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Cornparison of Quality of Life Across Renal Replacement Therapies for End-Stage Renal Disease: A Meta-Analysis
Ji11 Irene Cameron
A thesis subrnitted in confonnity with the requirements for the degree of Master's of Science
Gradoate Department of The Institute of Medical Sciences University of Toronto
O Copyright by Ji11 Irene Cameron ( 1 997)
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Cornparison of Quality of Life Across Rend Replacement Therapies for End-stage Renal Disease: A Meta-analysis. Master's of Science Degree, 1997, Ji11 Irene Cameron, Institute of Medical Science, University of Toronto
A meta-analysis compared emotional distress (ED) and psychological well-being
(PWB) across renal replacement therapies (RRTs) and examined whether differences were
related to: a) treatment moddities; b) case-rnix; or c) methodological rigour. Standard
meta-analytic procedures were used to evaluate published comparative studies.
Significant findings were as follows: renal transplantation was associated with Iower ED
(d = -.43) and higher PWB (d = 52) than in-centre haemodialysis; renal transplantation
was associated with lower ED (d = -.29) and higher PWB (d = -53) than continuous
ambulatory peritoneal didysis (CAPD); CAPD was associated with higher PWB (d = .14)
than in-centre haernodialysis; and in-centre haemodialysis was associated with higher ED
(d = -16) than home haemodialysis. Methodological ngour and case-rnix differences were
not related to the magnitude of treatment differences in ED or PWB. Many cornparisons
were threatened by publication bias. Significant differences were evident between
treatment groups but the patient groups also differed in case-rnix variables relevant to ED
and PWB.
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This work could not have been completed without the interest and help of many
individuals and 1 should like to take this opportunity to express my appreciation.
Foremost, I would like to thank my supervisor, Dr. Jerry Devins, for his excellent
scholarship in guiding and supporting me in this thesis. 1 would also like to thank my
committee members, Drs. Catharine Whiteside and Joel Katz, for their valuable input,
interest, and support. Special thanks are also extended to Dr. John Hunsley for his advice
in the early stages of my thesis regarding meta-analytic method and always being available
for tùrther inquiries. 1 would also like to thank Kirsten Woodend for assisting with the
evaluation of my data extraction form and for her knowledge and interest in meta-analytic
methodology. 1 extend thanks to al1 of the researchers who responded to rny request for
additional information that was not included in their published reports. 1 would aIso like
to thank my feilow students, Monica Bettazzoni and Kksten Woodend, for their
friendship, support, and interest in rny research. And Iastly, T would like to thank and
dedicate this thesis to my husband, Geof, for his love, understanding, and support, without
which this thesis would not have been possible.
Table of Contents
Contents
List of Tables.
List of Appendices.
Chapter 1 : Studying Quality of Life in End-Stage Renal Disease.
End-Stage Renal Disease and its Treatment
Renal Transplantation
Haemodialysis
Pentoneal Dialysis
Quality of Life Implications
Economic Implications
The Typical Study
Independent-(jroup Design
Prospective Repeated-Measures Design
Equivalent-Group Design
Case-Mix Differences
Case-Mix Differences Across Renal Replacement Therapies
Case-Mix Variables and Quality of Life
Methodological Rigour
Interna1 Validity
External Validity
Qualitative or Narrative Research Synthesis
Quantitative or Met a- Analytic Research Synt hesis
Page
i
. . . I I I
1
2
3
4
6
7
9
1 I
12
14
16
17
19
19
21
22
23
24
25
Conducting a Meta-Analysis
Research Question
Identification of Research
The Meta-Analytic Mode1
Effect-Size Estimate
Data Extraction Process
Mean (Summary) Effect-Size Estimates
Sensitivity Analyses
Publication Bias
lnterpretation of the Results
Summary
Chapter 2: Cornpanson of Quality of Life Across Renal
Replacement Therapies: A Meta-Analysis.
Quality of Life
Limitations of Current Research Literature
Thorough Review of Literature
Research Question
Method
Study Identification
Inclusion/Exclusion Criteria
Data Extraction
Meta-Analytic Mode1
Effect-Size Calculation
S tatistical Analyses
Results
Identification of Published Reports
Treatment Cornparison of Emotional Distress and
Psychological WelI-Being
Fixed-Effects Model
Successfiil Renal Transplant vs. In-Centre Haemodialysis
Successhl Renal Transplant vs. Continuous Ambulatory
Pentoneal Dialysis (CAPD)
Successfùl Renal Transplant vs. Home Haemodialysis
Continuous Ambulatory Peritoneal Dialysis (CAPD) vs.
In-Centre Haemodialysis
Continuous Ambulatory Peritoneal Dialysis (CAPD) vs.
