolone 3 Mild 1 -0.1068 0.0946 1.2757 0.2587
Prednisolone 3 Mod 1 -0.0786 0.1018 0.5955 0.4403
Prednisolone 3 Severe 1 0.6423 0.2296 7.8244 0.0052
Prednisolone 4 Mild 1 0.8885 0.4683 3.5995 0.0578
Prednisolone 4 Mod 1 -0.2050 0.6427 0.1017 0.7498
Prednisolone 4 Severe 1 -0.4415 1.1782 0.1404 0.7079
Prednisolone currently taking Mild 1 0.1044 0.0911 1.3132 0.2518
Prednisolone currently taking Mod 1 0.0129 0.0995 0.0169 0.8967
Prednisolone currently taking Severe 1 0.9617 0.2236 18.4899 <.0001
Prednisolone never taking Mild 0 0 . . .
Prednisolone never taking Mod 0 0 . . .
Prednisolone never taking Severe 0 0 . . .
Rituximab 3 Mild 1 0.2088 0.1608 1.6855 0.1942
Rituximab 3 Mod 1 -0.2103 0.1883 1.2478 0.2640
Rituximab 3 Severe 1 -0.2355 0.2839 0.6882 0.4068
Rituximab 4 Mild 1 -0.5847 0.5650 1.0710 0.3007
Rituximab 4 Mod 1 -0.8432 0.5514 2.3389 0.1262
Rituximab 4 Severe 1 0.1096 0.5674 0.0373 0.8469
Rituximab currently taking Mild 1 -0.4992 0.1693 8.6962 0.0032
Rituximab currently taking Mod 1 -0.4075 0.1673 5.9319 0.0149
Rituximab currently taking Severe 1 -0.4535 0.2610 3.0184 0.0823
Rituximab never taking Mild 0 0 . . .
Rituximab never taking Mod 0 0 . . .
Rituximab never taking Severe 0 0 . . .
Sulphasalazine 3 Mild 1 0.0853 0.0636 1.8015 0.1795
Sulphasalazine 3 Mod 1 0.1456 0.0706 4.2466 0.0393
Sulphasalazine 3 Severe 1 0.3094 0.1209 6.5541 0.0105
Sulphasalazine 4 Mild 1 -0.0553 0.2821 0.0385 0.8445
Sulphasalazine 4 Mod 1 0.6406 0.2353 7.4089 0.0065
Page 324 of 577
Analysis of Maximum Likelihood Estimates
Parameter
Taking
medication
status
Skin and nail
infection DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Sulphasalazine 4 Severe 1 0.7439 0.3634 4.1916 0.0406
Sulphasalazine currently taking Mild 1 -0.3428 0.1158 8.7686 0.0031
Sulphasalazine currently taking Mod 1 -0.1098 0.1187 0.8565 0.3547
Sulphasalazine currently taking Severe 1 0.0885 0.1946 0.2068 0.6493
Sulphasalazine never taking Mild 0 0 . . .
Sulphasalazine never taking Mod 0 0 . . .
Sulphasalazine never taking Severe 0 0 . . .
Page 325 of 577
Table C.51- Odds ratio estimates in Skin and nail infection
Odds Ratio Estimates
Effect InfSkin
Point
Estimate
95% Wald
Confidence Limits
Etanercept 3 vs never taking Mild 0.983 0.837 1.154
Etanercept 3 vs never taking Mod 0.921 0.773 1.096
Etanercept 3 vs never taking Severe 0.902 0.682 1.192
Etanercept 4 vs never taking Mild 1.631 0.574 4.638
Etanercept 4 vs never taking Mod 3.229 1.396 7.467
Etanercept 4 vs never taking Severe 4.184 1.406 12.454
Etanercept currently taking vs never taking Mild 1.002 0.850 1.180
Etanercept currently taking vs never taking Mod 0.812 0.679 0.971
Etanercept currently taking vs never taking Severe 0.706 0.522 0.956
Adalimumab 3 vs never taking Mild 1.181 1.003 1.391
Adalimumab 3 vs never taking Mod 1.163 0.976 1.386
Adalimumab 3 vs never taking Severe 1.442 1.096 1.899
Adalimumab 4 vs never taking Mild 0.703 0.214 2.309
Adalimumab 4 vs never taking Mod 0.769 0.253 2.336
Adalimumab 4 vs never taking Severe 1.237 0.360 4.253
Adalimumab currently taking vs never taking Mild 1.248 1.061 1.468
Adalimumab currently taking vs never taking Mod 1.008 0.843 1.205
Adalimumab currently taking vs never taking Severe 0.919 0.676 1.249
Infliximab 3 vs never taking Mild 0.830 0.635 1.085
Infliximab 3 vs never taking Mod 1.229 0.956 1.580
Infliximab 3 vs never taking Severe 1.618 1.121 2.337
Infliximab 4 vs never taking Mild 1.640 0.805 3.341
Infliximab 4 vs never taking Mod 2.289 1.194 4.387
Infliximab 4 vs never taking Severe 3.610 1.546 8.427
Infliximab currently taking vs never taking Mild 1.248 0.885 1.759
Infliximab currently taking vs never taking Mod 0.893 0.594 1.344
Infliximab currently taking vs never taking Severe 1.727 1.024 2.911
Rituximab 3 vs never taking Mild 1.232 0.899 1.689
Rituximab 3 vs never taking Mod 0.810 0.560 1.172
Rituximab 3 vs never taking Severe 0.790 0.453 1.378
Page 326 of 577
Odds Ratio Estimates
Effect InfSkin
Point
Estimate
95% Wald
Confidence Limits
Rituximab 4 vs never taking Mild 0.557 0.184 1.687
Rituximab 4 vs never taking Mod 0.430 0.146 1.268
Rituximab 4 vs never taking Severe 1.116 0.367 3.393
Rituximab currently taking vs never taking Mild 0.607 0.436 0.846
Rituximab currently taking vs never taking Mod 0.665 0.479 0.924
Rituximab currently taking vs never taking Severe 0.635 0.381 1.060
Methotrexate and Folic Acid currently taking vs never
taking
Mild 0.739 0.640 0.853
Methotrexate and Folic Acid currently taking vs never
taking
Mod 0.835 0.716 0.972
Methotrexate and Folic Acid currently taking vs never
taking
Severe 1.239 0.982 1.563
Sulphasalazine 3 vs never taking Mild 1.089 0.961 1.234
Sulphasalazine 3 vs never taking Mod 1.157 1.007 1.328
Sulphasalazine 3 vs never taking Severe 1.363 1.075 1.727
Sulphasalazine 4 vs never taking Mild 0.946 0.544 1.645
Sulphasalazine 4 vs never taking Mod 1.898 1.196 3.010
Sulphasalazine 4 vs never taking Severe 2.104 1.032 4.289
Sulphasalazine currently taking vs never taking Mild 0.710 0.566 0.891
Sulphasalazine currently taking vs never taking Mod 0.896 0.710 1.131
Sulphasalazine currently taking vs never taking Severe 1.093 0.746 1.600
Arava (Leflunomide) 3 vs never taking Mild 1.049 0.891 1.236
Arava (Leflunomide) 3 vs never taking Mod 1.131 0.946 1.353
Arava (Leflunomide) 3 vs never taking Severe 0.891 0.664 1.194
Arava (Leflunomide) 4 vs never taking Mild 0.402 0.122 1.325
Arava (Leflunomide) 4 vs never taking Mod 1.241 0.563 2.734
Arava (Leflunomide) 4 vs never taking Severe 0.398 0.086 1.836
Arava (Leflunomide) currently taking vs never taking Mild 1.357 1.131 1.629
Arava (Leflunomide) currently taking vs never taking Mod 1.245 1.015 1.528
Arava (Leflunomide) currently taking vs never taking Severe 1.198 0.860 1.668
Prednisolone 3 vs never taking Mild 0.899 0.747 1.082
Prednisolone 3 vs never taking Mod 0.924 0.757 1.129
Page 327 of 577
Odds Ratio Estimates
Effect InfSkin
Point
Estimate
95% Wald
Confidence Limits
Prednisolone 3 vs never taking Severe 1.901 1.212 2.981
Prednisolone 4 vs never taking Mild 2.432 0.971 6.089
Prednisolone 4 vs never taking Mod 0.815 0.231 2.871
Prednisolone 4 vs never taking Severe 0.643 0.064 6.474
Prednisolone currently taking vs never taking Mild 1.110 0.929 1.327
Prednisolone currently taking vs never taking Mod 1.013 0.833 1.231
Prednisolone currently taking vs never taking Severe 2.616 1.688 4.055
Page 328 of 577
APPENDIX D: OUTPUT OF SAS FOR
ARTIFICIAL JOINT INFECTION
Table D.1- Complete statistics for Artificial Joint infection
Model Information
Data Set WORK.IMPORT2
Response Variable TB Infection TB Infection
Number of Response Levels 4
Model generalized logit
Optimization Technique Newton-Raphson
Table D.2- Observation status for Artificial Joint infection
Number of Observations Read 27711
Number of Observations Used 21506
Table D.3- response value for Artificial Joint infection
Response Profile
Ordered
Value TB Infection
Total
Frequency
1 1 1050
2 2 1829
3 3 406
4 4 18221
Page 329 of 577
Table D.4- Backward Elimination Procedure for ARTIFICIAL JOINT infection
Logits modelled use TB Infection='4' as the reference category.
Note: 6205 observations were deleted due to missing values
for the response or explanatory variables.
Backward Elimination Procedure
Class Level Information
Class Value Design Variables
Etanercept 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Adalimumab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Anakinra 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Infliximab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Rituximab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Abatacept 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Tocilizumab 3 1 0 0 0
4 0 1 0 0
Page 330 of 577
Class Level Information
Class Value Design Variables
currently taking 0 0 1 0
never taking 0 0 0 1
Golimumab 3 1 0 0
currently taking 0 1 0
never taking 0 0 1
Certolizumab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Folic Acid currently taking 1 0
never taking 0 1
Hydroxychloroquine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Sulphasalazine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Arava (Leflunomide) 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Azathioprine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Cyclosporin 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Prednisolone 3 1 0 0 0
Page 331 of 577
Class Level Information
Class Value Design Variables
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
IM Gold injection 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Penicillamine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Page 332 of 577
Table D.5- Model Convergence status for ARTIFICIAL JOINT infection
Step 0. The following effects were entered:
Intercept Etanercept Adalimumab Anakinra Infliximab Rituximab Abatacept Tocilizumab
Golimumab Certolizumab Folic Acid Hydroxychloroquine Sulphasalazine Arava
(Leflunomide) Azathioprine Cyclosporin Prednisolone IM Gold injection Penicillamine
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table D.6- Model Fit statistics for ARTIFICIAL JOINT infection
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 24626.355 24501.128
SC 24650.284 25745.398
-2 Log L 24620.355 24189.128
Table D.7- Testing null hypothesis for ARTIFICIAL JOINT infection
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 431.2272 153 <.0001
Page 333 of 577
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Score 463.0664 153 <.0001
Wald 419.5882 153 <.0001
Table D.8- Model Fit statistics for removing covariant step 1
Step 1. Effect Azathioprine is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table D.9- Model Fit statistics for removing covariant step 1
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 24626.355 24488.566
SC 24650.284 25661.051
-2 Log L 24620.355 24194.566
Table D.10- Testing Null hypothesis after removing covariant step 1
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 425.7897 144 <.0001
Score 457.8861 144 <.0001
Wald 415.1007 144 <.0001
Table D.11- Residual removing covariant step 1
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
5.0524 9 0.8297
Page 334 of 577
Table D.12- Model Fit statistics for removing covariant step 2
Step 2. Effect Certolizumab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table D.13- Model Fit statistics after removing covariant step 2
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 24626.355 24476.658
SC 24650.284 25577.358
-2 Log L 24620.355 24200.658
Table D.14- Testing Null hypothesis after removing covariant step 2
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 419.6974 135 <.0001
Score 450.9468 135 <.0001
Wald 408.7712 135 <.0001
Page 335 of 577
Table D.15- Residual removing covariant step 2
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
10.9161 18 0.8979
Table D.16- Model Fit statistics for removing covariant step 3
Step 3. Effect Penicillamine is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table D.17- Model Fit statistics after removing covariant step 3
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 24626.355 24473.673
SC 24650.284 25502.589
-2 Log L 24620.355 24215.673
Table D.18- Testing Null hypothesis after removing covariant step 3
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 404.6821 126 <.0001
Score 440.0301 126 <.0001
Wald 401.9656 126 <.0001
Page 336 of 577
Table D.19- Residual removing covariant step 3
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
22.0077 27 0.7370
Table D.20- Model Fit statistics for removing covariant step 4
Step 4. Effect IM Gold injection is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table D.21- Model Fit statistics after removing covariant step 4
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 24626.355 24465.880
SC 24650.284 25423.011
-2 Log L 24620.355 24225.880
Table D.22- Testing Null hypothesis after removing covariant step 4
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 394.4751 117 <.0001
Page 337 of 577
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Score 430.4313 117 <.0001
Wald 392.2553 117 <.0001
Table D.23- Residual removing covariant step 4
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
31.2787 36 0.6926
Table D.24- Model Fit statistics for removing covariant step 5
Step 5. Effect Rituximab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table D.25- Model Fit statistics after removing covariant step 5
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 24626.355 24461.305
SC 24650.284 25346.650
-2 Log L 24620.355 24239.305
Page 338 of 577
Table D.26- Testing Null hypothesis after removing covariant step 5
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 381.0509 108 <.0001
Score 415.9895 108 <.0001
Wald 378.4582 108 <.0001
Table D.27- Residual removing covariant step 5
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
44.9975 45 0.4721
Table D.28- Model Fit statistics for removing covariant step 6
Step 6. Effect Golimumab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Page 339 of 577
Table D.29- Model Fit statistics after removing covariant step 6
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 24626.355 24462.511
SC 24650.284 25300.000
-2 Log L 24620.355 24252.511
Table D.30- Testing Null hypothesis after removing covariant step 6
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 367.8443 102 <.0001
Score 403.4935 102 <.0001
Wald 366.8141 102 <.0001
Table D.31- Residual removing covariant step 7
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
57.2672 51 0.2539
Note: No (additional) effects met the 0.05 significance level for removal from the model.
Page 340 of 577
Table D.32- Summary of backward elimination in ARTIFICIAL JOINT
Summary of Backward Elimination
Step
Effect
Removed DF
Number
In
Wald
Chi-Square Pr > ChiSq
Variable
Label
1 Azathioprine 9 17 4.9893 0.8352 Azathioprine
2 Certolizumab 9 16 5.4537 0.7931 Certolizumab
3 Penicillamine 9 15 7.1956 0.6168 Penicillamine
4 IM Gold injection 9 14 9.1915 0.4198 IM Gold injection
5 Rituximab 9 13 13.6536 0.1352 Rituximab
6 Golimumab 6 12 11.2165 0.0819 Golimumab
Table D.33- Type 3 analysis of effects in ARTIFICIAL JOINT
Type 3 Analysis of Effects
Effect DF
Wald
Chi-Square Pr > ChiSq
Etanercept 9 52.1431 <.0001
Adalimumab 9 22.4139 0.0077
Anakinra 9 18.2690 0.0322
Infliximab 9 31.0160 0.0003
Abatacept 9 18.0153 0.0350
Tocilizumab 9 18.1032 0.0340
Folic Acid 3 9.4165 0.0242
Hydroxychloroquine 9 23.3663 0.0054
Sulphasalazine 9 26.7402 0.0015
Arava (Leflunomide) 9 17.5339 0.0410
Cyclosporin 9 47.3358 <.0001
Prednisolone 9 29.4764 0.0005
Page 341 of 577
Table D.34- Analysis of maximum likelihood estimates in ARTIFICIAL JOINT
Analysis of Maximum Likelihood Estimates
Parameter
TB
Infection DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Intercept 1 1 -3.4872 0.1190 859.2759 <.0001
Intercept 2 1 -2.9786 0.0928 1031.0501 <.0001
Intercept 3 1 -4.3609 0.1917 517.2695 <.0001
Etanercept 3 1 1 -0.0509 0.0911 0.3118 0.5766
Etanercept 3 2 1 -0.0713 0.0705 1.0220 0.3120
Etanercept 3 3 1 -0.3981 0.1457 7.4653 0.0063
Etanercept 4 1 1 1.3033 0.5444 5.7307 0.0167
Etanercept 4 2 1 1.9227 0.3431 31.3968 <.0001
Etanercept 4 3 1 1.3439 0.8633 2.4234 0.1195
Etanercept currently
taking
1 1 0.1730 0.0941 3.3831 0.0659
Etanercept currently
taking
2 1 0.0891 0.0722 1.5232 0.2171
Etanercept currently
taking
3 1 -0.3383 0.1446 5.4736 0.0193
Etanercept never taking 1 0 0 . . .
Etanercept never taking 2 0 0 . . .
Etanercept never taking 3 0 0 . . .
Adalimumab 3 1 1 0.0104 0.0914 0.0129 0.9094
Adalimumab 3 2 1 0.1823 0.0686 7.0504 0.0079
Adalimumab 3 3 1 0.1418 0.1403 1.0222 0.3120
Adalimumab 4 1 1 -0.5402 0.6756 0.6394 0.4239
Adalimumab 4 2 1 -
0.00090
0.4440 0.0000 0.9984
Adalimumab 4 3 1 -
10.2462
147.6 0.0048 0.9447
Adalimumab currently
taking
1 1 0.2887 0.0941 9.4206 0.0021
Adalimumab currently
taking
2 1 0.1847 0.0737 6.2813 0.0122
Page 342 of 577
Analysis of Maximum Likelihood Estimates
Parameter
TB
Infection DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Adalimumab currently
taking
3 1 -0.0798 0.1470 0.2946 0.5873
Adalimumab never taking 1 0 0 . . .
Adalimumab never taking 2 0 0 . . .
Adalimumab never taking 3 0 0 . . .
Anakinra 3 1 1 0.1448 0.2523 0.3295 0.5659
Anakinra 3 2 1 -0.0761 0.2191 0.1205 0.7285
Anakinra 3 3 1 0.4597 0.3413 1.8146 0.1780
Anakinra 4 1 1 -0.7187 0.6297 1.3026 0.2537
Anakinra 4 2 1 0.0275 0.4047 0.0046 0.9459
Anakinra 4 3 1 -0.4484 1.0513 0.1819 0.6697
Anakinra currently
taking
1 1 -
11.9697
512.1 0.0005 0.9814
Anakinra currently
taking
2 1 1.7999 0.4745 14.3901 0.0001
Anakinra currently
taking
3 1 -
12.2422
809.5 0.0002 0.9879
Anakinra never taking 1 0 0 . . .
Anakinra never taking 2 0 0 . . .
Anakinra never taking 3 0 0 . . .
Infliximab 3 1 1 0.0552 0.1337 0.1707 0.6795
Infliximab 3 2 1 -0.2055 0.1098 3.5047 0.0612
Infliximab 3 3 1 0.0478 0.1974 0.0585 0.8088
Infliximab 4 1 1 0.4422 0.4291 1.0621 0.3027
Infliximab 4 2 1 -0.1974 0.3745 0.2779 0.5981
Infliximab 4 3 1 -0.8062 0.8952 0.8110 0.3678
Infliximab currently
taking
1 1 0.6440 0.1747 13.5909 0.0002
Infliximab currently
taking
2 1 0.4727 0.1396 11.4614 0.0007
Page 343 of 577
Analysis of Maximum Likelihood Estimates
Parameter
TB
Infection DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Infliximab currently
taking
3 1 -0.3706 0.3711 0.9971 0.3180
Infliximab never taking 1 0 0 . . .
Infliximab never taking 2 0 0 . . .
Infliximab never taking 3 0 0 . . .
Abatacept 3 1 1 0.5166 0.1769 8.5260 0.0035
Abatacept 3 2 1 0.1147 0.1534 0.5587 0.4548
Abatacept 3 3 1 -0.4339 0.3573 1.4747 0.2246
Abatacept 4 1 1 0.2751 0.8455 0.1059 0.7449
Abatacept 4 2 1 -0.6022 0.6388 0.8887 0.3458
Abatacept 4 3 1 1.3251 1.0615 1.5582 0.2119
Abatacept currently
taking
1 1 0.3362 0.1582 4.5176 0.0335
Abatacept currently
taking
2 1 0.1491 0.1240 1.4460 0.2292
Abatacept currently
taking
3 1 -0.2016 0.2673 0.5686 0.4508
Abatacept never taking 1 0 0 . . .
Abatacept never taking 2 0 0 . . .
Abatacept never taking 3 0 0 . . .
Tocilizumab 3 1 1 0.1595 0.2454 0.4224 0.5157
Tocilizumab 3 2 1 0.1835 0.1951 0.8847 0.3469
Tocilizumab 3 3 1 0.7127 0.3269 4.7534 0.0292
Tocilizumab 4 1 1 -
11.4739
529.0 0.0005 0.9827
Tocilizumab 4 2 1 -
11.5154
222.6 0.0027 0.9587
Tocilizumab 4 3 1 -
10.9097
820.5 0.0002 0.9894
Tocilizumab currently
taking
1 1 0.4933 0.1695 8.4682 0.0036
Page 344 of 577
Analysis of Maximum Likelihood Estimates
Parameter
TB
Infection DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Tocilizumab currently
taking
2 1 0.3301 0.1348 5.9962 0.0143
Tocilizumab currently
taking
3 1 0.1795 0.2814 0.4069 0.5236
Tocilizumab never taking 1 0 0 . . .
Tocilizumab never taking 2 0 0 . . .
Tocilizumab never taking 3 0 0 . . .
Folic Acid currently
taking
1 1 -0.1059 0.0761 1.9365 0.1641
Folic Acid currently
taking
2 1 -0.1683 0.0598 7.9220 0.0049
Folic Acid currently
taking
3 1 -0.0493 0.1190 0.1713 0.6789
Folic Acid never taking 1 0 0 . . .
Folic Acid never taking 2 0 0 . . .
Folic Acid never taking 3 0 0 . . .