Home Haemodialysis
In-Centre Haemodialysis vs. Home Haemodialysis
Evaluation of Heterogeneity
Research Characteristics
Method of Effect-Size Estirnate Calculation
Methodological Rigour
Case-Mix Differences
Random-Effects Model
- - - - - - - - - -
Publication Bias
Discussion
Fixed-Effects Model Result s
Evaluation of Heterogeneity
Random-Effects Model Result s
Interpretation of Effect-Size Estimates
Clinical Implications of Research Findings
Benefits and Limitations of Meta-Analysis
Publication Bias
Methodological Rigour
Research Design
Methodological Limitations of Meta-Analysis
Combining Effect-Size Estirnates within a Study
Further Evaluation of the Relationship between Case-Mix
and Quality of Life
Assessrnent of Physical Status
Emotional Distress and Psychological Well-Being as
Distinct Constructs
Future Cornparisons of Quality of Life Across Renal Replacement
T herapies 1 06
Conclusion 1 09
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Chapter 3: The Future for Quality of Life Studies in End-Stage
Renal Disease and the Role of Meta-AnaIysis.
Reference List.
Meta-Analysis Reference List.
List of Tables
Page
56
66
Summary of Articles Included in the Meta-AnaIysis.
Surnmary Results of Treatment Comparisons of Emotional Distress (Fixed-Effects Model).
Summary Results of Treatment Comparisons of Psychological Weil-Being (Fixed-Effects Model).
Relationship of Research Design Characteristics to Dependent Variable Effect Sizes Across A11 Treatment Comparisons.
Relationship of Research Design Characteristics to Emotional Distress for each Treatment Comparison.
'Relationship of Research Design Characteristics to Psychological Well-Being for each Treatment Comparison.
Case-Mix Summary Information: Renal Transplant vs. In-Centre Haemodialysis (n = 3 0).
Case-Mix Summary Information: Renal Transplant vs. Continuous Ambulatory Peritoneal Dialysis (n = 13).
Case-Mix Summary Information: Renal Transplant vs. Home Haemodidysis (n = 9).
Case-Mix Surnmary Information: Continuous Ambulatory Peritoneal Dialysis vs. In-Centre Haemodialysis (n = 25).
Case-Mix Summary Information: Continuous Ambulatory Peritoneal Dialysis vs. Home Haemodialysis (n = 9).
Case-Mix Summary Information: In-Centre Haemodialysis vs. Home Haemodialysis ( r l = 13).
Availability of Case-Mix Information for Treatment Cornparisons on Emotionai Distress.
Availability of Case-Mix Information for Treatment Comparisons on Psychological Well-Being.
Summary Results of Treatment Comparisons of Emotional Distress (Random-Effects Model).
16 Surnmary Results o f Treatment Comparisons of Psychological Well-Being (Random-Effects Model).
17. Summary Comparisons of RRTs on Case-Mix Variables, Emotional Distress, and Psychological Well-Being.
18. Recommended Reporting Format for Case-Mix Variables.
Page
89
List of Appendices
A. Data Extraction Form
B. Operational Definitions of Dependent Variables
C. Effect Size Calculations and Statistical Analyses
D. Coding Manual for Data Extraction
Page
132
138
139
143
Chapter 1
Studying Quality of Life in End-Stage Renal Disease
As modem medicine progresses and treatments become more complex, issues
concerning patient quality of life gain in importance. In end-stage renal disease (ESRD),
where renal replacement therapy is required to maintain Me, the disease and treatment
modalities pose many demands and adaptive challenges for the affected individual. Many
aspects of life are affected by ESRD, which is characterized by syrnptoms such as fatigue
and weakness, nausea, and the occurrence of other intercurrent diseases. Physical, mental,
social, and vocational well-being are threatened by the disease and its treatment. The
treatment modalities have different characteristics and, therefore, may differentially impact
on well-being.
This thesis consists of three chapters. Chapter 1 provides the background
information relevant to the examination of quality of life cornparisons across renal
replacement therapies (RRTs) in ESRD. This section will describe ES-, the patient
population, current treatment modalities, the differential impact of treatment modalities on
lifestyle, and the resulting impact on quality of life. A description of the typical studies
used to compare the treatment modalities on quality of life with specific examples fiom
this literature and the strengths and weaknesses of these studies highlight the role that a
thorough research synthesis can play in clariijring the literature in this field. Two methods
of research synt hesis, qualitative and quantitative, are compared. The quantitative meta-
anatytic procedure is described in detail with specific references to quality of life research
in ESRD.
The second chapter descnbes a meta-analysis conducted to examine differences in
quality of life across RRTs in ESRD. It consists of a bnef description of the issue, the
specific meta-analytic procedures used, the research included in the synthesis, the results
of the meta-analysis, and a discussion of the research findings.
The third chapter suggests a standardized format for reporting case-mix
infonnation to facilitate h ture meta-analysis. The case-mix variables to be assessed are
described with suggestions for measurement. The reporting format suggested provides
case-mix information separately for each treatment group and will facilitate future research
synthesis.