Hydroxychloroquine 3 1 1 0.1431 0.0736 3.7794 0.0519
Hydroxychloroquine 3 2 1 0.2299 0.0575 15.9873 <.0001
Hydroxychloroquine 3 3 1 0.1860 0.1175 2.5057 0.1134
Hydroxychloroquine 4 1 1 -0.0695 0.4165 0.0278 0.8676
Hydroxychloroquine 4 2 1 -0.0273 0.3338 0.0067 0.9348
Hydroxychloroquine 4 3 1 0.6305 0.5074 1.5444 0.2140
Hydroxychloroquine currently
taking
1 1 0.0100 0.0960 0.0109 0.9168
Hydroxychloroquine currently
taking
2 1 0.0789 0.0753 1.0991 0.2945
Hydroxychloroquine currently
taking
3 1 0.0332 0.1546 0.0463 0.8297
Hydroxychloroquine never taking 1 0 0 . . .
Hydroxychloroquine never taking 2 0 0 . . .
Hydroxychloroquine never taking 3 0 0 . . .
Page 345 of 577
Analysis of Maximum Likelihood Estimates
Parameter
TB
Infection DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Sulphasalazine 3 1 1 0.0933 0.0714 1.7093 0.1911
Sulphasalazine 3 2 1 0.2229 0.0554 16.1883 <.0001
Sulphasalazine 3 3 1 0.1577 0.1136 1.9284 0.1649
Sulphasalazine 4 1 1 0.1273 0.3002 0.1799 0.6714
Sulphasalazine 4 2 1 -0.2181 0.2582 0.7132 0.3984
Sulphasalazine 4 3 1 0.6855 0.3912 3.0697 0.0798
Sulphasalazine currently
taking
1 1 0.1470 0.1112 1.7468 0.1863
Sulphasalazine currently
taking
2 1 0.00403 0.0926 0.0019 0.9653
Sulphasalazine currently
taking
3 1 -0.0445 0.1896 0.0551 0.8144
Sulphasalazine never taking 1 0 0 . . .
Sulphasalazine never taking 2 0 0 . . .
Sulphasalazine never taking 3 0 0 . . .
Arava (Leflunomide) 3 1 1 0.1098 0.0935 1.3804 0.2400
Arava (Leflunomide) 3 2 1 0.1933 0.0729 7.0343 0.0080
Arava (Leflunomide) 3 3 1 0.1484 0.1434 1.0712 0.3007
Arava (Leflunomide) 4 1 1 -0.2856 0.5428 0.2768 0.5988
Arava (Leflunomide) 4 2 1 0.4582 0.3091 2.1967 0.1383
Arava (Leflunomide) 4 3 1 -0.7134 1.0581 0.4546 0.5002
Arava (Leflunomide) currently
taking
1 1 0.2705 0.1060 6.5075 0.0107
Arava (Leflunomide) currently
taking
2 1 0.1492 0.0858 3.0250 0.0820
Arava (Leflunomide) currently
taking
3 1 0.00639 0.1726 0.0014 0.9705
Arava (Leflunomide) never taking 1 0 0 . . .
Arava (Leflunomide) never taking 2 0 0 . . .
Arava (Leflunomide) never taking 3 0 0 . . .
Cyclosporin 3 1 1 0.0263 0.0937 0.0789 0.7788
Page 346 of 577
Analysis of Maximum Likelihood Estimates
Parameter
TB
Infection DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Cyclosporin 3 2 1 0.2042 0.0692 8.7084 0.0032
Cyclosporin 3 3 1 0.4662 0.1325 12.3789 0.0004
Cyclosporin 4 1 1 -0.2418 0.3214 0.5662 0.4518
Cyclosporin 4 2 1 0.0673 0.2205 0.0931 0.7603
Cyclosporin 4 3 1 -1.0526 0.7303 2.0770 0.1495
Cyclosporin currently
taking
1 1 0.5290 0.3373 2.4603 0.1168
Cyclosporin currently
taking
2 1 1.0439 0.2236 21.7983 <.0001
Cyclosporin currently
taking
3 1 1.0216 0.4398 5.3965 0.0202
Cyclosporin never taking 1 0 0 . . .
Cyclosporin never taking 2 0 0 . . .
Cyclosporin never taking 3 0 0 . . .
Prednisolone 3 1 1 0.3310 0.1083 9.3359 0.0022
Prednisolone 3 2 1 0.2552 0.0838 9.2693 0.0023
Prednisolone 3 3 1 0.4980 0.1834 7.3738 0.0066
Prednisolone 4 1 1 0.7838 0.5610 1.9520 0.1624
Prednisolone 4 2 1 0.5466 0.4327 1.5961 0.2065
Prednisolone 4 3 1 0.7162 1.0389 0.4753 0.4906
Prednisolone currently
taking
1 1 0.1671 0.1087 2.3642 0.1241
Prednisolone currently
taking
2 1 0.1308 0.0838 2.4345 0.1187
Prednisolone currently
taking
3 1 0.3911 0.1833 4.5509 0.0329
Prednisolone never taking 1 0 0 . . .
Prednisolone never taking 2 0 0 . . .
Prednisolone never taking 3 0 0 . . .
Page 347 of 577
Table D.35- Odds ratio estimates in ARTIFICIAL JOINT
Odds Ratio Estimates
Effect
TB
Infection
Point
Estimate
95% Wald
Confidence Limits
Etanercept 3 vs never taking 1 0.950 0.795 1.136
Etanercept 3 vs never taking 2 0.931 0.811 1.069
Etanercept 3 vs never taking 3 0.672 0.505 0.894
Etanercept 4 vs never taking 1 3.682 1.266 10.702
Etanercept 4 vs never taking 2 6.840 3.491 13.400
Etanercept 4 vs never taking 3 3.834 0.706 20.819
Etanercept currently taking vs never taking 1 1.189 0.989 1.430
Etanercept currently taking vs never taking 2 1.093 0.949 1.259
Etanercept currently taking vs never taking 3 0.713 0.537 0.947
Adalimumab 3 vs never taking 1 1.010 0.845 1.209
Adalimumab 3 vs never taking 2 1.200 1.049 1.373
Adalimumab 3 vs never taking 3 1.152 0.875 1.517
Adalimumab 4 vs never taking 1 0.583 0.155 2.190
Adalimumab 4 vs never taking 2 0.999 0.419 2.385
Adalimumab 4 vs never taking 3 <0.001 <0.001 >999.999
Adalimumab currently taking vs never taking 1 1.335 1.110 1.605
Adalimumab currently taking vs never taking 2 1.203 1.041 1.390
Adalimumab currently taking vs never taking 3 0.923 0.692 1.232
Anakinra 3 vs never taking 1 1.156 0.705 1.895
Anakinra 3 vs never taking 2 0.927 0.603 1.424
Anakinra 3 vs never taking 3 1.584 0.811 3.091
Anakinra 4 vs never taking 1 0.487 0.142 1.675
Anakinra 4 vs never taking 2 1.028 0.465 2.272
Anakinra 4 vs never taking 3 0.639 0.081 5.013
Anakinra currently taking vs never taking 1 <0.001 <0.001 >999.999
Anakinra currently taking vs never taking 2 6.049 2.387 15.330
Anakinra currently taking vs never taking 3 <0.001 <0.001 >999.999
Infliximab 3 vs never taking 1 1.057 0.813 1.373
Infliximab 3 vs never taking 2 0.814 0.657 1.010
Infliximab 3 vs never taking 3 1.049 0.712 1.544
Page 348 of 577
Odds Ratio Estimates
Effect
TB
Infection
Point
Estimate
95% Wald
Confidence Limits
Infliximab 4 vs never taking 1 1.556 0.671 3.608
Infliximab 4 vs never taking 2 0.821 0.394 1.710
Infliximab 4 vs never taking 3 0.447 0.077 2.582
Infliximab currently taking vs never taking 1 1.904 1.352 2.682
Infliximab currently taking vs never taking 2 1.604 1.220 2.109
Infliximab currently taking vs never taking 3 0.690 0.334 1.429
Abatacept 3 vs never taking 1 1.676 1.185 2.371
Abatacept 3 vs never taking 2 1.122 0.830 1.515
Abatacept 3 vs never taking 3 0.648 0.322 1.305
Abatacept 4 vs never taking 1 1.317 0.251 6.905
Abatacept 4 vs never taking 2 0.548 0.157 1.915
Abatacept 4 vs never taking 3 3.763 0.470 30.134
Abatacept currently taking vs never taking 1 1.400 1.027 1.908
Abatacept currently taking vs never taking 2 1.161 0.910 1.480
Abatacept currently taking vs never taking 3 0.817 0.484 1.380
Tocilizumab 3 vs never taking 1 1.173 0.725 1.897
Tocilizumab 3 vs never taking 2 1.201 0.820 1.761
Tocilizumab 3 vs never taking 3 2.039 1.075 3.870
Tocilizumab 4 vs never taking 1 <0.001 <0.001 >999.999
Tocilizumab 4 vs never taking 2 <0.001 <0.001 >999.999
Tocilizumab 4 vs never taking 3 <0.001 <0.001 >999.999
Tocilizumab currently taking vs never taking 1 1.638 1.175 2.283
Tocilizumab currently taking vs never taking 2 1.391 1.068 1.812
Tocilizumab currently taking vs never taking 3 1.197 0.689 2.077
Folic Acid currently taking vs never taking 1 0.899 0.775 1.044
Folic Acid currently taking vs never taking 2 0.845 0.752 0.950
Folic Acid currently taking vs never taking 3 0.952 0.754 1.202
Hydroxychloroquine 3 vs never taking 1 1.154 0.999 1.333
Hydroxychloroquine 3 vs never taking 2 1.259 1.124 1.409
Hydroxychloroquine 3 vs never taking 3 1.204 0.957 1.516
Hydroxychloroquine 4 vs never taking 1 0.933 0.412 2.110
Hydroxychloroquine 4 vs never taking 2 0.973 0.506 1.872
Page 349 of 577
Odds Ratio Estimates
Effect
TB
Infection
Point
Estimate
95% Wald
Confidence Limits
Hydroxychloroquine 4 vs never taking 3 1.879 0.695 5.078
Hydroxychloroquine currently taking vs never
taking
1 1.010 0.837 1.219
Hydroxychloroquine currently taking vs never
taking
2 1.082 0.934 1.254
Hydroxychloroquine currently taking vs never
taking
3 1.034 0.764 1.400
Sulphasalazine 3 vs never taking 1 1.098 0.955 1.263
Sulphasalazine 3 vs never taking 2 1.250 1.121 1.393
Sulphasalazine 3 vs never taking 3 1.171 0.937 1.463
Sulphasalazine 4 vs never taking 1 1.136 0.631 2.046
Sulphasalazine 4 vs never taking 2 0.804 0.485 1.334
Sulphasalazine 4 vs never taking 3 1.985 0.922 4.273
Sulphasalazine currently taking vs never taking 1 1.158 0.931 1.440
Sulphasalazine currently taking vs never taking 2 1.004 0.837 1.204
Sulphasalazine currently taking vs never taking 3 0.956 0.660 1.387
Arava (Leflunomide) 3 vs never taking 1 1.116 0.929 1.341
Arava (Leflunomide) 3 vs never taking 2 1.213 1.052 1.399
Arava (Leflunomide) 3 vs never taking 3 1.160 0.876 1.536
Arava (Leflunomide) 4 vs never taking 1 0.752 0.259 2.178
Arava (Leflunomide) 4 vs never taking 2 1.581 0.863 2.898
Arava (Leflunomide) 4 vs never taking 3 0.490 0.062 3.898
Arava (Leflunomide) currently taking vs never
taking
1 1.311 1.065 1.613
Arava (Leflunomide) currently taking vs never
taking
2 1.161 0.981 1.374
Arava (Leflunomide) currently taking vs never
taking
3 1.006 0.717 1.412
Cyclosporin 3 vs never taking 1 1.027 0.854 1.234
Cyclosporin 3 vs never taking 2 1.227 1.071 1.405
Cyclosporin 3 vs never taking 3 1.594 1.229 2.066
Cyclosporin 4 vs never taking 1 0.785 0.418 1.474
Page 350 of 577
Odds Ratio Estimates
Effect
TB
Infection
Point
Estimate
95% Wald
Confidence Limits
Cyclosporin 4 vs never taking 2 1.070 0.694 1.648
Cyclosporin 4 vs never taking 3 0.349 0.083 1.461
Cyclosporin currently taking vs never taking 1 1.697 0.876 3.287
Cyclosporin currently taking vs never taking 2 2.840 1.833 4.403
Cyclosporin currently taking vs never taking 3 2.778 1.173 6.577
Prednisolone 3 vs never taking 1 1.392 1.126 1.722
Prednisolone 3 vs never taking 2 1.291 1.095 1.521
Prednisolone 3 vs never taking 3 1.645 1.149 2.357
Prednisolone 4 vs never taking 1 2.190 0.729 6.576
Prednisolone 4 vs never taking 2 1.727 0.740 4.034
Prednisolone 4 vs never taking 3 2.047 0.267 15.680
Prednisolone currently taking vs never taking 1 1.182 0.955 1.462
Prednisolone currently taking vs never taking 2 1.140 0.967 1.343
Prednisolone currently taking vs never taking 3 1.479 1.032 2.118
Page 351 of 577
APPENDIX E: OUTPUT OF SAS FOR
BONE MUSCLE JOINT INFECTION
Table E.1- Complete statistics for bone muscle joint infection.
Model Information
Data Set WORK.IMPORT2
Response Variable InfBone, Joint and muscle InfBone, Joint and muscle
Number of Response Levels 4
Model generalized logit
Optimization Technique Newton-Raphson
Table E.2- Observation status for BONE MUSCLE JOINT infection
Number of Observations Read 27711
Number of Observations Used 21506
Page 352 of 577
Table E.3- response value for BONE MUSCLE JOINT infection
Response Profile
Ordered
Value Bone/Joint/Muscle infection
Total
Frequency
1 1 82
2 2 213
3 3 243
4 4 20968
Table E.4- Backward Elimination Procedure for bone muscle joint infection
Class Level Information
Class Value Design Variables
Etanercept 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
Adalimumab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
Anakinra 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
Page 353 of 577
Class Level Information
Class Value Design Variables
Infliximab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
Rituximab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
Abatacept 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
Tocilizumab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
Golimumab 3 1 0 0
currently taking 0 1 0
b never taking 0 0 1
Methotrexate 1 1 0 0 0
2 0 1 0 0
3 0 0 1 0
4 0 0 0 1
Page 354 of 577
Class Level Information
Class Value Design Variables
Certolizumab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
Methotrexate (plus Folic acid) currently taking 1 0
b never taking 0 1
Hydroxychloroquine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
Sulphasalazine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
Arava (Leflunomide) 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
Azathioprine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
Page 355 of 577
Class Level Information
Class Value Design Variables
Cyclosporin 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
Prednisolone 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
IM Gold 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
Penicillamine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
b never taking 0 0 0 1
Step 0. The following effects were entered:
Intercept Etanercept Adalimumab Anakinra Infliximab Rituximab Abatacept Tocilizumab
Golimumab Methotrexate Certolizumab Methotrexate(plus Folic acid) Hydroxychloroquine
Sulphasalazine Arava (Leflunomide) Azathioprine Cyclosporin Prednisolone IM Gold
Penicillamine
Page 356 of 577
Table E.5- Model Convergence status for BONE MUSCLE JOINT infection
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table E.6- Model Fit statistics for BONE MUSCLE JOINT infection
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 6126.457 6158.903
SC 6150.385 7474.957
-2 Log L 6120.457 5828.903
Table E.7- Testing null hypothesis for BONE MUSCLE JOINT infection
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 291.5546 162 <.0001
Score 316.8699 162 <.0001
Wald 282.4093 162 <.0001
Page 357 of 577
Table E.8- Model Fit statistics for removing covariant step 1
Step 1. Effect Certolizumab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table E.9- Model Fit statistics for removing covariant step 1
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 6126.457 6146.339
SC 6150.385 7390.609
-2 Log L 6120.457 5834.339
Table E.10- Testing Null hypothesis after removing covariant step 1
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 286.1178 153 <.0001
Score 313.7027 153 <.0001
Wald 282.2026 153 <.0001
Page 358 of 577
Table E.11- Residual removing covariant step 1
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
3.7370 9 0.9279
Table E.12- Model Fit statistics for removing covariant step 2
Step 2. Effect Azathioprine is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table E.13- Model Fit statistics after removing covariant step 2
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 6126.457 6132.503
SC 6150.385 7304.988
-2 Log L 6120.457 5838.503
Table E.14- Testing Null hypothesis after removing covariant step 2
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 281.9542 144 <.0001
Page 359 of 577
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Score 307.4486 144 <.0001
Wald 277.8842 144 <.0001
Table E.15- Residual removing covariant step 2
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
7.6575 18 0.9833
Table E.16- Model Fit statistics for removing covariant step 3
Step 3. Effect Anakinra is removed
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table E.17- Model Fit statistics after removing covariant step 3
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 6126.457 6119.824
SC 6150.385 7220.524
-2 Log L 6120.457 5843.824
Table E.18- Testing Null hypothesis after removing covariant step 3
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 276.6335 135 <.0001
Score 299.1248 135 <.0001
Wald 271.6162 135 <.0001
Page 360 of 577
Table E.19- Residual removing covariant step 3
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
12.9434 27 0.9896
Table E.20- Model Fit statistics for removing covariant step 4
Step 4. Effect IM Golimumab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Page 361 of 577
Table E.21- Model Fit statistics after removing covariant step 4
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 6126.457 6111.659
SC 6150.385 7164.502
-2 Log L 6120.457 5847.659
Table E.22- Testing Null hypothesis after removing covariant step 4
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 272.7986 129 <.0001
Score 295.4929 129 <.0001
Wald 268.3543 129 <.0001
Table E.23- Residual removing covariant step 4
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
16.2276 33 0.9937
Page 362 of 577
Table E.24- Model Fit statistics for removing covariant step 5
Step 5. Effect Tocilizumab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table E.25- Model Fit statistics after removing covariant step 5
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 6126.457 6103.307
SC 6150.385 7084.366
-2 Log L 6120.457 5857.307
Table E.26- Testing Null hypothesis after removing covariant step 5
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 263.1498 120 <.0001
Score 289.4733 120 <.0001
Wald 265.8322 120 <.0001
Page 363 of 577
Table E.27- Residual removing covariant step 5
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
24.2774 42 0.9870
Table E.28- Model Fit statistics for removing covariant step 6
Step 6. Effect Cyclosporin is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table E.29- Model Fit statistics after removing covariant step 6
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 6126.457 6090.488
SC 6150.385 6999.762
-2 Log L 6120.457 5862.488
Page 364 of 577
Table E.30- Testing Null hypothesis after removing covariant step 6
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 257.9689 111 <.0001
Score 281.3060 111 <.0001
Wald 258.4273 111 <.0001
Table E.31- Residual removing covariant step 7
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
31.0446 51 0.9877
Note: No (additional) effects met the 0.05 significance level for removal from the model.
Table E.32- Model Fit statistics for removing covariant step 7
Step 7. Effect Methotrexate is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is
questionable.
The LOGISTIC Procedure WARNING: The validity of the model fit is questionable.
Saturday, 14 December 2019 07:51:16 PM 365Table E.33- Model Fit statistics after removing covariant
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 6126.457 6081.126
SC 6150.385 6918.615
-2 Log L 6120.457 5871.126
Table E.34- Testing Null hypothesis after removing covariant
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 249.3310 102 <.0001
Score 269.9568 102 <.0001
Wald 249.6230 102 <.0001
Table E.35- Residual removing covariant
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
40.0344 60 0.9780
Table E.36- Model Fit statistics for removing covariant
The LOGISTIC Procedure WARNING: The validity of the model fit is questionable.
Saturday, 14 December 2019 07:51:16 PM 366Step 8. Effect Rituximab is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is
questionable.
Table E.37- Model Fit statistics after removing covariant
367
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 6126.457 6074.207
SC 6150.385 6839.912
-2 Log L 6120.457 5882.207
Table E.38- Testing Null hypothesis after removing covariant
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 238.2500 93 <.0001
Score 258.7575 93 <.0001
Wald 237.4385 93 <.0001
Table E.39- Residual removing covariant
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
53.0612 69 0.9222
Table E.40- Model Fit statistics for removing covariant
Step 9. Effect Abatacept is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
368
Table E.41- Model Fit statistics after removing covariant
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 6126.457 6069.572
SC 6150.385 6763.492
-2 Log L 6120.457 5895.572
Table E.42- Testing Null hypothesis after removing covariant
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 224.8848 84 <.0001
Score 244.5347 84 <.0001
Wald 226.2461 84 <.0001
Table E.43- Residual removing covariant
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
65.8098 78 0.8359
Table E.44- Model Fit statistics for removing covariant
Step 10. Effect Sulphasalazine is removed:
369
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table E.45- Model Fit statistics after removing covariant
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 6126.457 6067.034
SC 6150.385 6689.169
-2 Log L 6120.457 5911.034
Table E.46- Testing Null hypothesis after removing covariant
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 209.4231 75 <.0001
Score 229.3403 75 <.0001
Wald 210.2999 75 <.0001
Table E.47- Residual removing covariant
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
81.1596 87 0.6562
Table E.48- Model Fit statistics for removing covariant
370
Step 11. Effect Etanercept is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table E.49- Model Fit statistics after removing covariant
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 6126.457 6065.927
SC 6150.385 6616.277
-2 Log L 6120.457 5927.927
Table E.50- Testing Null hypothesis after removing covariant
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 192.5302 66 <.0001
Score 213.8622 66 <.0001
Wald 195.0069 66 <.0001
Table E.51- Residual removing covariant
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
96.0334 96 0.4798
Table E.52- Model Fit statistics for removing covariant
371
Step 12. Effect Adalimumab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table E.53- Model Fit statistics after removing covariant
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 6126.457 6065.506
SC 6150.385 6544.071
-2 Log L 6120.457 5945.506
Table E.54- Testing Null hypothesis after removing covariant
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 174.9515 57 <.0001
Score 191.8185 57 <.0001
Wald 176.7190 57 <.0001
Table E.55- Residual removing covariant
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
110.9740 105 0.3262
372
Table E.56- Model Fit statistics for removing covariant
Step 13. Effect IM Gold is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table E.57- Model Fit statistics after removing covariant
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 6126.457 6063.434
SC 6150.385 6470.215
-2 Log L 6120.457 5961.434
Table E.58- Testing Null hypothesis after removing covariant
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 159.0230 48 <.0001
Score 174.2918 48 <.0001
Wald 161.1437 48 <.0001
Table E.59- Residual removing covariant
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
129.3306 114 0.1546
373
Note: No (additional) effects met the 0.05 significance level for removal from the model.
Table E.60- Summary of backward elimination in bone muscle joint
374
Summary of Backward Elimination
Step
Effect
Removed DF
Number
In
Wald
Chi-Square Pr > ChiSq
Variable
Label
1 Certolizumab 9 18 1.6031 0.9963 Certolizumab
2 Azathioprine 9 17 3.8457 0.9213 Azathioprine
3 Anakinra 9 16 4.3211 0.8890
4 Golimumab 6 15 3.0321 0.8048 Golimumab
5 Tocilizumab 9 14 5.4188 0.7964 Tocilizumab
6 Cyclosporin 9 13 6.0931 0.7306 Cyclosporin
7 Methotrexate 9 12 8.0079 0.5334 Methotrexate
8 Rituximab 9 11 10.8068 0.2892 Rituximab
9 Abatacept 9 10 11.4004 0.2493 Abatacept
10 Sulphasalazine 9 9 14.0763 0.1196 Sulphasalazine
11 Etanercept 9 8 15.3543 0.0817
12 Adalimumab 9 7 15.0322 0.0901
13 IM Gold 9 6 16.1101 0.0646 IM Gold
Table E.61- Type 3 analysis of effects in BONE MUSCLE JOINT
Type 3 Analysis of Effects
Effect DF
Wald
Chi-Square Pr > ChiSq
Infliximab 9 24.8305 0.0032
Methotrexate (plus Folic acid) 3 8.3854 0.0387
Hydroxychloroquine 9 25.4841 0.0025
375
Type 3 Analysis of Effects
Effect DF
Wald
Chi-Square Pr > ChiSq
Arava (Leflunomide) 9 35.2574 <.0001
Prednisolone 9 33.2572 0.0001
Penicillamine 9 28.5823 0.0008
Table E.62- Analysis of maximum likelihood estimates in BONE MUSCLE JOINT
Analysis of Maximum Likelihood Estimates
Parameter
Bone/Joint/Muscl
e infection
D
F
Estimat
e
Standar
d
Error
Wald
Chi-
Square
Pr > ChiS
q
Intercept Mild 1 -5.1566 0.3087 278.964
7
<.0001
Intercept Mod 1 -4.9462 0.2433 413.256
7
<.0001
Intercept Severe 1 -5.3447 0.2712 388.337
9
<.0001
Arava
(Leflunomide)
3 Mild 1 -0.1188 0.2888 0.1693 0.6808
Arava
(Leflunomide)
3 Mod 1 -0.1982 0.1832 1.1705 0.2793
Arava
(Leflunomide)
3 Severe 1 -0.0531 0.1837 0.0835 0.7726
Arava
(Leflunomide)
4 Mild 1 2.0608 0.8039 6.5709 0.0104
Arava
(Leflunomide)
4 Mod 1 0.1281 0.7933 0.0261 0.8718
376
Analysis of Maximum Likelihood Estimates
Parameter
Bone/Joint/Muscl
e infection
D
F
Estimat
e
Standar
d
Error
Wald
Chi-
Square
Pr > ChiS
q
Arava
(Leflunomide)
4 Severe 1 0.8231 0.6687 1.5153 0.2183
Arava
(Leflunomide)
currentl
y taking
Mild 1 0.1898 0.3336 0.3236 0.5694
Arava
(Leflunomide)
currentl
y taking
Mod 1 0.1851 0.2077 0.7935 0.3730
Arava
(Leflunomide)
currentl
y taking
Severe 1 0.6292 0.1968 10.2160 0.0014
Arava
(Leflunomide)
b never
taking
Mild 0 0 . . .
Arava
(Leflunomide)
b never
taking
Mod 0 0 . . .
Arava
(Leflunomide)
b never
taking
Severe 0 0 . . .
Hydroxychloroqui
ne
3 Mild 1 -0.2910 0.2544 1.3078 0.2528
Hydroxychloroqui
ne
3 Mod 1 0.00465 0.1644 0.0008 0.9774
Hydroxychloroqui
ne
3 Severe 1 -0.5410 0.1560 12.0268 0.0005
Hydroxychloroqui
ne
4 Mild 1 -
11.6261
412.2 0.0008 0.9775
Hydroxychloroqui
ne
4 Mod 1 0.8595 0.5838 2.1676 0.1409
Hydroxychloroqui
ne
4 Severe 1 0.6666 0.5145 1.6791 0.1950
377
Analysis of Maximum Likelihood Estimates
Parameter
Bone/Joint/Muscl
e infection
D
F
Estimat
e
Standar
d
Error
Wald
Chi-
Square
Pr > ChiS
q
Hydroxychloroqui
ne
currentl
y taking
Mild 1 -0.2784 0.3205 0.7543 0.3851
Hydroxychloroqui
ne
currentl
y taking
Mod 1 0.2671 0.1893 1.9918 0.1582
Hydroxychloroqui
ne
currentl
y taking
Severe 1 0.1313 0.1708 0.5916 0.4418
Hydroxychloroqui
ne
b never
taking
Mild 0 0 . . .
Hydroxychloroqui
ne
b never
taking
Mod 0 0 . . .
Hydroxychloroqui
ne
b never
taking
Severe 0 0 . . .
Infliximab 3 Mild 1 0.2744 0.4349 0.3981 0.5281
Infliximab 3 Mod 1 0.0665 0.2839 0.0548 0.8149
Infliximab 3 Severe 1 0.8912 0.1977 20.3197 <.0001
Infliximab 4 Mild 1 -
11.3660
435.3 0.0007 0.9792
Infliximab 4 Mod 1 0.7831 0.5782 1.8342 0.1756
Infliximab 4 Severe 1 0.4180 0.6459 0.4189 0.5175
Infliximab currentl
y taking
Mild 1 -0.2314 0.7289 0.1007 0.7510
Infliximab currentl
y taking
Mod 1 -0.0882 0.4578 0.0372 0.8471
Infliximab currentl
y taking
Severe 1 0.5992 0.3308 3.2819 0.0700
378
Analysis of Maximum Likelihood Estimates
Parameter
Bone/Joint/Muscl
e infection
D
F
Estimat
e
Standar
d
Error
Wald
Chi-
Square
Pr > ChiS
q
Infliximab b never
taking
Mild 0 0 . . .
Infliximab b never
taking
Mod 0 0 . . .
Infliximab b never
taking
Severe 0 0 . . .
Methotrexate (plus
Folic acid)
currentl
y taking
Mild 1 -0.4576 0.2957 2.3950 0.1217
Methotrexate (plus
Folic acid)
currentl
y taking
Mod 1 -0.4093 0.1788 5.2389 0.0221
Methotrexate (plus
Folic acid)
currentl
y taking
Severe 1 0.1239 0.1458 0.7217 0.3956
Methotrexate (plus
Folic acid)
b never
taking
Mild 0 0 . . .
Methotrexate (plus
Folic acid)
b never
taking
Mod 0 0 . . .
Methotrexate (plus
Folic acid)
b never
taking
Severe 0 0 . . .
Penicillamine 3 Mild 1 0.0335 0.3849 0.0076 0.9306
Penicillamine 3 Mod 1 0.3480 0.2056 2.8650 0.0905
Penicillamine 3 Severe 1 0.7969 0.1692 22.1732 <.0001
Penicillamine 4 Mild 1 -0.2000 1.0526 0.0361 0.8493
Penicillamine 4 Mod 1 -0.2949 0.6090 0.2346 0.6281
Penicillamine 4 Severe 1 -0.3479 0.5814 0.3581 0.5496
379
Analysis of Maximum Likelihood Estimates
Parameter
Bone/Joint/Muscl
e infection
D
F
Estimat
e
Standar
d
Error
Wald
Chi-
Square
Pr > ChiS
q
Penicillamine currentl
y taking
Mild 1 1.6260 1.0242 2.5204 0.1124
Penicillamine currentl
y taking
Mod 1 -
13.0777
913.3 0.0002 0.9886
Penicillamine currentl
y taking
Severe 1 -
12.9395
847.3 0.0002 0.9878
Penicillamine b never
taking
Mild 0 0 . . .
Penicillamine b never
taking
Mod 0 0 . . .
Penicillamine b never
taking
Severe 0 0 . . .
Prednisolone 3 Mild 1 -0.4588 0.3457 1.7618 0.1844
Prednisolone 3 Mod 1 0.2064 0.2531 0.6652 0.4147
Prednisolone 3 Severe 1 0.4961 0.2745 3.2653 0.0708
Prednisolone 4 Mild 1 -
11.7508
537.3 0.0005 0.9826
Prednisolone 4 Mod 1 0.7436 1.0554 0.4963 0.4811
Prednisolone 4 Severe 1 0.6734 1.0753 0.3921 0.5312
Prednisolone currentl
y taking
Mild 1 0.0363 0.3124 0.0135 0.9075
Prednisolone currentl
y taking
Mod 1 0.6298 0.2394 6.9233 0.0085
Prednisolone currentl
y taking
Severe 1 0.9273 0.2630 12.4305 0.0004
380
Analysis of Maximum Likelihood Estimates
Parameter
Bone/Joint/Muscl
e infection
D
F
Estimat
e
Standar
d
Error
Wald
Chi-
Square
Pr > ChiS
q
Prednisolone b never
taking
Mild 0 0 . . .
Prednisolone b never
taking
Mod 0 0 . . .
Prednisolone b never
taking
Severe 0 0 . . .
Table E.63- Odds ratio estimates in BONE MUSCLE JOINT
Odds Ratio Estimates
Effect
Bone/Joint/Muscle
infection
Point
Estimate
95% Wald
Confidence Limits
Infliximab 3 Versus
never taking
1 1.316 0.561 3.086
Infliximab 3 Versus
never taking
2 1.069 0.613 1.864
Infliximab 3 Versus
never taking
3 2.438 1.655 3.592
Infliximab 4 Versus
never taking
1 <0.001 <0.001 >999.999
Infliximab 4 Versus
never taking
2 2.188 0.705 6.796
Infliximab 4 Versus
never taking
3 1.519 0.428 5.387
Infliximab currently taking
Versus never taking
1 0.793 0.190 3.311
381
Odds Ratio Estimates
Effect
Bone/Joint/Muscle
infection
Point
Estimate
95% Wald
Confidence Limits
Infliximab currently taking
Versus never taking
2 0.916 0.373 2.246
Infliximab currently taking
Versus never taking
3 1.821 0.952 3.482
Methotrexate (plus Folic acid)
currently taking Versus never
taking
1 0.633 0.354 1.130
Methotrexate (plus Folic acid)
currently taking Versus never
taking
2 0.664 0.468 0.943
Methotrexate (plus Folic acid)
currently taking Versus never
taking
3 1.132 0.851 1.506
Hydroxychloroquine 3
Versus never taking
1 0.748 0.454 1.231
Hydroxychloroquine 3
Versus never taking
2 1.005 0.728 1.387
Hydroxychloroquine 3
Versus never taking
3 0.582 0.429 0.790
Hydroxychloroquine 4
Versus never taking
1 <0.001 <0.001 >999.999
Hydroxychloroquine 4
Versus never taking
2 2.362 0.752 7.417
Hydroxychloroquine 4
Versus never taking
3 1.948 0.711 5.339
Hydroxychloroquine currently
taking Versus never taking
1 0.757 0.404 1.419
382
Odds Ratio Estimates
Effect
Bone/Joint/Muscle
infection
Point
Estimate
95% Wald
Confidence Limits
Hydroxychloroquine currently
taking Versus never taking
2 1.306 0.901 1.893
Hydroxychloroquine currently
taking Versus never taking
3 1.140 0.816 1.594
Arava (Leflunomide) 3
Versus never taking
1 0.888 0.504 1.564
Arava (Leflunomide) 3
Versus never taking
2 0.820 0.573 1.175
Arava (Leflunomide) 3
Versus never taking
3 0.948 0.662 1.359
Arava (Leflunomide) 4
Versus never taking
1 7.852 1.624 37.956
Arava (Leflunomide) 4
Versus never taking
2 1.137 0.240 5.381
Arava (Leflunomide) 4
Versus never taking
3 2.278 0.614 8.446
Arava (Leflunomide)
currently taking Versus never
taking
1 1.209 0.629 2.325
Arava (Leflunomide)
currently taking Versus never
taking
2 1.203 0.801 1.808
Arava (Leflunomide)
currently taking Versus never
taking
3 1.876 1.276 2.759
Prednisolone 3
Versus never taking
1 0.632 0.321 1.244
383
Odds Ratio Estimates
Effect
Bone/Joint/Muscle
infection
Point
Estimate
95% Wald
Confidence Limits
Prednisolone 3
Versus never taking
2 1.229 0.749 2.019
Prednisolone 3
Versus never taking
3 1.642 0.959 2.813
Prednisolone 4
Versus never taking
1 <0.001 <0.001 >999.999
Prednisolone 4
Versus never taking
2 2.103 0.266 16.646
Prednisolone 4
Versus never taking
3 1.961 0.238 16.136
Prednisolone currently taking
Versus never taking
1 1.037 0.562 1.913
Prednisolone currently taking
Versus never taking
2 1.877 1.174 3.001
Prednisolone currently taking
Versus never taking
3 2.528 1.510 4.232
Penicillamine 3
Versus never taking
1 1.034 0.486 2.199
Penicillamine 3
Versus never taking
2 1.416 0.947 2.119
Penicillamine 3
Versus never taking
3 2.219 1.592 3.092
Penicillamine 4
Versus never taking
1 0.819 0.104 6.444
Penicillamine 4
Versus never taking
2 0.745 0.226 2.456
384
Odds Ratio Estimates
Effect
Bone/Joint/Muscle
infection
Point
Estimate
95% Wald
Confidence Limits
Penicillamine 4
Versus never taking
3 0.706 0.226 2.207
Penicillamine currently taking
Versus never taking
1 5.084 0.683 37.844
Penicillamine currently taking
Versus never taking
2 <0.001 <0.001 >999.999
Penicillamine currently taking
Versus never taking
3 <0.001 <0.001 >999.999
385
APPENDIX F: OUTPUT OF SAS FOR
BLOOD INFECTION
Table F.1- Complete statistics for blood infection.
Model Information
Data Set WORK.IMPORT2
Response Variable InfBlood InfBlood
Number of Response Levels 4
Model generalized logit
Optimization Technique Newton-Raphson
Table F.2- Observation status for BLOOD infection
Number of Observations Read 27711
Number of Observations Used 21506
Table F.3- response value for BLOOD infection
Response Profile
Ordered
Value InfBlood
Total
Frequency
1 1 21
2 2 70
3 3 111
4 4 21304
0 .
Logits modelled use InfBlood='4' as the reference category.
Note: 6205 observations were deleted due to missing values for
the response or explanatory variables.
386
Note: 1 response level was deleted due to missing or invalid values for its explanatory,
frequency, or weight variables.
Table F.4- Backward Elimination Procedure for BLOOD infection
Backward Elimination Procedure
Class Level Information
Class Value Design Variables
Etanercept 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Adalimumab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Anakinra 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Infliximab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Rituximab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Abatacept 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
387
Tocilizumab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Golimumab 3 1 0 0
currently taking 0 1 0
never taking 0 0 1
Certolizumab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Folic Acid currently taking 1 0
never taking 0 1
Hydroxychloroquine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Sulphasalazine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Arava (Leflunomide) 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Azathioprine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Cyclosporin 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
388
Prednisolone 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
IM Gold injection 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Penicillamine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Step 0. The following effects were entered:
Intercept Etanercept Adalimumab Anakinra Infliximab Rituximab Abatacept Tocilizumab
Golimumab Certolizumab Folic Acid Hydroxychloroquine Sulphasalazine Arava
(Leflunomide) Azathioprine Cyclosporin Prednisolone IM Gold injection Penicillamine
Table F.5- Model Convergence status for BLOOD infection
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
Table F.6- Model Fit statistics for BLOOD infection
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 2670.261 2718.376
389
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
SC 2694.189 3962.646
-2 Log L 2664.261 2406.376
Table F.7- Testing null hypothesis for BLOOD infection
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 257.8845 153 <.0001
Score 284.8579 153 <.0001
Wald 233.2076 153 <.0001
390
Table F.8- Model Fit statistics for removing covariant step 1
Step 1. Effect Anakinra is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
Table F.9- Model Fit statistics for removing covariant step 1
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 2670.261 2706.095
SC 2694.189 3878.580
-2 Log L 2664.261 2412.095
Table F.10- Testing Null hypothesis after removing covariant step 1
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 252.1657 144 <.0001
Score 282.2859 144 <.0001
Wald 231.6736 144 <.0001
391
Table F.11- Residual removing covariant step 1
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
3.1885 9 0.9563
Table F.12- Model Fit statistics for removing covariant step 2
Step 2. Effect Certolizumab is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
Table F.13- Model Fit statistics after removing covariant step 2
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 2670.261 2701.408
SC 2694.189 3802.108
-2 Log L 2664.261 2425.408
392
Table F.14- Testing Null hypothesis after removing covariant step 2
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 238.8526 135 <.0001
Score 268.2359 135 <.0001
Wald 222.0689 135 <.0001
Table F.15- Residual removing covariant step 2
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
11.6827 18 0.8632
Table F.16- Model Fit statistics for removing covariant step 3
Step 3. Effect Infliximab is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
393
Table F.17- Model Fit statistics after removing covariant step 3
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 2670.261 2688.609
SC 2694.189 3717.524
-2 Log L 2664.261 2430.609
Table F.18- Testing Null hypothesis after removing covariant step 3
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 233.6524 126 <.0001
Score 261.5980 126 <.0001
Wald 217.2921 126 <.0001
Table F.19- Residual removing covariant step 3
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
16.0867 27 0.9513
394
Table F.20- Model Fit statistics for removing covariant step 4
Step 4. Effect Rituximab is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
Table F.21- Model Fit statistics after removing covariant step 4
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 2670.261 2678.948
SC 2694.189 3636.079
-2 Log L 2664.261 2438.948
Table F.22- Testing Null hypothesis after removing covariant step 4
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 225.3126 117 <.0001
Score 253.3066 117 <.0001
Wald 210.1667 117 <.0001
395
Table F.23- Residual removing covariant step 4
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
23.2706 36 0.9500
Table F.24- Model Fit statistics for removing covariant step 5
Step 5. Effect Arava (Leflunomide) is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
Table F.25- Model Fit statistics after removing covariant step 5
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 2670.261 2666.028
SC 2694.189 3551.373
-2 Log L 2664.261 2444.028
Table F.26- Testing Null hypothesis after removing covariant step 5
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 220.2334 108 <.0001
Score 246.5724 108 <.0001
Wald 203.9904 108 <.0001
396
Table F.27- Residual removing covariant step 5
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
28.7370 45 0.9717
Table F.28- Model Fit statistics for removing covariant step 6
Step 6. Effect Penicillamine is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
Table F.29- Model Fit statistics after removing covariant step 6
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 2670.261 2658.846
SC 2694.189 3472.407
-2 Log L 2664.261 2454.846
Table F.30- Testing Null hypothesis after removing covariant step 6
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 209.4150 99 <.0001
Score 235.7326 99 <.0001
Wald 196.5912 99 <.0001
397
Table F.31- Residual removing covariant step 6
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
37.4641 54 0.9577
Table F.32- Model Fit statistics for removing covariant step 7
Step 7. Effect Golimumab is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
Table F.33- Model Fit statistics after removing covariant step 7
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 2670.261 2655.632
SC 2694.189 3421.337
-2 Log L 2664.261 2463.632
Table F.34- Testing Null hypothesis after removing covariant step 7
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 200.6285 93 <.0001
Score 228.1270 93 <.0001
Wald 193.4477 93 <.0001
398
Table F.35- Residual removing covariant step 7
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
44.5830 60 0.9316
Table F.36- Model Fit statistics for removing covariant step 8
Step 8. Effect Cyclosporin is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
Table F.37- Model Fit statistics after removing covariant step 8
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 2670.261 2653.141
SC 2694.189 3347.061
-2 Log L 2664.261 2479.141
Table F.38- Testing Null hypothesis after removing covariant step 8
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 185.1200 84 <.0001
Score 211.1646 84 <.0001
Wald 178.6733 84 <.0001
399
Table F.39- Residual removing covariant step 8
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
59.2165 69 0.7934
Table F.40- Model Fit statistics for removing covariant step 9
Step 9. Effect Sulphasalazine is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
400
Table F.41- Model Fit statistics after removing covariant step 9
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 2670.261 2642.193
SC 2694.189 3264.328
-2 Log L 2664.261 2486.193
Table F.42- Testing Null hypothesis after removing covariant step 9
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 178.0675 75 <.0001
Score 200.9988 75 <.0001
Wald 169.6897 75 <.0001
Table F.43- Residual removing covariant step 9
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
67.3223 78 0.8005
Table F.44- Model Fit statistics for removing covariant step 10
Step 10. Effect Azathioprine is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
401
Table F.45- Model Fit statistics after removing covariant step 10
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 2670.261 2635.960
SC 2694.189 3186.310
-2 Log L 2664.261 2497.960
Table F.46- Testing Null hypothesis after removing covariant step 10
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 166.3009 66 <.0001
Score 184.7483 66 <.0001
Wald 158.3863 66 <.0001
Table F.47- Residual removing covariant step 10
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
81.5794 87 0.6439
Table F.48- Model Fit statistics for removing covariant step 11
Step 11. Effect Abatacept is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
Table F.49- Model Fit statistics after removing covariant step 11
402
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 2670.261 2637.362
SC 2694.189 3115.928
-2 Log L 2664.261 2517.362
Table F.50- Testing Null hypothesis after removing covariant step 11
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 146.8987 57 <.0001
Score 164.0564 57 <.0001
Wald 139.9299 57 <.0001
Table F.51- Residual removing covariant step 11
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
103.8113 96 0.2753
Table F.52- Model Fit statistics for removing covariant step 12
Step 12. Effect Tocilizumab is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
403
Table F.53- Model Fit statistics for removing covariant step 12
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 2670.261 2632.264
SC 2694.189 3039.045
-2 Log L 2664.261 2530.264
Table F.54- Testing Null hypothesis after removing covariant step 12
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 133.9966 48 <.0001
Score 144.2262 48 <.0001
Wald 125.1312 48 <.0001
Table F.55- Residual removing covariant step 12
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
127.3818 105 0.0679
404
Table F.56- Model Fit statistics for removing covariant step 13
Step 13. Effect Folic Acid is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
Table F.57- Model Fit statistics after removing covariant step 13
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 2670.261 2630.847
SC 2694.189 3013.699
-2 Log L 2664.261 2534.847
Table F.58- Testing Null hypothesis after removing covariant step 13
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 129.4141 45 <.0001
Score 138.7698 45 <.0001
Wald 120.0137 45 <.0001
Table F.59- Residual removing covariant step 13
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
133.2998 108 0.0497
405
Table F.60- Model Fit statistics for removing covariant step 14
Step 14. Effect IM Gold injection is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
Table F.61- Model Fit statistics after removing covariant step 14
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 2670.261 2627.554
SC 2694.189 2938.621
-2 Log L 2664.261 2549.554
Table F.62- Testing Null hypothesis after removing covariant step 14
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 114.7072 36 <.0001
Score 122.2303 36 <.0001
Wald 104.6456 36 <.0001
Table F.63- Residual removing covariant step 14
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
151.0167 117 0.0187
406
Table F.64- Model Fit statistics for removing covariant step 15
Step 15. Effect Adalimumab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table F.65- Model Fit statistics after removing covariant step 15
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 2670.261 2629.777
SC 2694.189 2869.059
-2 Log L 2664.261 2569.777
Table F.66- Testing Null hypothesis after removing covariant step 15
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 94.4841 27 <.0001
Score 101.5949 27 <.0001
Wald 85.0867 27 <.0001
Table F.67- Residual removing covariant step 15
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
175.1574 126 0.0025
Table F.68- Model Fit statistics for removing covariant step 16
Step 16. Effect Etanercept is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
407
Table F.68- Model Fit statistics after removing covariant step 16
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 2670.261 2628.182
SC 2694.189 2795.680
-2 Log L 2664.261 2586.182
Table F.69- Testing Null hypothesis after removing covariant step 16
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 78.0792 18 <.0001
Score 84.6206 18 <.0001
Wald 69.6515 18 <.0001
Table F.70- Residual removing covariant step 16
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
191.7886 135 0.0010
Note: No (additional) effects met the 0.05 significance level for removal from the model.