End-Stage Rend Disease and its Treatment
ESRD is the irreversible loss of kidney fiinction that may occur for a variety of
reasons, such as diabetes and hypertension. The main finctions of the kidneys are to
maintain fluid and electrolyte balance (e.g., sodium and potassium); to remove metabolic
waste products and foreign compounds (e.g,, drugs) frorn the blood; to maintain proper
plasma volume (contributing to artenal blood pressure control); to maintain proper acid-
base balance; and to secrete hormones (e.g., erythropoietin for red blood ce11 formation
and renin, important in the process of salt conservation). When the kidneys are no longer
able to perforrn these tasks, as in ESRD, imbalances will result in death if not treated by a
renal replacement therapy (RRT) and rnedications.
The prevalence of ESRD in the world ranges fiom 509 (Sweden) to 1076 (Japan)
per million population (1 9). The rate in Canada is approximately 6 13 per million
population. In 1995, there were 309 1 new patients (1 04.4 per million population). The
most cornmon causes of ESRD in Canada incfude: diabetes mellitus (26.9%),
glomemlonephritis ( 1 5.9%), and renal vascular disease including hypertension ( 1 8.1 %).
The average age of new patients is 59 years. Approximately 10% of patients die per year,
primarily due to cardiovascular disease (44.7%), undetermined causes (13.6%), social
factors (1 5.8%) including patient refùsal of hrther treatment, suicide, among other causes
(19).
Renal Transplantation
The two main foms of renal replacement therapy (RRT) are transplantation and
didysis. Renal transplantation involves the surgical placement of an irnrnunologically
matched kidney fiom a living-related or cadaveric donor. Individuals who receive a
transplanted kidney can usually retum to a lifestyle sirnilar to their pre-renal failure life.
Their main treatment thereafier involves taking immunosuppressive medications to prevent
the body's rejection of the transplanted kidney. A potential consequence of the prolonged
use of immunosuppressive medications is a weakened immune system making the patient
more susceptible to health problems such as skin cancer and lymphoma (120). Metabolic
side-effects entai1 additional consequences of imrnunosuppressive medication, including
protein hypercatabolism, obesity, hyperlipidaernia, glucose intolerance, hypertension,
hyperkalemia, and interference in the metabolism and action of vitamin D (85). Therefore,
carefiil nutritional management is required to maintain health. Recipients of a transplant
also live with the possibility that their body will reject the transplanted organ. The one,
five, and ten year survival rates for living-related renal transplantation are 92%, 80%, and
64% respectively and for cadaveric renal transplantation are, and 82%, 66%, and 44%
respectively (1 9). In Canada, 8,340 (46%) individuals with ESRD had a fùnctioning
kidney transplant in 1995 (1 9).
Haernodialysis
The second fom of RRT is maintenance dialysis which involves the cleansing of
the blood by an artificial kidney to remove toxins and excess fluid that is normally
removed by the healthy kidneys. There are two forms of dialysis, haemodialysis and
peritoneal dialysis. Haemodialysis involves the surgical creation of a fistula (i.e., the
joining of a vein and artery under the skin in the foream) or other graft vascular access,
insertion of needles, removai of the blood from the body (approximately 4% of the entire
blood supply at any one time), circulation of the blood through an artificial kidney where
toxins and excess fluid are removed, and returning the blood to the body. In 1995,
treatment by haemodialysis was provided to 6,403 (35.3%) ESRD patients in Canada
(19).
In Canada, haemodialysis is usually conducted by staff members in the hospital
(8 1.2%), in a self-care centre in the hospital or elsewhere (1 5.6%), or at home (3.2%)
(19). For in-centre (hospital) haemodialysis, patients corne to the hospital where the
dialysis support staff conduct the treatment. Three treatments per week are typically
required and each session requires between 3-6 hours. Self-care centre haemodialysis is
available for individuals who have been successfùlly trained to administer their own
dialysis sessions, including attaching themselves to the dialysis machines and monitoring
their treatment. It can be conducted in the hospital, in non-hospital facilities, such as
shopping malls or community centres, or in the patient's home. Non-hospital facilities
have the advantage of being fùlly finctioning dialysis facilities while being more accessible
as they are usually located outside of the downtown core of large cities (the most common
location for tertiary care centres). Nurses are available for assistance should this be
required. Home haemodialysis is the least fiequently used form of haemodialysis.
Individuals must be trained to administer their dialysis and their home must be adapted to
accommodate the equipment. The assistance of a home partner (usually a family member)
is required in the event of an emergency.
In contrast to renal transplant recipients who return to a lifestyle similar to their
pre-renal failure lives, individuals receiving haemodialysis must accommodate many
lifestyle disruptions, including dietary, travel, and employrnent restrictions. The dietary
restrictions are severe but necessary to maintain health. Sodium (< 3-6 gm per day) and
fluid restrictions (< 800ml per day) may be necessary to prevent excessive weight gain,
extracellular fluid overload, edema, hypertension, and cardiac congestion (1 0). An
adequate intake of protein, calories, and water-soluble vitamins are recommended to
prevent the common nutritional problems related to ESRD (e.g., protein energy
malnutrition which often results fiom uraemic symptoms, such as nausea and anorexia;
irnpaired lipid metabolism, which is associated with increased risk of cardiovascular
disease; disturbed calcium-phosphate metabolism; and deficiency in water soluble
vitamins). As a result of these restrictions, the diet associated with maintenance
haernodialysis tends to taste very bland. In addition, fluid-intake restrictions often lead
patients to experience extreme thirst (1 10).