Table F.71- Summary of backward elimination in BLOOD
Summary of Backward Elimination
Step
Effect
Removed DF
Number
In
Wald
Chi-Square Pr > ChiSq
Variable
Label
1 Anakinra 9 17 0.2808 1.0000
2 Certolizumab 9 16 1.6755 0.9956 Certolizumab
3 Infliximab 9 15 3.5451 0.9387
4 Rituximab 9 14 4.8713 0.8454 Rituximab
408
Summary of Backward Elimination
Step
Effect
Removed DF
Number
In
Wald
Chi-Square Pr > ChiSq
Variable
Label
5 Arava (Leflunomide) 9 13 5.3385 0.8039 Arava (Leflunomide)
6 Penicillamine 9 12 5.6548 0.7739 Penicillamine
7 Golimumab 6 11 3.5097 0.7427 Golimumab
8 Cyclosporin 9 10 7.0960 0.6271 Cyclosporin
9 Sulphasalazine 9 9 7.1260 0.6240 Sulphasalazine
10 Azathioprine 9 8 9.5235 0.3904 Azathioprine
11 Abatacept 9 7 12.2368 0.2003 Abatacept
12 Tocilizumab 9 6 12.2063 0.2019 Tocilizumab
13 Folic Acid 3 5 4.8032 0.1868 Folic Acid
14 IM Gold injection 9 4 15.5529 0.0768 IM Gold injection
15 Adalimumab 9 3 16.5298 0.0566
16 Etanercept 9 2 14.0456 0.1207
Table F.71- Type 3 analysis of effects in BLOOD
Type 3 Analysis of Effects
Effect DF
Wald
Chi-Square Pr > ChiSq
Hydroxychloroquine 9 18.1008 0.0340
Prednisolone 9 49.5445 <.0001
Table F.72- Analysis of maximum likelihood estimates in BLOOD
Analysis of Maximum Likelihood Estimates
Parameter InfBlood DF Estimate
Standard
Error
Wald
Chi-Square Pr > ChiSq
Intercept Mild 1 -5.9671 0.4591 168.9262 <.0001
Intercept Mod 1 -5.4061 0.3070 309.9955 <.0001
Intercept Severe 1 -6.2875 0.4552 190.7863 <.0001
Hydroxychloroquine 3 Mild 1 -1.1890 0.5196 5.2369 0.0221
Hydroxychloroquine 3 Mod 1 -0.4748 0.2724 3.0385 0.0813
409
Analysis of Maximum Likelihood Estimates
Parameter InfBlood DF Estimate
Standard
Error
Wald
Chi-Square Pr > ChiSq
Hydroxychloroquine 3 Severe 1 -0.1629 0.2022 0.6495 0.4203
Hydroxychloroquine 4 Mild 1 -11.8338 746.4 0.0003 0.9874
Hydroxychloroquine 4 Mod 1 0.1219 1.0413 0.0137 0.9068
Hydroxychloroquine 4 Severe 1 0.5880 0.7276 0.6530 0.4190
Hydroxychloroquine currently taking Mild 1 -1.9353 1.0331 3.5095 0.0610
Hydroxychloroquine currently taking Mod 1 -0.2859 0.3367 0.7211 0.3958
Hydroxychloroquine currently taking Severe 1 -0.8566 0.3478 6.0675 0.0138
Hydroxychloroquine never taking Mild 0 0 . . .
Hydroxychloroquine never taking Mod 0 0 . . .
Hydroxychloroquine never taking Severe 0 0 . . .
Prednisolone 3 Mild 1 -0.4482 0.5889 0.5791 0.4467
Prednisolone 3 Mod 1 -0.8646 0.4105 4.4356 0.0352
Prednisolone 3 Severe 1 0.6384 0.4932 1.6756 0.1955
Prednisolone 4 Mild 1 -11.7277 1195.8 0.0001 0.9922
Prednisolone 4 Mod 1 2.1498 0.7901 7.4034 0.0065
Prednisolone 4 Severe 1 -10.3498 552.6 0.0004 0.9851
Prednisolone currently taking Mild 1 -0.3973 0.5607 0.5020 0.4786
Prednisolone currently taking Mod 1 0.2259 0.3281 0.4738 0.4912
Prednisolone currently taking Severe 1 1.6700 0.4622 13.0559 0.0003
Prednisolone never taking Mild 0 0 . . .
Prednisolone never taking Mod 0 0 . . .
Prednisolone never taking Severe 0 0 . . .
Table F.73- Odds ratio estimates in BLOOD
Odds Ratio Estimates
Effect InfBlood Point Estimate
95% Wald
Confidence Limits
Hydroxychloroquine 3 vs never taking Mild 0.305 0.110 0.843
Hydroxychloroquine 3 vs never taking Mod 0.622 0.365 1.061
Hydroxychloroquine 3 vs never taking Severe 0.850 0.572 1.263
Hydroxychloroquine 4 vs never taking Mild <0.001 <0.001 >999.999
410
Odds Ratio Estimates
Effect InfBlood Point Estimate
95% Wald
Confidence Limits
Hydroxychloroquine 4 vs never taking Mod 1.130 0.147 8.695
Hydroxychloroquine 4 vs never taking Severe 1.800 0.433 7.493
Hydroxychloroquine currently taking vs never taking Mild 0.144 0.019 1.094
Hydroxychloroquine currently taking vs never taking Mod 0.751 0.388 1.453
Hydroxychloroquine currently taking vs never taking Severe 0.425 0.215 0.839
Prednisolone 3 vs never taking Mild 0.639 0.201 2.026
Prednisolone 3 vs never taking Mod 0.421 0.188 0.942
Prednisolone 3 vs never taking Severe 1.893 0.720 4.977
Prednisolone 4 vs never taking Mild <0.001 <0.001 >999.999
Prednisolone 4 vs never taking Mod 8.583 1.824 40.380
Prednisolone 4 vs never taking Severe <0.001 <0.001 >999.999
Prednisolone currently taking vs never taking Mild 0.672 0.224 2.017
Prednisolone currently taking vs never taking Mod 1.253 0.659 2.385
Prednisolone currently taking vs never taking Severe 5.312 2.147 13.142
411
APPENDIX G: OUTPUT OF SAS FOR
GIT INFECTION
Table G.1- complete statistics for GIT infection
Model Information
Data Set WORK.IMPORT2
Response Variable InfGit InfGit
Number of Response Levels 4
Model generalized logit
Optimization Technique Newton-Raphson
Table G.2- Observation status for GIT infection
Number of Observations Read 27711
Number of Observations Used 21506
Table G.3- response value for GIT infection
Response Profile
Ordered
Value InfGit
Total
Frequency
1 1 118
2 2 241
3 3 155
4 4 20992
412
Logits modelled use InfGit='4' as the reference category.
Note: 6205 observations were deleted due to missing values for the response or explanatory
variables.
Table G.4- Backward Elimination Procedure for GIT infection
Backward Elimination Procedure
Class Level Information
Class Value Design Variables
Etanercept 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Adalimumab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Anakinra 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Infliximab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Rituximab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Abatacept 3 1 0 0 0
4 0 1 0 0
413
currently taking 0 0 1 0
never taking 0 0 0 1
Tocilizumab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Golimumab 3 1 0 0
currently taking 0 1 0
never taking 0 0 1
Certolizumab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Folic Acid currently taking 1 0
never taking 0 1
Hydroxychloroquine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Sulphasalazine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Arava (Leflunomide) 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Azathioprine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Cyclosporin 3 1 0 0 0
4 0 1 0 0
414
currently taking 0 0 1 0
never taking 0 0 0 1
Prednisolone 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
IM Gold injection 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Penicillamine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Step 0. The following effects were entered:
Intercept Etanercept Adalimumab Anakinra Infliximab Rituximab Abatacept Tocilizumab
Golimumab Certolizumab Folic Acid Hydroxychloroquine Sulphasalazine Arava
(Leflunomide) Azathioprine Cyclosporin Prednisolone IM Gold injection Penicillamine
Table G.5- Model Convergence status for GIT infection
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
415
Table G.6- Model Fit statistics for GIT infection
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 5944.018 6016.696
SC 5967.947 7260.965
-2 Log L 5938.018 5704.696
Table G.7- Testing null hypothesis for GIT infection
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 233.3227 153 <.0001
Score 267.8657 153 <.0001
Wald 231.4222 153 <.0001
Table G.8- Model Fit statistics for removing covariant step 1
Step 1. Effect Certolizumab is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
Table G.10- Testing Null hypothesis after removing covariant step 1
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 225.2887 144 <.0001
Score 262.5650 144 <.0001
Wald 229.5599 144 <.0001
416
Table G.11- Residual removing covariant step 1
Table G.12- Model Fit statistics for removing covariant step 2
Step 2. Effect Azathioprine is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table G.13- Model Fit statistics after removing covariant step 2
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 5944.018 5995.805
SC 5967.947 7096.505
-2 Log L 5938.018 5719.805
Table G.14- Testing Null hypothesis after removing covariant step 2
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 218.2134 135 <.0001
Score 256.4742 135 <.0001
Wald 225.2282 135 <.0001
Table G.15- Residual removing covariant step 2
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
10.5472 18 0.9126
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
4.6014 9 0.8676
417
Table G.16- Model Fit statistics for removing covariant step 3
Step 3. Effect IM Gold injection is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table G.17- Model Fit statistics after removing covariant step 3
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 5944.018 5987.499
SC 5967.947 7016.414
-2 Log L 5938.018 5729.499
Table G.18- Testing Null hypothesis after removing covariant step 3
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 208.5195 126 <.0001
Score 249.5871 126 <.0001
Wald 218.5522 126 <.0001
Table G.19- Residual removing covariant step 3
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
18.1288 27 0.8995
Table G.20- Model Fit statistics for removing covariant step 4
Step 4. Effect Golimumab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
418
Table G.21- Model Fit statistics after removing covariant step 4
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 5944.018 5981.489
SC 5967.947 6962.548
-2 Log L 5938.018 5735.489
Table G.22- Testing Null hypothesis after removing covariant step 4
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 202.5292 120 <.0001
Score 244.1877 120 <.0001
Wald 215.0861 120 <.0001
Table G.23- Residual removing covariant step 4
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
22.1197 33 0.9249
419
Table G.24- Model Fit statistics for removing covariant step 5
Step 5. Effect Tocilizumab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table G.25- Model Fit statistics after removing covariant step 5
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 5944.018 5968.320
SC 5967.947 6877.594
-2 Log L 5938.018 5740.320
Table G.26- Testing Null hypothesis after removing covariant step 5
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 197.6985 111 <.0001
Score 238.1638 111 <.0001
Wald 209.0709 111 <.0001
Table G.27- Residual removing covariant step 5
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
26.6451 42 0.9688
Table G.28- Model Fit statistics for removing covariant step 6
Step 6. Effect Etanercept is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
420
Table G.29- Model Fit statistics after removing covariant step 6
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 5944.018 5956.458
SC 5967.947 6793.947
-2 Log L 5938.018 5746.458
Table G.30- Testing Null hypothesis after removing covariant step 6
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 191.5606 102 <.0001
Score 232.3845 102 <.0001
Wald 204.2680 102 <.0001
Table G.31- Residual removing covariant step 6
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
32.5729 51 0.9792
Table G.32- Model Fit statistics for removing covariant step 7
Step 7. Effect Arava (Leflunomide) is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table G.33- Model Fit statistics after removing covariant step 7
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 5944.018 5951.321
SC 5967.947 6717.025
-2 Log L 5938.018 5759.321
421
Table G.34- Testing Null hypothesis after removing covariant step 7
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 178.6973 93 <.0001
Score 222.8700 93 <.0001
Wald 195.3658 93 <.0001
Table G.35- Residual removing covariant step 7
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
42.0866 60 0.9618
Table G.36- Model Fit statistics for removing covariant step 8
Step 8. Effect Anakinra is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table G.37- Model Fit statistics after removing covariant step 8
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 5944.018 5941.440
SC 5967.947 6635.359
-2 Log L 5938.018 5767.440
Table G.38- Testing Null hypothesis after removing covariant step 8
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 170.5786 84 <.0001
422
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Score 212.2333 84 <.0001
Wald 186.4767 84 <.0001
Table G.39- Residual removing covariant step 8
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
51.4198 69 0.9439
Table G.40- Model Fit statistics for removing covariant step 9
Step 9. Effect Penicillamine is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table G.41- Model Fit statistics after removing covariant step 9
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 5944.018 5938.817
SC 5967.947 6560.952
-2 Log L 5938.018 5782.817
Table G.42- Testing Null hypothesis after removing covariant step 9
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 155.2011 75 <.0001
Score 199.4953 75 <.0001
Wald 176.5609 75 <.0001
423
Table G.43- Residual removing covariant step 9
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
63.9498 78 0.8742
424
Table G.44- Model Fit statistics for removing covariant step 10
Step 10. Effect Abatacept is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table G.45- Model Fit statistics after removing covariant step 10
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 5944.018 5932.444
SC 5967.947 6482.794
-2 Log L 5938.018 5794.444
Table G.46- Testing Null hypothesis after removing covariant step 10
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 143.5746 66 <.0001
Score 181.8225 66 <.0001
Wald 161.2515 66 <.0001
Table G.47- Residual removing covariant step 10
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
74.9017 87 0.8192
Table G.48- Model Fit statistics for removing covariant step 11
Step 11. Effect Folic Acid is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
425
Table G.49- Model Fit statistics after removing covariant step 11
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 5944.018 5929.937
SC 5967.947 6456.359
-2 Log L 5938.018 5797.937
Table G.50- Testing Null hypothesis after removing covariant step 11
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 140.0810 63 <.0001
Score 178.1497 63 <.0001
Wald 157.6714 63 <.0001
Table G.51- Residual removing covariant step 11
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
77.7585 90 0.8178
426
Table G.52- Model Fit statistics for removing covariant step 12
Step 12. Effect Rituximab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table G.53- Model Fit statistics for removing covariant step 12
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 5944.018 5923.849
SC 5967.947 6378.486
-2 Log L 5938.018 5809.849
Table G.54- Testing Null hypothesis after removing covariant step 12
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 128.1694 54 <.0001
Score 163.3382 54 <.0001
Wald 144.8243 54 <.0001
Table G.55- Residual removing covariant step 12
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
93.3813 99 0.6404
Table G.56- Model Fit statistics for removing covariant step 13
427
Step 13. Effect Hydroxychloroquine is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table G.57- Model Fit statistics after removing covariant step 13
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 5944.018 5917.151
SC 5967.947 6300.003
-2 Log L 5938.018 5821.151
Table G.58- Testing Null hypothesis after removing covariant step 13
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 116.8672 45 <.0001
Score 151.1024 45 <.0001
Wald 132.5918 45 <.0001
Table G.59- Residual removing covariant step 13
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
105.9946 108 0.5366
Table G.60- Model Fit statistics for removing covariant step 14
Step 14. Effect Sulphasalazine is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
428
Table G.61- Model Fit statistics after removing covariant step 14
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 5944.018 5913.025
SC 5967.947 6224.093
-2 Log L 5938.018 5835.025
Table G.62- Testing Null hypothesis after removing covariant step 14
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 102.9929 36 <.0001
Score 137.5680 36 <.0001
Wald 118.9900 36 <.0001
Table G.63- Residual removing covariant step 14
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
118.3833 117 0.4468
Table G.64- Summary of backward elimination in GIT
Note: No (additional) effects met the 0.05 significance level for removal from the model.
Summary of Backward Elimination
Step
Effect
Removed DF
Number
In
Wald
Chi-Square Pr > ChiSq
Variable
Label
1 Certolizumab 9 17 1.0448 0.9993 Certolizumab
2 Azathioprine 9 16 3.6049 0.9354 Azathioprine
3 IM Gold injection 9 15 4.1551 0.9009 IM Gold injection
4 Golimumab 6 14 2.5967 0.8575 Golimumab
429
Summary of Backward Elimination
Step
Effect
Removed DF
Number
In
Wald
Chi-Square Pr > ChiSq
Variable
Label
5 Tocilizumab 9 13 4.0299 0.9094 Tocilizumab
6 Etanercept 9 12 4.7392 0.8564
7 Arava (Leflunomide) 9 11 7.0144 0.6356 Arava (Leflunomide)
8 Anakinra 9 10 8.1882 0.5153
9 Penicillamine 9 9 9.0251 0.4350 Penicillamine
10 Abatacept 9 8 9.7122 0.3743 Abatacept
11 Folic Acid 3 7 3.3746 0.3374 Folic Acid
12 Rituximab 9 6 11.2027 0.2621 Rituximab
13 Hydroxychloroquine 9 5 12.0724 0.2093 Hydroxychloroquine
14 Sulphasalazine 9 4 13.3164 0.1488 Sulphasalazine
Table G.65- Type 3 analysis of effects in GIT
Type 3 Analysis of Effects
Effect DF
Wald
Chi-Square Pr > ChiSq
Adalimumab 9 17.6510 0.0394
Infliximab 9 20.5203 0.0150
Cyclosporin 9 45.7794 <.0001
Prednisolone 9 21.3027 0.0114
Table G.66- Analysis of maximum likelihood estimates in GIT
Analysis of Maximum Likelihood Estimates
Parameter InfGit DF Estimate
Standard
Error
Wald
Chi-Square Pr > ChiSq
Intercept Mild 1 -5.2849 0.2624 405.6943 <.0001
Intercept Mod 1 -5.0911 0.2188 541.1765 <.0001
Intercept Severe 1 -5.3511 0.2559 437.3119 <.0001
Adalimumab 3 Mild 1 -0.2671 0.2430 1.2084 0.2716
Adalimumab 3 Mod 1 0.3088 0.1609 3.6832 0.0550
430
Analysis of Maximum Likelihood Estimates
Parameter InfGit DF Estimate
Standard
Error
Wald
Chi-Square Pr > ChiSq
Adalimumab 3 Severe 1 0.4587 0.1904 5.8070 0.0160
Adalimumab 4 Mild 1 -10.7504 379.2 0.0008 0.9774
Adalimumab 4 Mod 1 -0.0400 0.8145 0.0024 0.9609
Adalimumab 4 Severe 1 0.2794 1.1205 0.0622 0.8031
Adalimumab currently taking Mild 1 -0.6383 0.2648 5.8087 0.0159
Adalimumab currently taking Mod 1 0.1312 0.1664 0.6217 0.4304
Adalimumab currently taking Severe 1 -0.1035 0.2231 0.2151 0.6428
Adalimumab never taking Mild 0 0 . . .
Adalimumab never taking Mod 0 0 . . .
Adalimumab never taking Severe 0 0 . . .