Due to the ongoing nature of the treatment -- i.e., thrice weekly treatment is
O
required to maintain life -- other lifestyle restrictions are common, including the domain of
travel and employment. Travel for work or pleasure becomes very difficult as patients
must arrange to have dialysis in any destination to which they wish to travel. The time
requirement for haemodialysis treatments often makes it difficult for individuals to
maintain employment. Hospital dialysis facilities in selected areas of Canada, including
Toronto, are filled beyond capacity which makes the scheduling of dialysis sessions
inflexible resulting in increased interference with work schedules (71). Haemodialysis
conducted by self-care in the hospital, alternative facilities or at home allows more flexible
scheduling. This is especially true for home haemodialysis which can be conducted at the
patient's convenience. Therefore, it is usually easier for self-care haemodialysis patients to
maintain participation in valued activities and interests, such as employment .
Peritoneal Dialysis
The other form of dialysis is peritoneal dialysis. Continuous ambulatory peritoneal
dialysis (CAPD) is the most common form accounting for 96% of al1 peritoneal dialysis
patients in Canada (1 9). Peritoneal dialysis involves the placement of approximately two
litres of dialysate fluid through a catheter into the peritoneal cavity of the abdomen where
the peritoneal membrane acts as a filter across which metabolic wastes and excess fluid are
removed. With CAPD, dialysate-fluid exchanges must be completed four times per day
and can be camed out at home, or anywhere sanitary conditions exist. Each exchange
requires approximately 30 to 60 minutes. In Canada, in 1995, peritoneal dialysis was the
treatment for 3,394 (1 8.7%) individuals with ESRD.
The lifestyle restrictions associated with CAPD are considerably less than those
with haemodialysis due to the continuous nature of the treatment. Dietary restrictions are
less severe; for example, fluid and salt restrictions are relaxed, but the sarne nutritional
concerns exist as for haemodialysis (e.g., protein energy malnutrition) (60). Maintainhg
employment is possible when the work environment is amenable to dialysate exchanges.
Travel restrictions can remain an important issue, however. Although, bringing bags of
dialysate on vacation is possible, this is cumbersome and it may be more convenient to
arrange to obtain dialysate at vacation sites. Pentonitis is a common complication of
peritoneal dialysis. It is an infection of the peritoneum and accounts for 7.3% of the
reasons for discontinuing CAPD treatment (1 9). A study conducted in Manitoba, Canada
observed a rate of 0.766 episodes of peritonitis per patient per year (37).
Ouality of Life Im~lications
There are significant quality of life implications for individuals with ESRD treated
by RRT. ESRD and its treatment are associated with many physical syrnptoms, including
fatigue and nausea. Lifestyle disruptions are common, including the time demand for the
treatment regimen, severe dietary restrictions, decreased social involvement, decreased
participation in paid employment, and decreased freedom to travel. A consequence of
these lifestyle disruptions (i.e., illness intrusiveness) may be a negative emotional response
(27,28). Earlier research has reported suicide rates 400 times that of the general
population (2). Additionally, depression in ESRD patients is a common reason for referral
to Psychiatrists (92) and the discontinuation of treatment (95).
The results of studies evaluating depression prevalence rates in dialysis patients are
equivocal. Smith (1 14) highlighted studies in which the rate of depression in ESRD
-
patients ranged fiom 0% (40,lZ) to 100% (9 l)! There are potential reasons for the
varying prevalence rates of depression in dialysis patients. Different rneasurement
strategies are used including, structured psychiatric interviews and self-report rating
scales. Structured psychiatric i n t e ~ e w s use clinical diagnostic criteria to deterrnine if a
psychiatric diagnosis is present. Self-report rating scales assess the symptoms of the
psychiatric condition (e.g., depression) and assign a total score for the level of
symptomatology. A diagnosis of depression is determined by selecting a cut-off point for
the total score, above which depression is diagnosed and below which it is not. Diagnoses
of depression may Vary across studies when self-report measures are used because
different authors may utilize different cut-off points. The use of self-report tests in chronic
illness populations is a problem when the symptoms of depression included in the
measurement instrument over-lap with symptoms of the medical condition. High levels of
physical syrnptoms may result in the over diagnosis of depression. Additionally, the
characteristics of the patient population may Vary across studies contributing to the
varying rates of depression (e.g., the gender distribution) (29,96,114).
In ESRD, positive emotional states are less frequently studied. For example, some
research has indicated that higher levels of life satisfaction and positive affect are
associated with rend transplantation as compared to dialysis (27,36). Bradburn (13)
hypothesized that positive and negative affect are distinct constructs and that they are
influenced both by comrnon and non-overlapping factors. For exarnple, in Bradburn's
research, women reported significantly more negative affect than men but did not differ
with respect to positive affect. Also, as age increased, reported positive affect decreased
but age did not relate to negative affect.
To address the discrepancy in the diagnosis of depression among ESRD patients,
Smith (1 14) undertook a study to evaluate depression using three commonly used
diagnostic techniques. Sixty individuals, who had been receiving RRT for at least one
year, were randomly selected fiom 41 9 patients receiving RRT fiom four treatment
facilities. Three instruments were used to assess depression: a) Schedule for a c t i v e
Disorders (consistent with the criteria of the American Psychiatric Association, DSM-III)
(33); b) Beck Depression Inventory (self-report instrument) (8); and c) Multiple Affect
Adjective Checklist (self-report instrument) (126). The Beck Depression Inventory, the
Multiple Aflèct Adjective Checklist, and the Schedule for Affective Disorders diagnosed
depression in 47%, 17%, and 5% of the participants respectively. This research revealed
that the use of cut-off points on seIf-report measurement instruments tend to over-
diagnose depression at least in ESRD patients.
As has already been highlighted, the degree of lifestyle disruptions (i.e., illness
intrusiveness) differs across the RRTs. Renal Transplantation is thought to have the least
lifestyle disruptions, followed by home haemodialysis and CAPD. In-centre haemodialysis
is thought to have the greatest lifestyle disruptions. Due to the differing levels of illness
intrusiveness across RRTs, it may be expected that emotional well-being would also differ
across the RRTs (27,28).
Economic Implications
There are also econornic implications regarding the use of the different treatment
modalities. Tremendous financial costs are associated with RRTs. In Canada (42), the
average yearly per-patient costs in 1993 for in-centre haemodialysis, CAPD, self-care
haemodialysis, and home haemodialysis were $88,585, $44,790, $55,593, and $32,570
respectively. This included the costs associated with the hospital, professional fees,
medications (e.g., erythropoietin), and patient-specific expenses (e.g., transportation).
After successtùl rend transplantation, the average yearly cost (in 1988) associated with
this RRT was approximately $10,000 (83). There are also significant start-up costs
associated with treatment. For example, in Canada the start-up costs in 1988 for in-centre
haemodialysis, CAPD, self-care haemodialysis, and rend transplantation are approximately
$8,032, $10,002, $6,924, and $25,000 respectively (83). Patients who have difficulty or
complications associated with their RRT may have to switch to another f om of treatment
(e.g., switching fiom CAPD due to peritonitis or switching to dialysis after a failed
transplantation) and, therefore, incur additional start-up costs. In Canada, the United
Kingdom, and other countries with govement-Funded health-care programs, the least
expensive treatment is often preferred because it saves public funds. Unfortunately, when
cost saving is a priority, patients rnay be placed on the least expensive RRT which, if not
the best fit for the patient, may provide inadequate treatrnent requiring the patient to
change to another RRT. Modality switching is not desirable due to the additional start-up
costs that would result. In countries, such as the United States and Japan operating within
private health-care systems, more expensive treatment modalities may provide increased
incorne for the health-care companies providing dialysis (often owned by Nephrologists)
and may, therefore, be utilized more fiequently. For exarnple, in Japan a large proportion
(94%) of individuals with ESRD receive haemodialysis as compared to peritoneal dialysis
- -
and this is thought to be due to the higher physician reimbursement associated with
haemodialysis (76,77).
The treatment for ESRD may differentially impact on the Iifestyles of the patients.
Lifestyle disruptions (illness intrusiveness) are associated with emotional distress and
psycho~ogical well-being. Therefore, it is believed that the treatment modalities that
introduce severe lifestyle disruptions will be associated with increased emotional distress
and decreased psychological well-being. Many studies have compared RRTs on emotional
distress and psychological well-being. The three most commonly employed research
designs, independent-group, prospective repeated-measures, and equivalent-group
designs, are limited in their internal and external validity (discussed later in more detail).
Intemal validity concems the ability of the research to rule-out alternative explanations for
the research findings. External validity concerns the representativeness of the research
findings to al1 individuals with ESRD. As will be illustrated in the next section, threats to
internal and extemal validity are common in the ESRD literature concerning quality of life
differences across RRTs. These compromise the interpretability of individual findings and
render the literature more complex and difficult to synthesize.
The Tv~ical Study
To compare RRTs on emotional distress and psychological well-being, three types
of research designs are typically employed: cross-sectional independent-group, prospective
repeated-measures, and equivalent-group designs.