Cyclosporin 3 Mild 1 0.2056 0.2607 0.6218 0.4304
Cyclosporin 3 Mod 1 0.1697 0.1743 0.9487 0.3300
Cyclosporin 3 Severe 1 -0.0189 0.2203 0.0073 0.9318
Cyclosporin 4 Mild 1 -0.1041 1.0088 0.0106 0.9178
Cyclosporin 4 Mod 1 -1.6749 1.0458 2.5652 0.1092
Cyclosporin 4 Severe 1 -0.8419 1.0380 0.6579 0.4173
Cyclosporin currently taking Mild 1 1.8937 0.4688 16.3187 <.0001
Cyclosporin currently taking Mod 1 1.8260 0.3563 26.2594 <.0001
Cyclosporin currently taking Severe 1 0.7529 0.7200 1.0933 0.2957
Cyclosporin never taking Mild 0 0 . . .
Cyclosporin never taking Mod 0 0 . . .
Cyclosporin never taking Severe 0 0 . . .
Infliximab 3 Mild 1 0.1003 0.3783 0.0703 0.7909
Infliximab 3 Mod 1 0.2989 0.2397 1.5553 0.2124
Infliximab 3 Severe 1 0.6058 0.2596 5.4445 0.0196
Infliximab 4 Mild 1 -11.1291 340.5 0.0011 0.9739
Infliximab 4 Mod 1 1.3895 0.4631 9.0037 0.0027
Infliximab 4 Severe 1 -0.0667 1.0786 0.0038 0.9507
Infliximab currently taking Mild 1 -0.6060 0.7169 0.7147 0.3979
Infliximab currently taking Mod 1 0.6928 0.3058 5.1333 0.0235
Infliximab currently taking Severe 1 0.2397 0.4640 0.2669 0.6054
Infliximab never taking Mild 0 0 . . .
431
Analysis of Maximum Likelihood Estimates
Parameter InfGit DF Estimate
Standard
Error
Wald
Chi-Square Pr > ChiSq
Infliximab never taking Mod 0 0 . . .
Infliximab never taking Severe 0 0 . . .
Prednisolone 3 Mild 1 0.3716 0.2979 1.5564 0.2122
Prednisolone 3 Mod 1 0.4629 0.2400 3.7208 0.0537
Prednisolone 3 Severe 1 -0.0677 0.2991 0.0513 0.8209
Prednisolone 4 Mild 1 -11.1279 597.6 0.0003 0.9851
Prednisolone 4 Mod 1 1.6744 0.7943 4.4439 0.0350
Prednisolone 4 Severe 1 1.2636 1.0893 1.3457 0.2460
Prednisolone currently taking Mild 1 0.1868 0.3008 0.3856 0.5346
Prednisolone currently taking Mod 1 0.4391 0.2384 3.3921 0.0655
Prednisolone currently taking Severe 1 0.5437 0.2780 3.8254 0.0505
Prednisolone never taking Mild 0 0 . . .
Prednisolone never taking Mod 0 0 . . .
Prednisolone never taking Severe 0 0 . . .
Table G.67- Odds ratio estimates in GIT
Odds Ratio Estimates
Effect InfGit Point Estimate
95% Wald
Confidence Limits
Adalimumab 3 vs never taking Mild 0.766 0.476 1.233
Adalimumab 3 vs never taking Mod 1.362 0.993 1.867
Adalimumab 3 vs never taking Severe 1.582 1.089 2.297
Adalimumab 4 vs never taking Mild <0.001 <0.001 >999.999
Adalimumab 4 vs never taking Mod 0.961 0.195 4.742
Adalimumab 4 vs never taking Severe 1.322 0.147 11.889
Adalimumab currently taking vs never taking Mild 0.528 0.314 0.888
Adalimumab currently taking vs never taking Mod 1.140 0.823 1.580
Adalimumab currently taking vs never taking Severe 0.902 0.582 1.396
Infliximab 3 vs never taking Mild 1.105 0.527 2.320
Infliximab 3 vs never taking Mod 1.348 0.843 2.157
Infliximab 3 vs never taking Severe 1.833 1.102 3.048
432
Odds Ratio Estimates
Effect InfGit Point Estimate
95% Wald
Confidence Limits
Infliximab 4 vs never taking Mild <0.001 <0.001 >999.999
Infliximab 4 vs never taking Mod 4.013 1.619 9.946
Infliximab 4 vs never taking Severe 0.935 0.113 7.748
Infliximab currently taking vs never taking Mild 0.546 0.134 2.223
Infliximab currently taking vs never taking Mod 1.999 1.098 3.640
Infliximab currently taking vs never taking Severe 1.271 0.512 3.155
Cyclosporin 3 vs never taking Mild 1.228 0.737 2.047
Cyclosporin 3 vs never taking Mod 1.185 0.842 1.667
Cyclosporin 3 vs never taking Severe 0.981 0.637 1.511
Cyclosporin 4 vs never taking Mild 0.901 0.125 6.508
Cyclosporin 4 vs never taking Mod 0.187 0.024 1.455
Cyclosporin 4 vs never taking Severe 0.431 0.056 3.295
Cyclosporin currently taking vs never taking Mild 6.644 2.651 16.651
Cyclosporin currently taking vs never taking Mod 6.209 3.088 12.484
Cyclosporin currently taking vs never taking Severe 2.123 0.518 8.707
Prednisolone 3 vs never taking Mild 1.450 0.809 2.600
Prednisolone 3 vs never taking Mod 1.589 0.993 2.543
Prednisolone 3 vs never taking Severe 0.935 0.520 1.679
Prednisolone 4 vs never taking Mild <0.001 <0.001 >999.999
Prednisolone 4 vs never taking Mod 5.335 1.125 25.308
Prednisolone 4 vs never taking Severe 3.538 0.418 29.923
Prednisolone currently taking vs never taking Mild 1.205 0.668 2.174
Prednisolone currently taking vs never taking Mod 1.551 0.972 2.475
Prednisolone currently taking vs never taking Severe 1.722 0.999 2.970
433
APPENDIX H: OUTPUT OF SAS FOR
NERVOUS SYSTEM INFECTION
Table H.1- Complete statistics for Nervous system infection
Model Information
Data Set WORK.IMPORT2
Response Variable InfNeuro InfNeuro
Number of Response Levels 4
Model generalized logit
Optimization Technique Newton-Raphson
Table H.2- Observation status for Nervous system infection
Number of Observations Read 27711
Number of Observations Used 21506
434
Table H.3- response value for Nervous system infection
Response Profile
Ordered
Value InfNeuro
Total
Frequency
1 1 9
2 2 9
3 3 12
4 4 21476
Logits modelled use InfNeuro='4' as the reference category.
Note: 6205 observations were deleted due to missing values for the response or explanatory
variables.
Table H.4- Backward Elimination Procedure for Nervous system infection
Backward Elimination Procedure
Class Level Information
Class Value Design Variables
Etanercept 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Adalimumab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Anakinra 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
435
never taking 0 0 0 1
Infliximab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Rituximab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Abatacept 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Tocilizumab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Golimumab 3 1 0 0
currently taking 0 1 0
never taking 0 0 1
Certolizumab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Folic Acid currently taking 1 0
never taking 0 1
Hydroxychloroquine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Sulphasalazine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
436
never taking 0 0 0 1
Arava (Leflunomide) 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Azathioprine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Cyclosporin 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Prednisolone 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
IM Gold injection 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Penicillamine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Step 0. The following effects were entered:
Intercept Etanercept Adalimumab Anakinra Infliximab Rituximab Abatacept Tocilizumab
Golimumab Certolizumab Folic Acid Hydroxychloroquine Sulphasalazine Arava
(Leflunomide) Azathioprine Cyclosporin Prednisolone IM Gold injection Penicillamine
437
Table H.6- Model Fit statistics for Nervous system infection
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 525.786 719.397
SC 549.714 1963.667
-2 Log L 519.786 407.397
Table H.7- Testing null hypothesis for Nervous system infection
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 112.3885 153 0.9943
Score 170.5176 153 0.1578
Wald 83.3711 153 1.0000
Table H.8- Model Fit statistics for removing covariant step 1
Step 1. Effect Certolizumab is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
Table H.11- Residual removing covariant step 1
438
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
0.9204 9 0.9996
Table H.12- Model Fit statistics for removing covariant step 2
Step 2. Effect Anakinra is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
439
Table H.13- Model Fit statistics after removing covariant step 2
Table H.14- Testing Null hypothesis after removing covariant step 2
Table H.15- Residual removing covariant step 2
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 525.786 686.901
SC 549.714 1787.601
-2 Log L 519.786 410.901
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 108.8846 135 0.9519
Score 158.9265 135 0.0781
Wald 81.8673 135 0.9999
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
1.9099 18 1.0000
440
Table H.16- Model Fit statistics for removing covariant step 3
Model Convergence Status
Quasi-complete separation of data points detected.
Table H.17- Model Fit statistics after removing covariant step 3
Step 3. Effect Golimumab is removed:
Table H.18- Testing Null hypothesis after removing covariant step 3
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 106.0818 129 0.9305
Score 156.3178 129 0.0511
Wald 80.1217 129 0.9998
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 525.786 677.704
SC 549.714 1730.547
-2 Log L 519.786 413.704
441
Table H.19- Residual removing covariant step 3
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
3.7095 24 1.0000
Table H.20- Model Fit statistics for removing covariant step 4
Step 4. Effect Penicillamine is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
Table H.21- Model Fit statistics after removing covariant step 4
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 525.786 663.254
SC 549.714 1644.313
-2 Log L 519.786 417.254
442
Table H.22- Testing Null hypothesis after removing covariant step 4
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 102.5317 120 0.8737
Score 149.3559 120 0.0358
Wald 78.8583 120 0.9986
Table H.23- Residual removing covariant step 4
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
5.9028 33 1.0000
Table H.24- Model Fit statistics for removing covariant step 5
Step 5. Effect Tocilizumab is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
443
Table H.25- Model Fit statistics after removing covariant step 5
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 525.786 649.106
SC 549.714 1558.380
-2 Log L 519.786 421.106
Table H.26- Testing Null hypothesis after removing covariant step 5
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 98.6796 111 0.7923
Score 143.4857 111 0.0206
Wald 77.1681 111 0.9939
Table H.27- Residual removing covariant step 5
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
9.2419 42 1.0000
444
Table H.28- Model Fit statistics for removing covariant step 6
Step 6. Effect Rituximab is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
Table H.29- Model Fit statistics after removing covariant step 6
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 525.786 634.968
SC 549.714 1472.457
-2 Log L 519.786 424.968
Table H.30- Testing Null hypothesis after removing covariant step 6
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 94.8175 102 0.6802
Score 132.3092 102 0.0234
Wald 74.4728 102 0.9815
Table H.31- Residual removing covariant step 6
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
12.5221 51 1.0000
445
Table H.32- Model Fit statistics for removing covariant step 7
Step 7. Effect Arava (Leflunomide) is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
Table H.33- Model Fit statistics after removing covariant step 7
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 525.786 622.390
SC 549.714 1388.095
-2 Log L 519.786 430.390
446
Table H.34- Testing Null hypothesis after removing covariant step 7
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 89.3954 93 0.5866
Score 127.7230 93 0.0099
Wald 75.4615 93 0.9078
Table H.35- Residual removing covariant step 7
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
15.8844 60 1.0000
Table H.36- Model Fit statistics for removing covariant step 8
Step 8. Effect Abatacept is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
447
Table H.37- Model Fit statistics after removing covariant step 8
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 525.786 606.681
SC 549.714 1300.601
-2 Log L 519.786 432.681
Table H.38- Testing Null hypothesis after removing covariant step 8
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 87.1046 84 0.3867
Score 124.9842 84 0.0025
Wald 75.0946 84 0.7457
Table H.39- Residual removing covariant step 8
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
18.2897 69 1.0000
Table H.40- Model Fit statistics for removing covariant step 9
Step 9. Effect Hydroxychloroquine is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
Table H.41- Model Fit statistics after removing covariant step 9
448
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 525.786 592.141
SC 549.714 1214.276
-2 Log L 519.786 436.141
Table H.42- Testing Null hypothesis after removing covariant step 9
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 83.6442 75 0.2314
Score 120.9163 75 0.0006
Wald 72.3119 75 0.5665
Table H.43- Residual removing covariant step 9
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
21.4107 78 1.0000
449
Table H.44- Model Fit statistics for removing covariant step 10
Step 10. Effect Adalimumab is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
Table H.45- Model Fit statistics after removing covariant step 10
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 525.786 585.500
SC 549.714 1135.851
-2 Log L 519.786 447.500
Table H.46- Testing Null hypothesis after removing covariant step 10
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 72.2850 66 0.2782
Score 103.0295 66 0.0024
Wald 68.9775 66 0.3771
450
Table H.47- Residual removing covariant step 10
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
31.5451 87 1.0000
Table H.48- Model Fit statistics for removing covariant step 11
Step 11. Effect Infliximab is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
Table H.49- Model Fit statistics after removing covariant step 11
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 525.786 576.716
SC 549.714 1055.281
-2 Log L 519.786 456.716
451
Table H.50- Testing Null hypothesis after removing covariant step 11
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 63.0694 57 0.2705
Score 84.0113 57 0.0115
Wald 60.0232 57 0.3667
Table H.51- Residual removing covariant step 11
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
44.0268 96 1.0000
Table H.52- Model Fit statistics for removing covariant step 12
Step 12. Effect Prednisolone is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
Table H.53- Model Fit statistics for removing covariant step 12
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 525.786 566.295
SC 549.714 973.076
-2 Log L 519.786 464.295
452
Table H.54- Testing Null hypothesis after removing covariant step 12
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 55.4901 48 0.2132
Score 78.2457 48 0.0038
Wald 58.2370 48 0.1479
Table H.55- Residual removing covariant step 12
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
50.5258 105 1.0000
Table H.56- Model Fit statistics for removing covariant step 13
Step 13. Effect Etanercept is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
453
Table H.57- Model Fit statistics after removing covariant step 13
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 525.786 555.825
SC 549.714 890.820
-2 Log L 519.786 471.825
Table H.58- Testing Null hypothesis after removing covariant step 13
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 47.9610 39 0.1538
Score 69.5512 39 0.0019
Wald 51.9824 39 0.0798
Table H.59- Residual removing covariant step 13
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
60.1603 114 1.0000
Table H.60- Model Fit statistics for removing covariant step 14
Step 14. Effect Folic Acid is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
Table H.61- Model Fit statistics after removing covariant step 14
454
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 525.786 552.316
SC 549.714 863.383
-2 Log L 519.786 474.316
Table H.62- Testing Null hypothesis after removing covariant step 14
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 45.4695 36 0.1339
Score 67.3854 36 0.0012
Wald 49.9133 36 0.0615
Table H.63- Residual removing covariant step 14
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
62.5527 117 1.0000
455
Table H.64- Model Fit statistics for removing covariant step 15
Step 15. Effect IM Gold injection is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
Table H.65- Model Fit statistics after removing covariant step 15
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 525.786 551.109
SC 549.714 790.391
-2 Log L 519.786 491.109
Table H.66- Testing Null hypothesis after removing covariant step 15
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 28.6770 27 0.3767
Score 52.8187 27 0.0021
Wald 37.7840 27 0.0813
456
Table H.67- Residual removing covariant step 15
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
79.9812 126 0.9995
Table H.68(1)- Model Fit statistics for removing covariant step 16
Step 16. Effect Sulphasalazine is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
Table H.68 (2)- Model Fit statistics after removing covariant step 16
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 525.786 541.646
SC 549.714 709.144
-2 Log L 519.786 499.646
457
Table H.69- Testing Null hypothesis after removing covariant step 16
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 20.1392 18 0.3250
Score 41.3631 18 0.0014
Wald 28.2599 18 0.0582
Table H.70- Residual removing covariant step 16
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
99.5067 135 0.9904
Table H.71- Model Fit statistics for removing covariant step 17
Step 17. Effect Cyclosporin is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
Warning: The maximum likelihood estimate may not exist.
Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown
are based on the last maximum likelihood iteration. Validity of the model fit is questionable
Table H.72- Model Fit statistics after removing covariant step 17
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 525.786 534.280
SC 549.714 629.993
-2 Log L 519.786 510.280
458
Table H.73- Testing Null hypothesis after removing covariant step 17
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 9.5056 9 0.3920
Score 16.0170 9 0.0665
Wald 10.8344 9 0.2872
Table H.74- Residual removing covariant step 17
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
123.0393 144 0.8963
Table H.75- Model Fit statistics for removing covariant step 18
Step 18. Effect Azathioprine is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
459
Table H.76- Model Fit statistics after removing covariant step 18
-2 Log L = 519.786
Table H.77- Testing Null hypothesis after removing covariant step 18
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
170.5176 153 0.1578
Table H.78- Summary of backward elimination in Nervous system infection
Note: All effects have been removed from the model.
Summary of Backward Elimination
Step
Effect
Removed DF
Number
In
Wald
Chi-
Square Pr > ChiSq
Variable
Label
1 Certolizumab 9 17 0.0029 1.0000 Certolizumab
2 Anakinra 9 16 0.0051 1.0000
3 Golimumab 6 15 0.0054 1.0000 Golimumab
4 Penicillamine 9 14 0.3675 1.0000 Penicillamine
5 Tocilizumab 9 13 0.5269 1.0000 Tocilizumab
6 Rituximab 9 12 0.5838 0.9999 Rituximab
7 Arava (Leflunomide) 9 11 1.0212 0.9994 Arava (Leflunomide)
8 Abatacept 9 10 1.5025 0.9971 Abatacept
9 Hydroxychloroquine 9 9 2.5989 0.9781 Hydroxychloroquine
10 Adalimumab 9 8 4.0119 0.9106
11 Infliximab 9 7 4.8230 0.8495
12 Prednisolone 9 6 4.6980 0.8598 Prednisolone
13 Etanercept 9 5 6.7886 0.6591
14 Folic Acid 3 4 2.0592 0.5602 Folic Acid
15 IM Gold injection 9 3 8.1059 0.5235 IM Gold injection
16 Sulphasalazine 9 2 8.6399 0.4712 Sulphasalazine
460
17 Cyclosporin 9 1 11.0409 0.2729 Cyclosporin
18 Azathioprine 9 0 10.8344 0.2872 Azathioprine
Table H.79- Analysis of maximum likelihood estimates in Nervous system infection
Analysis of Maximum Likelihood Estimates
Parameter InfNeuro DF Estimate
Standard
Error
Wald
Chi-Square Pr > ChiSq
Intercept 1 1 -7.7775 0.3334 544.1729 <.0001
Intercept 2 1 -7.7775 0.3334 544.1729 <.0001
Intercept 3 1 -7.4898 0.2888 672.7866 <.0001
461
APPENDIX I: OUTPUT OF SAS FOR
TB INFECTION
Table I.1- Complete statistics for TB infection
Model Information
Data Set WORK.IMPORT2
Response Variable TB Infection TB Infection
Number of Response Levels 4
Model generalized logit
Optimization Technique Newton-Raphson
Table I.2- Observation status for TB infection
Number of Observations Read 27711
Number of Observations Used 21506
Table I.3- response value for TB infection
Response Profile
Ordered
Value TB Infection
Total
Frequency
1 1 1050
2 2 1829
3 3 406
4 4 18221
Logits modelled use TB Infection='4' as the reference category.
462
Note: 6205 observations were deleted due to missing values for the response or explanatory
variables.
Table I.4- Backward Elimination Procedure for TB infection
Backward Elimination Procedure
Class Level Information
Class Value Design Variables
Etanercept 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Adalimumab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Anakinra 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Infliximab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Rituximab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Abatacept 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Tocilizumab 3 1 0 0 0
463
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Golimumab 3 1 0 0
currently taking 0 1 0
never taking 0 0 1
Certolizumab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Folic Acid currently taking 1 0
never taking 0 1
Hydroxychloroquine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Sulphasalazine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Arava (Leflunomide) 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Azathioprine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Cyclosporin 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Prednisolone 3 1 0 0 0
464
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
IM Gold injection 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Penicillamine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Step 0. The following effects were entered:
Intercept Etanercept Adalimumab Anakinra Infliximab Rituximab Abatacept Tocilizumab
Golimumab Certolizumab Folic Acid Hydroxychloroquine Sulphasalazine Arava
(Leflunomide) Azathioprine Cyclosporin Prednisolone IM Gold injection Penicillamine
Table I.5- Model Convergence status for TB infection
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table I.6- Model Fit statistics for TB infection
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 24626.355 24501.128
SC 24650.284 25745.398
-2 Log L 24620.355 24189.128
465
Table I.7- Testing null hypothesis for TB infection
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 431.2272 153 <.0001
Score 463.0664 153 <.0001
Wald 419.5882 153 <.0001
Table I.8- Model Fit statistics for removing covariant step 1
Step 1. Effect Azathioprine is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table I.9- Model Fit statistics for removing covariant step 1
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 24626.355 24488.566
SC 24650.284 25661.051
-2 Log L 24620.355 24194.566
Table I.10- Testing Null hypothesis after removing covariant step 1
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 425.7897 144 <.0001
Score 457.8861 144 <.0001
Wald 415.1007 144 <.0001
466
Table I.11- Residual removing covariant step 1
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
5.0524 9 0.8297
467
Table I.12- Model Fit statistics for removing covariant step 2
Step 2. Effect Certolizumab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table I.13- Model Fit statistics after removing covariant step 2
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 24626.355 24476.658
SC 24650.284 25577.358
-2 Log L 24620.355 24200.658
Table I.14- Testing Null hypothesis after removing covariant step 2
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 419.6974 135 <.0001
Score 450.9468 135 <.0001
Wald 408.7712 135 <.0001
468
Table I.15- Residual removing covariant step 2
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
10.9161 18 0.8979
Table I.16- Model Fit statistics for removing covariant step 3
Step 3. Effect Penicillamine is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table I.17- Model Fit statistics after removing covariant step 3
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 24626.355 24473.673
SC 24650.284 25502.589
-2 Log L 24620.355 24215.673
469
Table I.18- Testing Null hypothesis after removing covariant step 3
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 404.6821 126 <.0001
Score 440.0301 126 <.0001
Wald 401.9656 126 <.0001
Table I.19- Residual removing covariant step 3
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
22.0077 27 0.7370
Table I.20- Model Fit statistics for removing covariant step 4
Step 4. Effect IM Gold injection is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
470
Table I.21- Model Fit statistics after removing covariant step 4
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 24626.355 24465.880
SC 24650.284 25423.011
-2 Log L 24620.355 24225.880
Table I.22- Testing Null hypothesis after removing covariant step 4
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 394.4751 117 <.0001
Score 430.4313 117 <.0001
Wald 392.2553 117 <.0001
Table I.23- Residual removing covariant step 4
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
31.2787 36 0.6926
471
Table I.24- Model Fit statistics for removing covariant step 5
Step 5. Effect Rituximab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table I.25- Model Fit statistics after removing covariant step 5
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 24626.355 24461.305
SC 24650.284 25346.650
-2 Log L 24620.355 24239.305
Table I.26- Testing Null hypothesis after removing covariant step 5
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 381.0509 108 <.0001
Score 415.9895 108 <.0001
Wald 378.4582 108 <.0001
472
Table I.27- Residual removing covariant step 5
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
44.9975 45 0.4721
Table I.28- Model Fit statistics for removing covariant step 6
Step 6. Effect Golimumab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table I.29- Model Fit statistics after removing covariant step 6
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 24626.355 24462.511
SC 24650.284 25300.000
-2 Log L 24620.355 24252.511
Table I.30- Testing Null hypothesis after removing covariant step 6
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 367.8443 102 <.0001
Score 403.4935 102 <.0001
Wald 366.8141 102 <.0001
Table I.31- Residual removing covariant step 6
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
57.2672 51 0.2539
473
Note: No (additional) effects met the 0.05 significance level for removal from the model.