Independent-Group Design
The most common is the cross-sectional independent-group design comparing
independent samples of ESRD patients at one point in time. For example, Evans (36)
conducted a multi-centre cross-sectional cornparison of quality of life across RRTs. The
treatment groups included renal transplantation, in-centre haemodialysis, home
haemodialysis, and CAPD. Psychological well-being was assessed by Campbell's Index of
Well-being which is a composite measure of general affect and life satisfaction ( 17).
Sociodemographic (e.g., age, sex, education, and race), medical (e.g., primary diagnosis,
CO-morbidity, length of tirne on current treatment, and whether there was a history of a
previous transplant failure), and objective indicators of quality of life (e.g., fûnctional
impairrnent and ability to work at a job for pay) were also assessed. Analysis of co-
variance was employed to compare psychological well-being across treatment groups.
Sociodemographic and medical characteristics that differed significantly between the
treatment groups were retained as covariates. The findings, based upon the covariance
analyses, suggested that renal transplant recipients reported higher quality of life than al1
dialysis groups combined; home haemodialysis patients reported higher quality of life than
CAPD and in-centre haemodialysis patients; and CAPD and in-centre haernodialysis
patients did not differ on quality of life.
There are advantages and disadvantages associated with the use of the cross-
sectional independent-group design. One advantage is the convenience of conducting a
study with pre-existing treatment groups. This design is simple and comparatively
inexpensive to administer because patients are assessed only once. There are also
disadvantages that can threaten internal and external validity. The Evans et. al. ( 198 5)
study assessed and statistically controlled for many potential case-mix differences between
the treatment groups but many other studies using this design do not assess or control for
this threat to internal validity. Additionally, there rnay be other variables, such as social
support, that were not measured or controlled which rnay also be related to quality of life.
For example, the level of social support rnay differ across the treatment groups as
individuals who receive a form of home dialysis must have assistance (usually fiom a
farnily member) with treatment. Individuals who receive hospitai dialysis rnay not have the
same level of social support. If social support is also related to quality of life, observed
treatment differences rnay be inftated or underestimated -- e.g., if individuals with Iess
social support are more likely to receive in-centre haemodialysis and report less
psychological well-being, then observed treatment differences rnay also be related to
differences between the treatment groups regarding social support. The main threat to
internal validity is that naturalistic experimental designs do not incorporate randomization
which can ensure that the groups are as equivalent as possible (21).
A threat to the external validity of this research design concerns the sampling of
research participants. In the Evans, et.al. (1985) study, the research sample was a sample
of convenience, i.e., no systematic sampling strategy was used to recruit participants.
Participants were recruited while in the hospital to see their nephrologist for treatment or a
regular check-up. It is possible that happier or healthier individuals agreed to participate
while distressed or sicker individuals declined to participate (a "volunteer bias", (1 02).
Therefore, the results of this experiment would not be generalizable to al1 individuals with
ESRD (ie., reduced external validity).
Prospective Repeated-Measures Design
Prospective repeated-measures designs are often used to compare transplant
recipients to in-centre haemodialysis patients. For example, a study by Rodin (94)
evaluated 42 dialysis patients on the waiting list for a renal transplant using the Sickness
Impact Profile (9) and the Beck Depression Inventory (8). Patients were re inte~ewed six
months after receiving a renal transplant. Repeated-measures analysis of variance
compared patient levels of emotional distress while on haemodialysis to those after
receiving a transplant. The research findings suggested that emotional distress
significantfy decreased after successfiil transplantation as indicated by both the Sickness
Impact Profile (psychological dimension) and the Beck Depression Invento~y. The
primary advantage of this design is that the same individuals are compared on each of the
treatment rnodalities and, therefore, observed differences are attributable largely to the
treatment. The main disadvantage with this design is lack of control for lifestyle or
physical changes that may also occur after treatment rnodality changes. It is difficult to
determine the extent to which quality of life changes are related to the new treatment
modality (e.g., lower illness intrusiveness), changes in physical status, as individuals with a
kidney transplant tend to be in better general health after transplantation (e.g., decrease in
uraernic symptorns), or lifestyle changes (e.g., change in marital or employment status).
Therefore, the changes in treatment modality, physical status, and lifestyle may be
associated with increases in psychological well-being and decreases in emotional distress.
Failure to mle out physical status and lifestyle changes in the explanation of treatment
differences in psychological well-being and emotional distress threatens the interna1
validity of the research.
A second limitation of this design concerns the possible exclusion of individuals
fiom the transplant cornparison group who experience a failed transplant. Ofien
participants who experience a failed transplant are re-grouped into the dialysis category
for follow-up comparisons. This results in comparatively healthier successfùl transplant
recipients being compared to a dialysis group that includes individuals who previously
received but lost a transphnted kidney. The inclusion of patients who experienced a failed
transplant rnay inflate the reported emotional distress of the dialysis group because the
experience of a failed transplant rnay itself be distressing (56).