Table I.32- Summary of backward elimination in TB infection
Summary of Backward Elimination
Step
Effect
Removed DF
Number
In
Wald
Chi-Square Pr > ChiSq
Variable
Label
1 Azathioprine 9 17 4.9893 0.8352 Azathioprine
2 Certolizumab 9 16 5.4537 0.7931 Certolizumab
3 Penicillamine 9 15 7.1956 0.6168 Penicillamine
4 IM Gold injection 9 14 9.1915 0.4198 IM Gold injection
5 Rituximab 9 13 13.6536 0.1352 Rituximab
6 Golimumab 6 12 11.2165 0.0819 Golimumab
474
Table I.33- Type 3 analysis of effects in TB infection
Type 3 Analysis of Effects
Effect DF
Wald
Chi-Square Pr > ChiSq
Etanercept 9 52.1431 <.0001
Adalimumab 9 22.4139 0.0077
Anakinra 9 18.2690 0.0322
Infliximab 9 31.0160 0.0003
Abatacept 9 18.0153 0.0350
Tocilizumab 9 18.1032 0.0340
Folic Acid 3 9.4165 0.0242
Hydroxychloroquine 9 23.3663 0.0054
Sulphasalazine 9 26.7402 0.0015
Arava (Leflunomide) 9 17.5339 0.0410
Cyclosporin 9 47.3358 <.0001
Prednisolone 9 29.4764 0.0005
Table I.34- Analysis of maximum likelihood estimates in TB infection
Analysis of Maximum Likelihood Estimates
Parameter
TB
Infection DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Intercept 1 1 -3.4872 0.1190 859.2759 <.0001
Intercept 2 1 -2.9786 0.0928 1031.0501 <.0001
Intercept 3 1 -4.3609 0.1917 517.2695 <.0001
Etanercept 3 1 1 -0.0509 0.0911 0.3118 0.5766
Etanercept 3 2 1 -0.0713 0.0705 1.0220 0.3120
Etanercept 3 3 1 -0.3981 0.1457 7.4653 0.0063
Etanercept 4 1 1 1.3033 0.5444 5.7307 0.0167
Etanercept 4 2 1 1.9227 0.3431 31.3968 <.0001
Etanercept 4 3 1 1.3439 0.8633 2.4234 0.1195
475
Analysis of Maximum Likelihood Estimates
Parameter
TB
Infection DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Etanercept currently
taking
1 1 0.1730 0.0941 3.3831 0.0659
Etanercept currently
taking
2 1 0.0891 0.0722 1.5232 0.2171
Etanercept currently
taking
3 1 -0.3383 0.1446 5.4736 0.0193
Etanercept never taking 1 0 0 . . .
Etanercept never taking 2 0 0 . . .
Etanercept never taking 3 0 0 . . .
Adalimumab 3 1 1 0.0104 0.0914 0.0129 0.9094
Adalimumab 3 2 1 0.1823 0.0686 7.0504 0.0079
Adalimumab 3 3 1 0.1418 0.1403 1.0222 0.3120
Adalimumab 4 1 1 -0.5402 0.6756 0.6394 0.4239
Adalimumab 4 2 1 -
0.00090
0.4440 0.0000 0.9984
Adalimumab 4 3 1 -
10.2462
147.6 0.0048 0.9447
Adalimumab currently
taking
1 1 0.2887 0.0941 9.4206 0.0021
Adalimumab currently
taking
2 1 0.1847 0.0737 6.2813 0.0122
Adalimumab currently
taking
3 1 -0.0798 0.1470 0.2946 0.5873
Adalimumab never taking 1 0 0 . . .
Adalimumab never taking 2 0 0 . . .
Adalimumab never taking 3 0 0 . . .
Anakinra 3 1 1 0.1448 0.2523 0.3295 0.5659
Anakinra 3 2 1 -0.0761 0.2191 0.1205 0.7285
Anakinra 3 3 1 0.4597 0.3413 1.8146 0.1780
Anakinra 4 1 1 -0.7187 0.6297 1.3026 0.2537
Anakinra 4 2 1 0.0275 0.4047 0.0046 0.9459
476
Analysis of Maximum Likelihood Estimates
Parameter
TB
Infection DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Anakinra 4 3 1 -0.4484 1.0513 0.1819 0.6697
Anakinra currently
taking
1 1 -
11.9697
512.1 0.0005 0.9814
Anakinra currently
taking
2 1 1.7999 0.4745 14.3901 0.0001
Anakinra currently
taking
3 1 -
12.2422
809.5 0.0002 0.9879
Anakinra never taking 1 0 0 . . .
Anakinra never taking 2 0 0 . . .
Anakinra never taking 3 0 0 . . .
Infliximab 3 1 1 0.0552 0.1337 0.1707 0.6795
Infliximab 3 2 1 -0.2055 0.1098 3.5047 0.0612
Infliximab 3 3 1 0.0478 0.1974 0.0585 0.8088
Infliximab 4 1 1 0.4422 0.4291 1.0621 0.3027
Infliximab 4 2 1 -0.1974 0.3745 0.2779 0.5981
Infliximab 4 3 1 -0.8062 0.8952 0.8110 0.3678
Infliximab currently
taking
1 1 0.6440 0.1747 13.5909 0.0002
Infliximab currently
taking
2 1 0.4727 0.1396 11.4614 0.0007
Infliximab currently
taking
3 1 -0.3706 0.3711 0.9971 0.3180
Infliximab never taking 1 0 0 . . .
Infliximab never taking 2 0 0 . . .
Infliximab never taking 3 0 0 . . .
Abatacept 3 1 1 0.5166 0.1769 8.5260 0.0035
Abatacept 3 2 1 0.1147 0.1534 0.5587 0.4548
Abatacept 3 3 1 -0.4339 0.3573 1.4747 0.2246
Abatacept 4 1 1 0.2751 0.8455 0.1059 0.7449
Abatacept 4 2 1 -0.6022 0.6388 0.8887 0.3458
Abatacept 4 3 1 1.3251 1.0615 1.5582 0.2119
477
Analysis of Maximum Likelihood Estimates
Parameter
TB
Infection DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Abatacept currently
taking
1 1 0.3362 0.1582 4.5176 0.0335
Abatacept currently
taking
2 1 0.1491 0.1240 1.4460 0.2292
Abatacept currently
taking
3 1 -0.2016 0.2673 0.5686 0.4508
Abatacept never taking 1 0 0 . . .
Abatacept never taking 2 0 0 . . .
Abatacept never taking 3 0 0 . . .
Tocilizumab 3 1 1 0.1595 0.2454 0.4224 0.5157
Tocilizumab 3 2 1 0.1835 0.1951 0.8847 0.3469
Tocilizumab 3 3 1 0.7127 0.3269 4.7534 0.0292
Tocilizumab 4 1 1 -
11.4739
529.0 0.0005 0.9827
Tocilizumab 4 2 1 -
11.5154
222.6 0.0027 0.9587
Tocilizumab 4 3 1 -
10.9097
820.5 0.0002 0.9894
Tocilizumab currently
taking
1 1 0.4933 0.1695 8.4682 0.0036
Tocilizumab currently
taking
2 1 0.3301 0.1348 5.9962 0.0143
Tocilizumab currently
taking
3 1 0.1795 0.2814 0.4069 0.5236
Tocilizumab never taking 1 0 0 . . .
Tocilizumab never taking 2 0 0 . . .
Tocilizumab never taking 3 0 0 . . .
Folic Acid currently
taking
1 1 -0.1059 0.0761 1.9365 0.1641
Folic Acid currently
taking
2 1 -0.1683 0.0598 7.9220 0.0049
478
Analysis of Maximum Likelihood Estimates
Parameter
TB
Infection DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Folic Acid currently
taking
3 1 -0.0493 0.1190 0.1713 0.6789
Folic Acid never taking 1 0 0 . . .
Folic Acid never taking 2 0 0 . . .
Folic Acid never taking 3 0 0 . . .
Hydroxychloroquine 3 1 1 0.1431 0.0736 3.7794 0.0519
Hydroxychloroquine 3 2 1 0.2299 0.0575 15.9873 <.0001
Hydroxychloroquine 3 3 1 0.1860 0.1175 2.5057 0.1134
Hydroxychloroquine 4 1 1 -0.0695 0.4165 0.0278 0.8676
Hydroxychloroquine 4 2 1 -0.0273 0.3338 0.0067 0.9348
Hydroxychloroquine 4 3 1 0.6305 0.5074 1.5444 0.2140
Hydroxychloroquine currently
taking
1 1 0.0100 0.0960 0.0109 0.9168
Hydroxychloroquine currently
taking
2 1 0.0789 0.0753 1.0991 0.2945
Hydroxychloroquine currently
taking
3 1 0.0332 0.1546 0.0463 0.8297
Hydroxychloroquine never taking 1 0 0 . . .
Hydroxychloroquine never taking 2 0 0 . . .
Hydroxychloroquine never taking 3 0 0 . . .
Sulphasalazine 3 1 1 0.0933 0.0714 1.7093 0.1911
Sulphasalazine 3 2 1 0.2229 0.0554 16.1883 <.0001
Sulphasalazine 3 3 1 0.1577 0.1136 1.9284 0.1649
Sulphasalazine 4 1 1 0.1273 0.3002 0.1799 0.6714
Sulphasalazine 4 2 1 -0.2181 0.2582 0.7132 0.3984
Sulphasalazine 4 3 1 0.6855 0.3912 3.0697 0.0798
Sulphasalazine currently
taking
1 1 0.1470 0.1112 1.7468 0.1863
Sulphasalazine currently
taking
2 1 0.00403 0.0926 0.0019 0.9653
479
Analysis of Maximum Likelihood Estimates
Parameter
TB
Infection DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Sulphasalazine currently
taking
3 1 -0.0445 0.1896 0.0551 0.8144
Sulphasalazine never taking 1 0 0 . . .
Sulphasalazine never taking 2 0 0 . . .
Sulphasalazine never taking 3 0 0 . . .
Arava (Leflunomide) 3 1 1 0.1098 0.0935 1.3804 0.2400
Arava (Leflunomide) 3 2 1 0.1933 0.0729 7.0343 0.0080
Arava (Leflunomide) 3 3 1 0.1484 0.1434 1.0712 0.3007
Arava (Leflunomide) 4 1 1 -0.2856 0.5428 0.2768 0.5988
Arava (Leflunomide) 4 2 1 0.4582 0.3091 2.1967 0.1383
Arava (Leflunomide) 4 3 1 -0.7134 1.0581 0.4546 0.5002
Arava (Leflunomide) currently
taking
1 1 0.2705 0.1060 6.5075 0.0107
Arava (Leflunomide) currently
taking
2 1 0.1492 0.0858 3.0250 0.0820
Arava (Leflunomide) currently
taking
3 1 0.00639 0.1726 0.0014 0.9705
Arava (Leflunomide) never taking 1 0 0 . . .
Arava (Leflunomide) never taking 2 0 0 . . .
Arava (Leflunomide) never taking 3 0 0 . . .
Cyclosporin 3 1 1 0.0263 0.0937 0.0789 0.7788
Cyclosporin 3 2 1 0.2042 0.0692 8.7084 0.0032
Cyclosporin 3 3 1 0.4662 0.1325 12.3789 0.0004
Cyclosporin 4 1 1 -0.2418 0.3214 0.5662 0.4518
Cyclosporin 4 2 1 0.0673 0.2205 0.0931 0.7603
Cyclosporin 4 3 1 -1.0526 0.7303 2.0770 0.1495
Cyclosporin currently
taking
1 1 0.5290 0.3373 2.4603 0.1168
Cyclosporin currently
taking
2 1 1.0439 0.2236 21.7983 <.0001
480
Analysis of Maximum Likelihood Estimates
Parameter
TB
Infection DF Estimate
Standard
Error
Wald
Chi-
Square Pr > ChiSq
Cyclosporin currently
taking
3 1 1.0216 0.4398 5.3965 0.0202
Cyclosporin never taking 1 0 0 . . .
Cyclosporin never taking 2 0 0 . . .
Cyclosporin never taking 3 0 0 . . .
Prednisolone 3 1 1 0.3310 0.1083 9.3359 0.0022
Prednisolone 3 2 1 0.2552 0.0838 9.2693 0.0023
Prednisolone 3 3 1 0.4980 0.1834 7.3738 0.0066
Prednisolone 4 1 1 0.7838 0.5610 1.9520 0.1624
Prednisolone 4 2 1 0.5466 0.4327 1.5961 0.2065
Prednisolone 4 3 1 0.7162 1.0389 0.4753 0.4906
Prednisolone currently
taking
1 1 0.1671 0.1087 2.3642 0.1241
Prednisolone currently
taking
2 1 0.1308 0.0838 2.4345 0.1187
Prednisolone currently
taking
3 1 0.3911 0.1833 4.5509 0.0329
Prednisolone never taking 1 0 0 . . .
Prednisolone never taking 2 0 0 . . .
Prednisolone never taking 3 0 0 . . .
Table I.35- Odds ratio estimates in TB infection
Odds Ratio Estimates
Effect
TB
Infection
Point
Estimate
95% Wald
Confidence Limits
Etanercept 3 vs never taking 1 0.950 0.795 1.136
Etanercept 3 vs never taking 2 0.931 0.811 1.069
Etanercept 3 vs never taking 3 0.672 0.505 0.894
Etanercept 4 vs never taking 1 3.682 1.266 10.702
Etanercept 4 vs never taking 2 6.840 3.491 13.400
481
Odds Ratio Estimates
Effect
TB
Infection
Point
Estimate
95% Wald
Confidence Limits
Etanercept 4 vs never taking 3 3.834 0.706 20.819
Etanercept currently taking vs never taking 1 1.189 0.989 1.430
Etanercept currently taking vs never taking 2 1.093 0.949 1.259
Etanercept currently taking vs never taking 3 0.713 0.537 0.947
Adalimumab 3 vs never taking 1 1.010 0.845 1.209
Adalimumab 3 vs never taking 2 1.200 1.049 1.373
Adalimumab 3 vs never taking 3 1.152 0.875 1.517
Adalimumab 4 vs never taking 1 0.583 0.155 2.190
Adalimumab 4 vs never taking 2 0.999 0.419 2.385
Adalimumab 4 vs never taking 3 <0.001 <0.001 >999.999
Adalimumab currently taking vs never taking 1 1.335 1.110 1.605
Adalimumab currently taking vs never taking 2 1.203 1.041 1.390
Adalimumab currently taking vs never taking 3 0.923 0.692 1.232
Anakinra 3 vs never taking 1 1.156 0.705 1.895
Anakinra 3 vs never taking 2 0.927 0.603 1.424
Anakinra 3 vs never taking 3 1.584 0.811 3.091
Anakinra 4 vs never taking 1 0.487 0.142 1.675
Anakinra 4 vs never taking 2 1.028 0.465 2.272
Anakinra 4 vs never taking 3 0.639 0.081 5.013
Anakinra currently taking vs never taking 1 <0.001 <0.001 >999.999
Anakinra currently taking vs never taking 2 6.049 2.387 15.330
Anakinra currently taking vs never taking 3 <0.001 <0.001 >999.999
Infliximab 3 vs never taking 1 1.057 0.813 1.373
Infliximab 3 vs never taking 2 0.814 0.657 1.010
Infliximab 3 vs never taking 3 1.049 0.712 1.544
Infliximab 4 vs never taking 1 1.556 0.671 3.608
Infliximab 4 vs never taking 2 0.821 0.394 1.710
Infliximab 4 vs never taking 3 0.447 0.077 2.582
Infliximab currently taking vs never taking 1 1.904 1.352 2.682
Infliximab currently taking vs never taking 2 1.604 1.220 2.109
Infliximab currently taking vs never taking 3 0.690 0.334 1.429
Abatacept 3 vs never taking 1 1.676 1.185 2.371
482
Odds Ratio Estimates
Effect
TB
Infection
Point
Estimate
95% Wald
Confidence Limits
Abatacept 3 vs never taking 2 1.122 0.830 1.515
Abatacept 3 vs never taking 3 0.648 0.322 1.305
Abatacept 4 vs never taking 1 1.317 0.251 6.905
Abatacept 4 vs never taking 2 0.548 0.157 1.915
Abatacept 4 vs never taking 3 3.763 0.470 30.134
Abatacept currently taking vs never taking 1 1.400 1.027 1.908
Abatacept currently taking vs never taking 2 1.161 0.910 1.480
Abatacept currently taking vs never taking 3 0.817 0.484 1.380
Tocilizumab 3 vs never taking 1 1.173 0.725 1.897
Tocilizumab 3 vs never taking 2 1.201 0.820 1.761
Tocilizumab 3 vs never taking 3 2.039 1.075 3.870
Tocilizumab 4 vs never taking 1 <0.001 <0.001 >999.999
Tocilizumab 4 vs never taking 2 <0.001 <0.001 >999.999
Tocilizumab 4 vs never taking 3 <0.001 <0.001 >999.999
Tocilizumab currently taking vs never taking 1 1.638 1.175 2.283
Tocilizumab currently taking vs never taking 2 1.391 1.068 1.812
Tocilizumab currently taking vs never taking 3 1.197 0.689 2.077
Folic Acid currently taking vs never taking 1 0.899 0.775 1.044
Folic Acid currently taking vs never taking 2 0.845 0.752 0.950
Folic Acid currently taking vs never taking 3 0.952 0.754 1.202
Hydroxychloroquine 3 vs never taking 1 1.154 0.999 1.333
Hydroxychloroquine 3 vs never taking 2 1.259 1.124 1.409
Hydroxychloroquine 3 vs never taking 3 1.204 0.957 1.516
Hydroxychloroquine 4 vs never taking 1 0.933 0.412 2.110
Hydroxychloroquine 4 vs never taking 2 0.973 0.506 1.872
Hydroxychloroquine 4 vs never taking 3 1.879 0.695 5.078
Hydroxychloroquine currently taking vs never
taking
1 1.010 0.837 1.219
Hydroxychloroquine currently taking vs never
taking
2 1.082 0.934 1.254
Hydroxychloroquine currently taking vs never
taking
3 1.034 0.764 1.400
483
Odds Ratio Estimates
Effect
TB
Infection
Point
Estimate
95% Wald
Confidence Limits
Sulphasalazine 3 vs never taking 1 1.098 0.955 1.263
Sulphasalazine 3 vs never taking 2 1.250 1.121 1.393
Sulphasalazine 3 vs never taking 3 1.171 0.937 1.463
Sulphasalazine 4 vs never taking 1 1.136 0.631 2.046
Sulphasalazine 4 vs never taking 2 0.804 0.485 1.334
Sulphasalazine 4 vs never taking 3 1.985 0.922 4.273
Sulphasalazine currently taking vs never taking 1 1.158 0.931 1.440
Sulphasalazine currently taking vs never taking 2 1.004 0.837 1.204
Sulphasalazine currently taking vs never taking 3 0.956 0.660 1.387
Arava (Leflunomide) 3 vs never taking 1 1.116 0.929 1.341
Arava (Leflunomide) 3 vs never taking 2 1.213 1.052 1.399
Arava (Leflunomide) 3 vs never taking 3 1.160 0.876 1.536
Arava (Leflunomide) 4 vs never taking 1 0.752 0.259 2.178
Arava (Leflunomide) 4 vs never taking 2 1.581 0.863 2.898
Arava (Leflunomide) 4 vs never taking 3 0.490 0.062 3.898
Arava (Leflunomide) currently taking vs never
taking
1 1.311 1.065 1.613
Arava (Leflunomide) currently taking vs never
taking
2 1.161 0.981 1.374
Arava (Leflunomide) currently taking vs never
taking
3 1.006 0.717 1.412
Cyclosporin 3 vs never taking 1 1.027 0.854 1.234
Cyclosporin 3 vs never taking 2 1.227 1.071 1.405
Cyclosporin 3 vs never taking 3 1.594 1.229 2.066
Cyclosporin 4 vs never taking 1 0.785 0.418 1.474
Cyclosporin 4 vs never taking 2 1.070 0.694 1.648
Cyclosporin 4 vs never taking 3 0.349 0.083 1.461
Cyclosporin currently taking vs never taking 1 1.697 0.876 3.287
Cyclosporin currently taking vs never taking 2 2.840 1.833 4.403
Cyclosporin currently taking vs never taking 3 2.778 1.173 6.577
Prednisolone 3 vs never taking 1 1.392 1.126 1.722
Prednisolone 3 vs never taking 2 1.291 1.095 1.521
484
Odds Ratio Estimates
Effect
TB
Infection
Point
Estimate
95% Wald
Confidence Limits
Prednisolone 3 vs never taking 3 1.645 1.149 2.357
Prednisolone 4 vs never taking 1 2.190 0.729 6.576
Prednisolone 4 vs never taking 2 1.727 0.740 4.034
Prednisolone 4 vs never taking 3 2.047 0.267 15.680
Prednisolone currently taking vs never taking 1 1.182 0.955 1.462
Prednisolone currently taking vs never taking 2 1.140 0.967 1.343
Prednisolone currently taking vs never taking 3 1.479 1.032 2.118
485
APPENDIX J: OUTPUT OF SAS FOR
URINARY TRACT INFECTION
Table J.1- Complete statistics for UTI
Model Information
Data Set WORK.IMPORT2
Response Variable InfKidUri InfKidUri
Number of Response Levels 4
Model generalized logit
Optimization Technique Newton-Raphson
Table J.2- Observation status for UTI
Number of Observations Read 27711
Number of Observations Used 21506
Table J.3- response value for UTI
Response Profile
Ordered
Value InfKidUri
Total
Frequency
1 1 290
2 2 833
3 3 256
4 4 20127
Logits modelled use InfKidUri='4' as the reference category.