A third limitation is referred to as the "honeymoon" effect (94). Individuals who
receive a renal transplant after being on haemodialysis rnay experience decreased distress
and increased well-being shortly afier their transplant due to increased fieedom and
optimism. Measurement of distress and well-being too soon after transplantation rnay
reflect this honeyrnoon effect which rnay not be an enduring change in the level of distress
and well-being (although it is difficult to speciSl precisely how long this period might last).
Additionally, in earlier studies, positive mood shortly following transplantation rnay also
have been attributable to high doses of steroid irnmunosuppressive medications (52).
Concerning external validity, the generalizability of these results to the broader
population of individuals with ESRD is also threatened because the people who are
eligible for a renal transplant tend to be younger and healthier, overall, than those not
eligible (64). Therefore, the results of prospective repeated-measures studies are
4 -
generalizable to the population of individuals eligible for renal transplantation but not to al1
individuals with ESRD.
Equivalent-Groups Design
The final design used is an equivalent-groups design. This attempts to control for
threats to intemal validity by cornparhg treatment groups that are equivalent with respect
to certain variables related to the dependent variable of interest. In ESRD, patients on one
mode of RRT are matched on key variables to patients on another mode. In this manner,
observed treatment group differences on the dependent variable are less likely to be
related to non-treatment related differences between the groups. For example, Maida
et.al. (68) matched in-centre haemodialysis patients to CAPD patients for sex, age,
duration of dialysis, presence or absence of diabetes, and ethnicity. Emotional distress and
psychological well-being were assessed by the Profile of Mood States depression and
vigour sub-scales, respectively (69). The findings did not reveal significant differences
between the treatment groups in emotional distress or psychological well-being. The main
strength of this design is that treatment groups being compared tend to be more sirnilar
with respect to certain characteristics than if treatment groups had not been matched.
Maida et.al. (1 991) evaluated whether their matching was successfÙ1 and found that their
groups differed significantly only on diabetic status. The main limitation of this design is
that matching cannot create complete equality between treatment groups as is possible
with randomization.
Although this design may enhance the interna1 validity of the findings, the
generalizability of these results is weakened as the matching procedure can result in
I I
treatrnent groups that differ considerably from the population of individuals on each fom
of RRT (e.g., age matching of in-centre haemodialysis patients to a comparatively younger
rend transplant group, can result in an in-centre haemodialysis group that is younger than
the population of individuals receiving in-centre haemodialysis).
In different ways, each of these research designs attempts to control for threats to
internal validity by niling out alternative explanations for research findings. Random
assignment of patients to RRTs is impractical due to the physical and social requirements
for each treatment (e.g., haplotype matching of the kidney in transplant recipients and the
requirement of a home and a home partner for home dialysis). Without random
assignment, however, it will be difficult for any of the research designs to control for al1
possible threats to internal validity.
The main threat to internal validity is the lack of control for case-mix differences
between the treatrnent groups as an alternative explanation for treatment-group
differences in quality of life. Additionally, quality of life pior to ESRD and RRT is rarely
evaluated. Therefore, it is impossible to establish a cause and effect relationship between
treatment and quality of life because higher levels of emotional distress may have existed
in one treatment group prior to the start of RRT. Another limitation pertains to differences
across studies in degree of methodological rigour which influences the interpretability and
generaiizability of the research findings, for example, use of established instruments to
insure valid and reliable measurement of constmcts.
Case-Mix Differences
As indicated, research reporting differences across treatment modalities for ESRD
a .- regarding quality of life may attribute differences to the treatment modalities themselves
but they may also be due to non-treatment differences across groups, i.e., case-mix
differences. Patients are not randomly assigned but are preferentially assigned to receive a
particular treatment modality and, therefore, patient groups differ on non-treatment related
variables. For example, it is recomrnended that a person receive CAPD instead of in-
centre haemodialysis if they have cardiovascular disease, are diabetic, are mentally stable,
and have a home (43,47,77). Rabinowitz (1 978) compared patients accepted into a renal
transplant prograrn with those not accepted and found that the rejected patients had lower
levels of education, intelligence, and socioeconomic status. Laupacis (1 996) studied
individuals on the transplant waiting list and found that those who were transplanted were
significantly younger and had been receiving dialysis for a shorter period. Therefore,
ESRD treatment groups diRer with respect to sociodemographic characteristics and
physical status, i.e., case rnix. Case-mix differences between treatment groups are
important considerations if a) if the treatment groups differ on these variables and b) the
case-mix variable is related to quality of life.
To identiS, case-mix differences between ESRD treatment groups, previous
research was examined, including a large multi-centre study of quality of life in ESRD
(36). Significant treatment group differences regarding age, sex, marital status, primary
renal diagnosis, presence of CO-rnorbid conditions, duration of treatment, and experience
of failed transplant were evident. These case-rnix variables can be categorized as
indicators of either sociodemographic status or physical status. sociodernographic
variables include age, gender, marital status, and socioeconomic status. Physical status
- -
variables describe the treatment groups according to their primary renal disease (e.g.,
glomerulonephritis), physical complications associated with RRT (e.g., peritonitis), and
general non-renal health (e.g., CO-morbid conditions such as diabetic complications and
cardiovascular disease).