486
Note: 6205 observations were deleted due to missing values for the response or explanatory
variables.
Table J.4- Backward Elimination Procedure for UTI
Backward Elimination Procedure
Class Level Information
Class Value Design Variables
Etanercept 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Adalimumab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Anakinra 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Infliximab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Rituximab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Abatacept 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
487
Tocilizumab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Golimumab 3 1 0 0
currently taking 0 1 0
never taking 0 0 1
Certolizumab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Folic Acid currently taking 1 0
never taking 0 1
Hydroxychloroquine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Sulphasalazine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Arava (Leflunomide) 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Azathioprine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Cyclosporin 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
488
Prednisolone 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
IM Gold injection 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Penicillamine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Step 0. The following effects were entered:
Intercept Etanercept Adalimumab Anakinra Infliximab Rituximab Abatacept Tocilizumab
Golimumab Certolizumab Folic Acid Hydroxychloroquine Sulphasalazine Arava
(Leflunomide) Azathioprine Cyclosporin Prednisolone IM Gold injection Penicillamine
Table J.5- Model Convergence status for UTI
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
489
Table J.6- Model Fit statistics for UTI
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 12856.104 12720.097
SC 12880.032 13964.367
-2 Log L 12850.104 12408.097
Table J.7- Testing null hypothesis for UTI
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 442.0070 153 <.0001
Score 494.1448 153 <.0001
Wald 442.5757 153 <.0001
490
Table J.8- Model Fit statistics status for removing covariant step 1
Step 1. Effect Abatacept is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table J.9- Model Fit statistics for removing covariant step 1
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 12856.104 12708.936
SC 12880.032 13881.421
-2 Log L 12850.104 12414.936
Table J.10- Testing Null hypothesis after removing covariant step 1
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 435.1680 144 <.0001
Score 486.6474 144 <.0001
Wald 438.5445 144 <.0001
491
Table J.11- Residual removing covariant step 1
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
5.2162 9 0.8151
Table J.12- Model Fit statistics for removing covariant step 2
Step 2. Effect Anakinra is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table J.13- Model Fit statistics after removing covariant step 2
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 12856.104 12698.658
SC 12880.032 13799.358
-2 Log L 12850.104 12422.658
492
Table J.14- Testing Null hypothesis after removing covariant step 2
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 427.4457 135 <.0001
Score 479.8413 135 <.0001
Wald 433.5090 135 <.0001
Table J.15- Residual removing covariant step 2
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
10.7595 18 0.9043
Table J.16- Model Fit statistics for removing covariant step 3
Step 3. Effect Certolizumab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table J.17- Model Fit statistics after removing covariant step 3
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 12856.104 12695.973
SC 12880.032 13724.888
-2 Log L 12850.104 12437.973
Table J.18- Testing Null hypothesis after removing covariant step 3
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 412.1314 126 <.0001
Score 467.3671 126 <.0001
Wald 425.1267 126 <.0001
493
Table J.19- Residual removing covariant step 3
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
21.4657 27 0.7640
Table J.20- Model Fit statistics for removing covariant step 4
Step 4. Effect Golimumab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
494
Table J.21- Model Fit statistics after removing covariant step 4
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 12856.104 12687.586
SC 12880.032 13668.645
-2 Log L 12850.104 12441.586
Table J.22- Testing Null hypothesis after removing covariant step 4
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 408.5177 120 <.0001
Score 463.4235 120 <.0001
Wald 421.3840 120 <.0001
Table J.23- Residual removing covariant step 4
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
25.7755 33 0.8106
495
Table J.24- Model Fit statistics for removing covariant step 5
Step 5. Effect Tocilizumab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table J.25- Model Fit statistics after removing covariant step 5
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 12856.104 12680.185
SC 12880.032 13589.459
-2 Log L 12850.104 12452.185
Table J.26- Testing Null hypothesis after removing covariant step 5
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 397.9187 111 <.0001
Score 449.3858 111 <.0001
Wald 408.9965 111 <.0001
496
Table J.27- Residual removing covariant step 5
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
37.0911 42 0.6860
Table J.28- Model Fit statistics for removing covariant step 6
Step 6. Effect Sulphasalazine is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table J.29- Model Fit statistics after removing covariant step 6
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 12856.104 12673.942
SC 12880.032 13511.431
-2 Log L 12850.104 12463.942
497
Table J.30- Testing Null hypothesis after removing covariant step 6
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 386.1623 102 <.0001
Score 438.4015 102 <.0001
Wald 398.7265 102 <.0001
Table J.31- Residual removing covariant step 6
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
47.9903 51 0.5939
Table J.32- Model Fit statistics for removing covariant step 7
Step 7. Effect Adalimumab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
498
Table J.33- Model Fit statistics after removing covariant step 7
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 12856.104 12669.937
SC 12880.032 13435.642
-2 Log L 12850.104 12477.937
Table J.34- Testing Null hypothesis after removing covariant step 7
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 372.1669 93 <.0001
Score 422.5308 93 <.0001
Wald 384.7193 93 <.0001
Table J.35- Residual removing covariant step 7
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
62.5971 60 0.3842
499
Table J.36- Model Fit statistics for removing covariant step 8
Step 8. Effect Rituximab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table J.37- Model Fit statistics after removing covariant step 8
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 12856.104 12667.611
SC 12880.032 13361.531
-2 Log L 12850.104 12493.611
Table J.38- Testing Null hypothesis after removing covariant step 8
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 356.4931 84 <.0001
Score 401.5030 84 <.0001
Wald 367.1355 84 <.0001
500
Table J.39- Residual removing covariant step 8
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
80.3612 69 0.1649
Table J.40- Model Fit statistics for removing covariant step 9
Step 9. Effect Folic Acid is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table J.41- Model Fit statistics after removing covariant step 9
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 12856.104 12669.548
SC 12880.032 13339.540
-2 Log L 12850.104 12501.548
501
Table J.42- Testing Null hypothesis after removing covariant step 9
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 348.5557 81 <.0001
Score 394.3441 81 <.0001
Wald 359.6161 81 <.0001
Table J.43- Residual removing covariant step 9
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
87.7330 72 0.1001
Note: No (additional) effects met the 0.05 significance level for removal from the model.
Table J.44- Summary of backward elimination in UTI
Summary of Backward Elimination
Step
Effect
Removed DF
Number
In
Wald
Chi-Square Pr > ChiSq
Variable
Label
1 Abatacept 9 17 2.9651 0.9657 Abatacept
2 Anakinra 9 16 3.6765 0.9314
3 Certolizumab 9 15 6.0565 0.7343 Certolizumab
4 Golimumab 6 14 4.1063 0.6623 Golimumab
5 Tocilizumab 9 13 9.3430 0.4062 Tocilizumab
6 Sulphasalazine 9 12 10.7076 0.2963 Sulphasalazine
7 Adalimumab 9 11 13.7461 0.1316
8 Rituximab 9 10 15.7336 0.0727 Rituximab
9 Folic Acid 3 9 7.4688 0.0584 Folic Acid
502
Table J.45- Type 3 analysis of effects in UTI
Type 3 Analysis of Effects
Effect DF
Wald
Chi-Square Pr > ChiSq
Etanercept 9 27.3183 0.0012
Infliximab 9 24.4209 0.0037
Hydroxychloroquine 9 20.7884 0.0136
Arava (Leflunomide) 9 22.1605 0.0084
Azathioprine 9 34.4145 <.0001
Cyclosporin 9 61.9727 <.0001
Prednisolone 9 56.1144 <.0001
IM Gold injection 9 26.8635 0.0015
Penicillamine 9 46.2679 <.0001
Table J.46- Analysis of maximum likelihood estimates in UTI
Analysis of Maximum Likelihood Estimates
Parameter InfKidUri DF Estimate
Standard
Error
Wald
Chi-Square Pr > ChiSq
Intercept Mild 1 -4.3801 0.1853 558.8895 <.0001
Intercept Mod 1 -3.6145 0.1223 872.9303 <.0001
Intercept Severe 1 -4.8991 0.2400 416.8122 <.0001
Arava (Leflunomide) 3 Mild 1 -0.2240 0.1564 2.0524 0.1520
Arava (Leflunomide) 3 Mod 1 0.1646 0.0988 2.7759 0.0957
Arava (Leflunomide) 3 Severe 1 0.0506 0.1776 0.0812 0.7757
Arava (Leflunomide) 4 Mild 1 -0.0668 0.7969 0.0070 0.9332
Arava (Leflunomide) 4 Mod 1 0.0573 0.4921 0.0135 0.9074
Arava (Leflunomide) 4 Severe 1 0.8420 0.6011 1.9621 0.1613
Arava (Leflunomide) currently taking Mild 1 -0.2083 0.1928 1.1666 0.2801
Arava (Leflunomide) currently taking Mod 1 -0.1359 0.1248 1.1862 0.2761
Arava (Leflunomide) currently taking Severe 1 -0.5347 0.2388 5.0129 0.0252
Arava (Leflunomide) never taking Mild 0 0 . . .
Arava (Leflunomide) never taking Mod 0 0 . . .
503
Analysis of Maximum Likelihood Estimates
Parameter InfKidUri DF Estimate
Standard
Error
Wald
Chi-Square Pr > ChiSq
Arava (Leflunomide) never taking Severe 0 0 . . .
Azathioprine 3 Mild 1 -0.5205 0.2460 4.4756 0.0344
Azathioprine 3 Mod 1 -0.7160 0.1596 20.1196 <.0001
Azathioprine 3 Severe 1 0.4816 0.2146 5.0389 0.0248
Azathioprine 4 Mild 1 -0.6989 0.6277 1.2400 0.2655
Azathioprine 4 Mod 1 0.1760 0.3073 0.3283 0.5667
Azathioprine 4 Severe 1 0.6065 0.4734 1.6415 0.2001
Azathioprine currently taking Mild 1 -11.8768 269.9 0.0019 0.9649
Azathioprine currently taking Mod 1 -0.8026 0.5911 1.8433 0.1746
Azathioprine currently taking Severe 1 -11.9304 256.6 0.0022 0.9629
Azathioprine never taking Mild 0 0 . . .
Azathioprine never taking Mod 0 0 . . .
Azathioprine never taking Severe 0 0 . . .
Cyclosporin 3 Mild 1 0.0286 0.1771 0.0261 0.8717
Cyclosporin 3 Mod 1 0.1203 0.1042 1.3324 0.2484
Cyclosporin 3 Severe 1 -1.1810 0.2454 23.1502 <.0001
Cyclosporin 4 Mild 1 1.1876 0.4920 5.8263 0.0158
Cyclosporin 4 Mod 1 -0.5268 0.3731 1.9935 0.1580
Cyclosporin 4 Severe 1 -0.7341 0.5515 1.7717 0.1832
Cyclosporin currently taking Mild 1 1.5236 0.3766 16.3680 <.0001
Cyclosporin currently taking Mod 1 1.0570 0.2906 13.2255 0.0003
Cyclosporin currently taking Severe 1 0.6898 0.5322 1.6798 0.1950
Cyclosporin never taking Mild 0 0 . . .
Cyclosporin never taking Mod 0 0 . . .
Cyclosporin never taking Severe 0 0 . . .
Etanercept 3 Mild 1 0.1829 0.1636 1.2501 0.2635
Etanercept 3 Mod 1 -0.2009 0.0961 4.3684 0.0366
Etanercept 3 Severe 1 0.3480 0.1590 4.7881 0.0287
Etanercept 4 Mild 1 -12.6298 554.8 0.0005 0.9818
Etanercept 4 Mod 1 -0.3715 0.8011 0.2151 0.6428
Etanercept 4 Severe 1 2.0503 0.6520 9.8884 0.0017
Etanercept currently taking Mild 1 0.3258 0.1452 5.0337 0.0249
504
Analysis of Maximum Likelihood Estimates
Parameter InfKidUri DF Estimate
Standard
Error
Wald
Chi-Square Pr > ChiSq
Etanercept currently taking Mod 1 -0.1108 0.0873 1.6099 0.2045
Etanercept currently taking Severe 1 -0.0992 0.1693 0.3431 0.5581
Etanercept never taking Mild 0 0 . . .
Etanercept never taking Mod 0 0 . . .
Etanercept never taking Severe 0 0 . . .
Hydroxychloroquine 3 Mild 1 0.00780 0.1381 0.0032 0.9550
Hydroxychloroquine 3 Mod 1 0.3203 0.0836 14.6913 0.0001
Hydroxychloroquine 3 Severe 1 0.000272 0.1453 0.0000 0.9985
Hydroxychloroquine 4 Mild 1 -0.7822 1.0278 0.5792 0.4466
Hydroxychloroquine 4 Mod 1 -0.4848 0.4789 1.0250 0.3113
Hydroxychloroquine 4 Severe 1 -1.2246 1.0441 1.3755 0.2409
Hydroxychloroquine currently taking Mild 1 0.0696 0.1733 0.1611 0.6882
Hydroxychloroquine currently taking Mod 1 0.0797 0.1096 0.5288 0.4671
Hydroxychloroquine currently taking Severe 1 -0.1762 0.1963 0.8059 0.3693
Hydroxychloroquine never taking Mild 0 0 . . .
Hydroxychloroquine never taking Mod 0 0 . . .
Hydroxychloroquine never taking Severe 0 0 . . .
IM Gold injection 3 Mild 1 0.4411 0.1444 9.3249 0.0023
IM Gold injection 3 Mod 1 0.1758 0.0890 3.9017 0.0482
IM Gold injection 3 Severe 1 0.1856 0.1582 1.3767 0.2407
IM Gold injection 4 Mild 1 0.2851 0.8555 0.1111 0.7389
IM Gold injection 4 Mod 1 0.7416 0.3618 4.2028 0.0404
IM Gold injection 4 Severe 1 -1.1430 0.9031 1.6021 0.2056
IM Gold injection currently taking Mild 1 -0.0266 0.7187 0.0014 0.9705
IM Gold injection currently taking Mod 1 0.4818 0.3327 2.0963 0.1477
IM Gold injection currently taking Severe 1 1.1145 0.4351 6.5630 0.0104
IM Gold injection never taking Mild 0 0 . . .
IM Gold injection never taking Mod 0 0 . . .
IM Gold injection never taking Severe 0 0 . . .
Infliximab 3 Mild 1 -0.4458 0.2930 2.3149 0.1281
Infliximab 3 Mod 1 -0.3481 0.1641 4.5005 0.0339
Infliximab 3 Severe 1 0.6390 0.2074 9.4950 0.0021
505
Analysis of Maximum Likelihood Estimates
Parameter InfKidUri DF Estimate
Standard
Error
Wald
Chi-Square Pr > ChiSq
Infliximab 4 Mild 1 -0.9060 1.0372 0.7631 0.3824
Infliximab 4 Mod 1 -0.5810 0.5126 1.2847 0.2570
Infliximab 4 Severe 1 -0.1676 0.6351 0.0696 0.7919
Infliximab currently taking Mild 1 0.3314 0.3353 0.9770 0.3229
Infliximab currently taking Mod 1 0.0242 0.2202 0.0121 0.9123
Infliximab currently taking Severe 1 -1.4910 0.7397 4.0632 0.0438
Infliximab never taking Mild 0 0 . . .
Infliximab never taking Mod 0 0 . . .
Infliximab never taking Severe 0 0 . . .
Penicillamine 3 Mild 1 0.6192 0.1785 12.0339 0.0005
Penicillamine 3 Mod 1 0.4545 0.1140 15.8882 <.0001
Penicillamine 3 Severe 1 0.0475 0.2175 0.0477 0.8271
Penicillamine 4 Mild 1 -0.4054 0.6593 0.3781 0.5386
Penicillamine 4 Mod 1 1.0189 0.2755 13.6737 0.0002
Penicillamine 4 Severe 1 1.3537 0.4485 9.1120 0.0025
Penicillamine currently taking Mild 1 -12.0460 451.7 0.0007 0.9787
Penicillamine currently taking Mod 1 -12.1211 270.8 0.0020 0.9643
Penicillamine currently taking Severe 1 -12.0786 478.6 0.0006 0.9799
Penicillamine never taking Mild 0 0 . . .
Penicillamine never taking Mod 0 0 . . .
Penicillamine never taking Severe 0 0 . . .
Prednisolone 3 Mild 1 -0.0749 0.1901 0.1553 0.6935
Prednisolone 3 Mod 1 -0.00739 0.1231 0.0036 0.9521
Prednisolone 3 Severe 1 0.1364 0.2528 0.2909 0.5897
Prednisolone 4 Mild 1 1.6536 0.6799 5.9144 0.0150
Prednisolone 4 Mod 1 0.6808 0.5292 1.6549 0.1983
Prednisolone 4 Severe 1 1.2628 0.8600 2.1559 0.1420
Prednisolone currently taking Mild 1 -0.0322 0.1887 0.0291 0.8645
Prednisolone currently taking Mod 1 0.3588 0.1186 9.1561 0.0025
Prednisolone currently taking Severe 1 0.7760 0.2393 10.5125 0.0012
Prednisolone never taking Mild 0 0 . . .
Prednisolone never taking Mod 0 0 . . .
506
Analysis of Maximum Likelihood Estimates
Parameter InfKidUri DF Estimate
Standard
Error
Wald
Chi-Square Pr > ChiSq
Prednisolone never taking Severe 0 0 . . .
Table J.47- Odds ratio estimates in UTI
Odds Ratio Estimates
Effect InfKidUri Point Estimate
95% Wald
Confidence Limits
Etanercept 3 vs never taking Mild 1.201 0.871 1.655
Etanercept 3 vs never taking Mod 0.818 0.678 0.988
Etanercept 3 vs never taking Severe 1.416 1.037 1.934
Etanercept 4 vs never taking Mild <0.001 <0.001 >999.999
Etanercept 4 vs never taking Mod 0.690 0.143 3.316
Etanercept 4 vs never taking Severe 7.770 2.165 27.887
Etanercept currently taking vs never taking Mild 1.385 1.042 1.841
Etanercept currently taking vs never taking Mod 0.895 0.754 1.062
Etanercept currently taking vs never taking Severe 0.906 0.650 1.262
Infliximab 3 vs never taking Mild 0.640 0.361 1.137
Infliximab 3 vs never taking Mod 0.706 0.512 0.974
Infliximab 3 vs never taking Severe 1.895 1.262 2.845
Infliximab 4 vs never taking Mild 0.404 0.053 3.086
Infliximab 4 vs never taking Mod 0.559 0.205 1.528
Infliximab 4 vs never taking Severe 0.846 0.244 2.937
Infliximab currently taking vs never taking Mild 1.393 0.722 2.687
Infliximab currently taking vs never taking Mod 1.025 0.665 1.578
Infliximab currently taking vs never taking Severe 0.225 0.053 0.960
Hydroxychloroquine 3 vs never taking Mild 1.008 0.769 1.321
Hydroxychloroquine 3 vs never taking Mod 1.377 1.169 1.623
Hydroxychloroquine 3 vs never taking Severe 1.000 0.752 1.330
Hydroxychloroquine 4 vs never taking Mild 0.457 0.061 3.429
Hydroxychloroquine 4 vs never taking Mod 0.616 0.241 1.574
Hydroxychloroquine 4 vs never taking Severe 0.294 0.038 2.275
507
Odds Ratio Estimates
Effect InfKidUri Point Estimate
95% Wald
Confidence Limits
Hydroxychloroquine currently taking vs never taking Mild 1.072 0.763 1.506
Hydroxychloroquine currently taking vs never taking Mod 1.083 0.874 1.343
Hydroxychloroquine currently taking vs never taking Severe 0.838 0.571 1.232
Arava (Leflunomide) 3 vs never taking Mild 0.799 0.588 1.086
Arava (Leflunomide) 3 vs never taking Mod 1.179 0.971 1.431
Arava (Leflunomide) 3 vs never taking Severe 1.052 0.743 1.490
Arava (Leflunomide) 4 vs never taking Mild 0.935 0.196 4.460
Arava (Leflunomide) 4 vs never taking Mod 1.059 0.404 2.778
Arava (Leflunomide) 4 vs never taking Severe 2.321 0.715 7.540
Arava (Leflunomide) currently taking vs never taking Mild 0.812 0.556 1.185
Arava (Leflunomide) currently taking vs never taking Mod 0.873 0.683 1.115
Arava (Leflunomide) currently taking vs never taking Severe 0.586 0.367 0.936
Azathioprine 3 vs never taking Mild 0.594 0.367 0.962
Azathioprine 3 vs never taking Mod 0.489 0.357 0.668
Azathioprine 3 vs never taking Severe 1.619 1.063 2.465
Azathioprine 4 vs never taking Mild 0.497 0.145 1.701
Azathioprine 4 vs never taking Mod 1.192 0.653 2.178
Azathioprine 4 vs never taking Severe 1.834 0.725 4.638
Azathioprine currently taking vs never taking Mild <0.001 <0.001 >999.999
Azathioprine currently taking vs never taking Mod 0.448 0.141 1.428
Azathioprine currently taking vs never taking Severe <0.001 <0.001 >999.999
Cyclosporin 3 vs never taking Mild 1.029 0.727 1.456
Cyclosporin 3 vs never taking Mod 1.128 0.919 1.383
Cyclosporin 3 vs never taking Severe 0.307 0.190 0.497
Cyclosporin 4 vs never taking Mild 3.279 1.250 8.602
Cyclosporin 4 vs never taking Mod 0.590 0.284 1.227
Cyclosporin 4 vs never taking Severe 0.480 0.163 1.415
Cyclosporin currently taking vs never taking Mild 4.589 2.193 9.599
Cyclosporin currently taking vs never taking Mod 2.878 1.628 5.087
Cyclosporin currently taking vs never taking Severe 1.993 0.702 5.658
Prednisolone 3 vs never taking Mild 0.928 0.639 1.347
Prednisolone 3 vs never taking Mod 0.993 0.780 1.263
508
Odds Ratio Estimates
Effect InfKidUri Point Estimate
95% Wald
Confidence Limits
Prednisolone 3 vs never taking Severe 1.146 0.698 1.881
Prednisolone 4 vs never taking Mild 5.226 1.378 19.811
Prednisolone 4 vs never taking Mod 1.975 0.700 5.573
Prednisolone 4 vs never taking Severe 3.535 0.655 19.076
Prednisolone currently taking vs never taking Mild 0.968 0.669 1.402
Prednisolone currently taking vs never taking Mod 1.432 1.135 1.806
Prednisolone currently taking vs never taking Severe 2.173 1.359 3.473
IM Gold injection 3 vs never taking Mild 1.554 1.171 2.063
IM Gold injection 3 vs never taking Mod 1.192 1.001 1.419
IM Gold injection 3 vs never taking Severe 1.204 0.883 1.642
IM Gold injection 4 vs never taking Mild 1.330 0.249 7.113
IM Gold injection 4 vs never taking Mod 2.099 1.033 4.266
IM Gold injection 4 vs never taking Severe 0.319 0.054 1.872
IM Gold injection currently taking vs never taking Mild 0.974 0.238 3.983
IM Gold injection currently taking vs never taking Mod 1.619 0.843 3.108
IM Gold injection currently taking vs never taking Severe 3.048 1.299 7.151
Penicillamine 3 vs never taking Mild 1.857 1.309 2.636
Penicillamine 3 vs never taking Mod 1.575 1.260 1.970
Penicillamine 3 vs never taking Severe 1.049 0.685 1.606
Penicillamine 4 vs never taking Mild 0.667 0.183 2.427
Penicillamine 4 vs never taking Mod 2.770 1.614 4.754
Penicillamine 4 vs never taking Severe 3.872 1.608 9.325
Penicillamine currently taking vs never taking Mild <0.001 <0.001 >999.999
Penicillamine currently taking vs never taking Mod <0.001 <0.001 >999.999
Penicillamine currently taking vs never taking Severe <0.001 <0.001 >999.999
509
APPENDIX K: OUTPUT OF SAS FOR
VIRAL INFECTION
Table K.1- Complete statistics for viral infection
Model Information
Data Set WORK.IMPORT2
Response Variable InfVir InfVir
Number of Response Levels 4
Model generalized logit
Optimization Technique Newton-Raphson
Table K.2- Observation status for VIRAL INFECTION
Number of Observations Read 27711
Number of Observations Used 21506
Table K.3- response value for VIRAL INFECTION
Response Profile
Ordered
Value InfVir
Total
Frequency
1 1 435
2 2 837
3 3 305
4 4 19929
0 .
510
Logits modelled use InfVir='4' as the reference category.
Note: 6205 observations were deleted due to missing values for the response or explanatory
variables.
Note1 response level was deleted due to missing or invalid values for its explanatory,
frequency, or weight variables
Table K.4- Backward Elimination Procedure for VIRAL INFECTION
Backward Elimination Procedure
Class Level Information
Class Value Design Variables
Etanercept 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Adalimumab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Anakinra 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Infliximab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Rituximab 3 1 0 0 0
4 0 1 0 0
511
currently taking 0 0 1 0
never taking 0 0 0 1
Abatacept 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Tocilizumab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Golimumab 3 1 0 0
currently taking 0 1 0
never taking 0 0 1
Certolizumab 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Folic Acid currently taking 1 0
never taking 0 1
Hydroxychloroquine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Sulphasalazine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Arava (Leflunomide) 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Azathioprine 3 1 0 0 0
4 0 1 0 0
512
currently taking 0 0 1 0
never taking 0 0 0 1
Cyclosporin 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Prednisolone 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
IM Gold injection 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Penicillamine 3 1 0 0 0
4 0 1 0 0
currently taking 0 0 1 0
never taking 0 0 0 1
Step 0. The following effects were entered:
Intercept Etanercept Adalimumab Anakinra Infliximab Rituximab Abatacept Tocilizumab
Golimumab Certolizumab Folic Acid Hydroxychloroquine Sulphasalazine Arava
(Leflunomide) Azathioprine Cyclosporin Prednisolone IM Gold injection Penicillamine
513
Table K.5- Model Convergence status for VIRAL INFECTION
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table K.6- Model Fit statistics for VIRAL INFECTION
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 14465.339 14470.560
SC 14489.267 15714.830
-2 Log L 14459.339 14158.560
Table K.7- Testing null hypothesis for VIRAL INFECTION
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 300.7784 153 <.0001
Score 331.3978 153 <.0001
Wald 311.3192 153 <.0001
514
Table K.8- Model Fit statistics for removing covariant step 1
Step 1. Effect Penicillamine is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table K.9- Model Fit statistics for removing covariant step 1
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 14465.339 14462.278
SC 14489.267 15634.763
-2 Log L 14459.339 14168.278
Table K.10- Testing Null hypothesis after removing covariant step 1
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 291.0609 144 <.0001
Score 325.8514 144 <.0001
Wald 306.2753 144 <.0001
515
Table K.11- Residual removing covariant step 1
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
7.0603 9 0.6308
Table K.12- Model Fit statistics for removing covariant step 2
Step 2. Effect Certolizumab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table K.13- Model Fit statistics after removing covariant step 2
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 14465.339 14452.290
SC 14489.267 15552.990
-2 Log L 14459.339 14176.290
516
Table K.14- Testing Null hypothesis after removing covariant step 2
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 283.0486 135 <.0001
Score 316.2107 135 <.0001
Wald 297.3478 135 <.0001
Table K.15- Residual removing covariant step 2
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
15.7431 18 0.6105
Table K.16- Model Fit statistics for removing covariant step 3
Step 3. Effect Golimumab is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
517
Table K.17- Model Fit statistics after removing covariant step 3
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 14465.339 14445.753
SC 14489.267 15498.596
-2 Log L 14459.339 14181.753
Table K.18- Testing Null hypothesis after removing covariant step 3
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 277.5858 129 <.0001
Score 311.0451 129 <.0001
Wald 292.3479 129 <.0001
Table K.19- Residual removing covariant step 3
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
21.2000 24 0.6269
518
Table K.20- Model Fit statistics for removing covariant step 4
Step 4. Effect Arava (Leflunomide) is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table K.21- Model Fit statistics after removing covariant step 4
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 14465.339 14443.433
SC 14489.267 15424.492
-2 Log L 14459.339 14197.433
Table K.22- Testing Null hypothesis after removing covariant step 4
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 261.9054 120 <.0001
Score 299.6898 120 <.0001
Wald 274.5846 120 <.0001
519
Table K.23- Residual removing covariant step 4
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
33.6212 33 0.4372
Table K.24- Model Fit statistics for removing covariant step 5
Step 5. Effect Abatacept is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table K.25- Model Fit statistics after removing covariant step 5
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 14465.339 14435.111
SC 14489.267 15344.385
-2 Log L 14459.339 14207.111
Table K.26- Testing Null hypothesis after removing covariant step 5
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 252.2274 111 <.0001
Score 291.0613 111 <.0001
Wald 266.0126 111 <.0001
Table K.27- Residual removing covariant step 5
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
44.0788 42 0.3837
520
Table K.28- Model Fit statistics for removing covariant step 6
Step 6. Effect IM Gold injection is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table K.29- Model Fit statistics after removing covariant step 6
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 14465.339 14427.297
SC 14489.267 15264.786
-2 Log L 14459.339 14217.297
521
Table K.30- Testing Null hypothesis after removing covariant step 6
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 242.0415 102 <.0001
Score 278.2277 102 <.0001
Wald 255.0262 102 <.0001
Table K.31- Residual removing covariant step 6
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
55.5679 51 0.3068
Step 7. Effect Azathioprine is removed:
Table K.32- Model Fit statistics for removing covariant step 7
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
522
Table K.33- Model Fit statistics after removing covariant step 7
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 14465.339 14422.480
SC 14489.267 15188.185
-2 Log L 14459.339 14230.480
Table K.34- Testing Null hypothesis after removing covariant step 7
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 228.8582 93 <.0001
Score 264.9909 93 <.0001
Wald 242.7767 93 <.0001
Table K.35- Residual removing covariant step 7
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
67.4931 60 0.2365
Step 8. Effect Sulphasalazine is removed:
Table K.36- Model Fit statistics for removing covariant step 8
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table K.37- Model Fit statistics after removing covariant step 8
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 14465.339 14413.609
523
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
SC 14489.267 15107.528
-2 Log L 14459.339 14239.609
Table K.38- Testing Null hypothesis after removing covariant step 8
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 219.7299 84 <.0001
Score 255.3106 84 <.0001
Wald 232.3624 84 <.0001
Table K.39- Residual removing covariant step 8
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
78.7472 69 0.1977
524
Step 9. Effect Anakinra is removed:
Table K.40- Model Fit statistics for removing covariant step 9
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table K.41- Model Fit statistics after removing covariant step 9
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 14465.339 14406.926
SC 14489.267 15029.060
-2 Log L 14459.339 14250.926
Table K.42- Testing Null hypothesis after removing covariant step 9
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 208.4131 75 <.0001
Score 242.5271 75 <.0001
Wald 221.0994 75 <.0001
525
Table K.43- Residual removing covariant step 9
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
88.8401 78 0.1885
Step 10. Effect Tocilizumab is removed:
Table K.44- Model Fit statistics for removing covariant step 10
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table K.45- Model Fit statistics after removing covariant step 10
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 14465.339 14402.102
SC 14489.267 14952.452
-2 Log L 14459.339 14264.102
526
Table K.46- Testing Null hypothesis after removing covariant step 10
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 195.2369 66 <.0001
Score 227.9013 66 <.0001
Wald 207.5631 66 <.0001
Table K.47- Residual removing covariant step 10
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
101.7314 87 0.1337
Step 11. Effect Adalimumab is removed:
Table K.48- Model Fit statistics for removing covariant step 11
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table K.49- Model Fit statistics after removing covariant step 11
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 14465.339 14399.250
SC 14489.267 14877.815
-2 Log L 14459.339 14279.250
Table K.50- Testing Null hypothesis after removing covariant step 11
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 180.0886 57 <.0001
527
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Score 211.6551 57 <.0001
Wald 191.8318 57 <.0001
Table K.51- Residual removing covariant step 11
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
116.7690 96 0.0735
Step 12. Effect Rituximab is removed:
Table K.52- Model Fit statistics for removing covariant step 12
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table K.53- Model Fit statistics for removing covariant step 12
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 14465.339 14402.145
SC 14489.267 14808.925
-2 Log L 14459.339 14300.145
Table K.54- Testing Null hypothesis after removing covariant step 12
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 159.1937 48 <.0001
Score 188.7845 48 <.0001
Wald 170.0883 48 <.0001
528
Table K.55- Residual removing covariant step 12
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
134.6372 105 0.0272
Step 13. Effect Infliximab is removed:
Table K.56- Model Fit statistics for removing covariant step 13
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
529
Table K.57- Model Fit statistics after removing covariant step 13
Model Fit Statistics
Criterion Intercept Only Intercept and Covariates
AIC 14465.339 14396.196
SC 14489.267 14731.191
-2 Log L 14459.339 14312.196
Table K.58- Testing Null hypothesis after removing covariant step 13
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 147.1431 39 <.0001
Score 173.8189 39 <.0001
Wald 156.0719 39 <.0001
Table K.59- Residual removing covariant step 13
Residual Chi-Square Test
Chi-Square DF Pr > ChiSq
150.7370 114 0.0121
Note: No (additional) effects met the 0.05 significance level for removal from the model.
530
Table K.60 - Summary of backward elimination in VIRAL INFECTION
Summary of Backward Elimination
Step
Effect
Removed DF
Number
In
Wald
Chi-Square Pr > ChiSq
Variable
Label
1 Penicillamine 9 17 4.9471 0.8389 Penicillamine
2 Certolizumab 9 16 8.1929 0.5148 Certolizumab
3 Golimumab 6 15 5.4697 0.4851 Golimumab
4 Arava (Leflunomide) 9 14 7.7065 0.5640 Arava (Leflunomide)
5 Abatacept 9 13 10.0331 0.3478 Abatacept
6 IM Gold injection 9 12 10.9493 0.2792 IM Gold injection
7 Azathioprine 9 11 11.2061 0.2618 Azathioprine
8 Sulphasalazine 9 10 9.9071 0.3581 Sulphasalazine
9 Anakinra 9 9 10.1434 0.3390
10 Tocilizumab 9 8 11.9686 0.2151 Tocilizumab
11 Adalimumab 9 7 15.0634 0.0892
12 Rituximab 9 6 16.7000 0.0536 Rituximab
13 Infliximab 9 5 13.1407 0.1563
Table K.61- Type 3 analysis of effects in VIRAL INFECTION
Type 3 Analysis of Effects
Effect DF
Wald
Chi-Square Pr > ChiSq
Etanercept 9 43.9767 <.0001
Folic Acid 3 13.7765 0.0032
Hydroxychloroquine 9 29.2958 0.0006
Cyclosporin 9 29.3135 0.0006
Prednisolone 9 25.6360 0.0023
531
Table K.62- Analysis of maximum likelihood estimates in VIRAL INFECTION
Analysis of Maximum Likelihood Estimates
Parameter InfVir DF Estimate
Standard
Error
Wald
Chi-Square Pr > ChiSq
Intercept 1 1 -4.0046 0.1449 763.8821 <.0001
Intercept 2 1 -3.7221 0.1215 938.7083 <.0001
Intercept 3 1 -4.9163 0.2170 513.2153 <.0001
Etanercept 3 1 1 0.0300 0.1317 0.0520 0.8196
Etanercept 3 2 1 -0.0552 0.0924 0.3574 0.5499
Etanercept 3 3 1 -0.0276 0.1511 0.0334 0.8550
Etanercept 4 1 1 0.8042 0.7503 1.1486 0.2838
Etanercept 4 2 1 1.4693 0.3785 15.0703 0.0001
Etanercept 4 3 1 2.4269 0.4611 27.6982 <.0001
Etanercept currently taking 1 1 0.2158 0.1135 3.6129 0.0573
Etanercept currently taking 2 1 -0.0696 0.0858 0.6574 0.4175
Etanercept currently taking 3 1 0.0166 0.1397 0.0142 0.9053
Etanercept never taking 1 0 0 . . .
Etanercept never taking 2 0 0 . . .
Etanercept never taking 3 0 0 . . .
Folic Acid currently taking 1 1 -0.2782 0.1215 5.2471 0.0220
Folic Acid currently taking 2 1 -0.2498 0.0874 8.1597 0.0043
Folic Acid currently taking 3 1 -0.1302 0.1385 0.8845 0.3470
Folic Acid never taking 1 0 0 . . .
Folic Acid never taking 2 0 0 . . .
Folic Acid never taking 3 0 0 . . .
Hydroxychloroquine 3 1 1 0.2452 0.1114 4.8438 0.0277
Hydroxychloroquine 3 2 1 0.3634 0.0826 19.3661 <.0001
Hydroxychloroquine 3 3 1 0.2016 0.1322 2.3252 0.1273
Hydroxychloroquine 4 1 1 -0.2717 0.7210 0.1420 0.7063
Hydroxychloroquine 4 2 1 0.7413 0.3267 5.1494 0.0233
Hydroxychloroquine 4 3 1 0.4258 0.5950 0.5121 0.4742
Hydroxychloroquine currently taking 1 1 0.2225 0.1394 2.5473 0.1105
Hydroxychloroquine currently taking 2 1 0.2648 0.1050 6.3532 0.0117
Hydroxychloroquine currently taking 3 1 0.1380 0.1700 0.6594 0.4168
Hydroxychloroquine never taking 1 0 0 . . .
532
Analysis of Maximum Likelihood Estimates
Parameter InfVir DF Estimate
Standard
Error
Wald
Chi-Square Pr > ChiSq
Hydroxychloroquine never taking 2 0 0 . . .
Hydroxychloroquine never taking 3 0 0 . . .
Cyclosporin 3 1 1 -0.1466 0.1488 0.9698 0.3247
Cyclosporin 3 2 1 0.3263 0.0938 12.0966 0.0005
Cyclosporin 3 3 1 0.2517 0.1518 2.7469 0.0974
Cyclosporin 4 1 1 -0.4712 0.5218 0.8156 0.3665
Cyclosporin 4 2 1 0.3039 0.2642 1.3229 0.2501
Cyclosporin 4 3 1 -1.0400 0.7326 2.0151 0.1557
Cyclosporin currently taking 1 1 0.000418 0.5873 0.0000 0.9994
Cyclosporin currently taking 2 1 0.9855 0.2950 11.1579 0.0008
Cyclosporin currently taking 3 1 0.0567 0.7165 0.0063 0.9369
Cyclosporin never taking 1 0 0 . . .
Cyclosporin never taking 2 0 0 . . .
Cyclosporin never taking 3 0 0 . . .
Prednisolone 3 1 1 0.1061 0.1497 0.5023 0.4785
Prednisolone 3 2 1 0.4422 0.1240 12.7089 0.0004
Prednisolone 3 3 1 0.6023 0.2242 7.2156 0.0072
Prednisolone 4 1 1 0.6125 0.7489 0.6690 0.4134
Prednisolone 4 2 1 0.0692 0.6535 0.0112 0.9156
Prednisolone 4 3 1 -9.4512 139.2 0.0046 0.9459
Prednisolone currently taking 1 1 -0.00427 0.1498 0.0008 0.9772
Prednisolone currently taking 2 1 0.3461 0.1239 7.8094 0.0052
Prednisolone currently taking 3 1 0.7363 0.2202 11.1834 0.0008
Prednisolone never taking 1 0 0 . . .
Prednisolone never taking 2 0 0 . . .
Prednisolone never taking 3 0 0 . . .
533
Table K.63- Odds ratio estimates in VIRAL INFECTION
Odds Ratio Estimates
Effect InfVir Point Estimate
95% Wald
Confidence Limits
Etanercept 3 vs never taking 1 1.030 0.796 1.334
Etanercept 3 vs never taking 2 0.946 0.790 1.134
Etanercept 3 vs never taking 3 0.973 0.723 1.308
Etanercept 4 vs never taking 1 2.235 0.514 9.726
Etanercept 4 vs never taking 2 4.346 2.070 9.126
Etanercept 4 vs never taking 3 11.323 4.586 27.957
Etanercept currently taking vs never taking 1 1.241 0.993 1.550
Etanercept currently taking vs never taking 2 0.933 0.788 1.104
Etanercept currently taking vs never taking 3 1.017 0.773 1.337
Folic Acid currently taking vs never taking 1 0.757 0.597 0.961
Folic Acid currently taking vs never taking 2 0.779 0.656 0.925
Folic Acid currently taking vs never taking 3 0.878 0.669 1.152
Hydroxychloroquine 3 vs never taking 1 1.278 1.027 1.590
Hydroxychloroquine 3 vs never taking 2 1.438 1.223 1.691
Hydroxychloroquine 3 vs never taking 3 1.223 0.944 1.585
Hydroxychloroquine 4 vs never taking 1 0.762 0.185 3.131
Hydroxychloroquine 4 vs never taking 2 2.099 1.106 3.982
Hydroxychloroquine 4 vs never taking 3 1.531 0.477 4.914
Hydroxychloroquine currently taking vs never taking 1 1.249 0.951 1.642
Hydroxychloroquine currently taking vs never taking 2 1.303 1.061 1.601
Hydroxychloroquine currently taking vs never taking 3 1.148 0.823 1.602
Cyclosporin 3 vs never taking 1 0.864 0.645 1.156
Cyclosporin 3 vs never taking 2 1.386 1.153 1.665
Cyclosporin 3 vs never taking 3 1.286 0.955 1.732
Cyclosporin 4 vs never taking 1 0.624 0.225 1.736
Cyclosporin 4 vs never taking 2 1.355 0.807 2.275
Cyclosporin 4 vs never taking 3 0.353 0.084 1.486
Cyclosporin currently taking vs never taking 1 1.000 0.316 3.163
Cyclosporin currently taking vs never taking 2 2.679 1.503 4.777
Cyclosporin currently taking vs never taking 3 1.058 0.260 4.310
534
Odds Ratio Estimates
Effect InfVir Point Estimate
95% Wald
Confidence Limits
Prednisolone 3 vs never taking 1 1.112 0.829 1.491
Prednisolone 3 vs never taking 2 1.556 1.220 1.984
Prednisolone 3 vs never taking 3 1.826 1.177 2.834
Prednisolone 4 vs never taking 1 1.845 0.425 8.007
Prednisolone 4 vs never taking 2 1.072 0.298 3.858
Prednisolone 4 vs never taking 3 <0.001 <0.001 >999.999
Prednisolone currently taking vs never taking 1 0.996 0.742 1.336
Prednisolone currently taking vs never taking 2 1.414 1.109 1.802
Prednisolone currently taking vs never taking 3 2.088 1.356 3.215
535
APPENDIX L: ETHICAL APPROVAL
FOR THE THESIS
536
APPENDIX M: SAMPLE OF ARAD
QUESTIONNAIRE
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538
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540
541
542
543
544
545
546
547
548
549
550
551
552
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554
555
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