Case-Mix Differences Across Renal Replacement Therapies
General trends for case-rnix differences between treatment groups were observed.
Transplant recipients tended to be younger, male, single, better educated, more intelligent,
higher socioeconomic status, and in better physical health than dialysis patients
(36,64,86,113). In-centre haemodialysis patients tended to be older, in poorer physical
health, less likely to be married, and lower in socioeconomic status than transplant, CAPD,
and home haemodialysis patients (36,105,113). Home haemodiaiysis patients are more
likely to be mamed, male, younger, and in better physical health than in-centre patients
(36,72,105). CAPD patients tend to be similar to home haemodialysis patients with
regard to sociodemographic and physical status indicators (36,72).
Case-Mix Variables and Quality of Life
There is conflicting evidence regarding the relationship between case-mix variables
and quality of life in ESRD. Increasing age was found to be related to increasing
emotional distress and increasing life satisfaction (4,27). Conversely, Oldenberg (78)
found that age did not significantly relate to emotional distress. Gudex (46) observed that
female gender was associated with higher levels of emotional distress but no significant
gender differences were observed by Oldenberg (78). Married ESRD patients have
reported significantly higher levels of psychological well-being and less emotional distress
--
than non-married patients (36,81,84). Higher socioeconornic status was related to greater
psychological well being and less emotional distress (36,s 1,124). Vocationally active
patients (i.e., employed for pay) have reported less emotional distress but confounding this
observation was the fact that the vocationally active patients were also more highly
educated and younger than their unemployed counterparts (1 24). Duration of treatment
has been shown to be significantly related to emotional distress. House (53) observed that
patients receiving dialysis for longer durations reported fewer psychiatric disorders. This
result was not related to the gender, age, or marital status of the patients. This research
was limited, however, because the rate of psychiatric disorders was not reported for the 10
patients lost to follow-up due to death. Supporting this result, Kutner (62) observed that
total months on dialysis inversely related to depression and anxiety (r = -. 18 and -.22
respectively). Additionally, individuals with newIy diagnosed chronic physical conditions,
including ESRD, reported higher levels of emotional distress (Cassileth, 1984).
ConverseIy, longer duration of dialysis and hospital dialysis were associated with increased
emotional distress in another study (78). ESRD patients in poorer general non-renal
health, as indicated by CO-morbid conditions and physical syrnptoms, reported less
psychological welI-being and increased emotional distress (6,27,46). Additionally, the
experience of a failed renal transplantation was associated with a negative impact on
quality of life (56). Devins et al. (27) reported that emotional distress increased with
increasing numbers of failed transplants. Evans (36), on the other hand, found that non-
renal health did not significantly relate to subjective quality of life (i.e., psychological well-
being).
L l
The relationship of case-mix variables to quality of life in ESRD is inconsistent
across studies. This inconsistency was mainly due to the fact that examining the
relationship between case-mix variables and quality of life was not usually the primary goal
of the research. As a result, the research method and sampling procedures were not
designed specifically to examine these relationships. In some studies, inadequate statistical
power may have resulted in the inability to detect significant relationships between quality
of life and case-mix variables. Multivariate methods require larger sarnple sizes when the
individual relationship of each case-mix variable to quality of life is exarnined.
Additionally, the use of non-specific measures, e.g., using rate of hospitalizations to
indicate the general non-renai health of patients, may have limited the ability to observe
significant relationships.
To clarify the relationship of case-rnix variables to quality of life in ESRD, studies
focusing on case-mix variables and quality of life are needed. Standardization of the
measurernent of case-mix variables, i.e,, which variables should be assessed and how they
should be measured, will facilitate this. Also, the research must have adequate sample size
to detect significant relationships with dependent variables.
Methodologicat R i ~ o u r
The studies exarnining differences in quality of Iife across RRTs differ in their
control for case-mix differences between treatment modalities as well as other issues
relevant to rnethodological rigour. Methodological rigour is reflected in the technical
merit of an experiment and has implications for the interpretability and generalizability of
the research findings (SI). Lipsey (66) described methodological rigour in terms of
--
research design, research participant selection, instrumentation, external validity, and
statistical conclusion validity. He found that the degree of methodologicai rigour could
result in an over- or underestimation of the magnitude of treatment effects depending upon
the field of research, including education, mental health, and organizational interventions.
Interna1 Validity
Experirnental evidence can be judged by two criteria, internal and external validity.
The internal validity of an experiment concerns the extent to which the experiment rules
out plausible alternative explanations for research findings. Two important threats to
internal validity are selection bias and instrumentation (66). In studies comparing RRTs
with respect to quality of life, the threat of selection bias is related to the previously noted
fact that individuals are often preferentially allocated to a treatment modality for ESRD.
Failure to rule out the effects of non-treatment related explanations for the research results
compromises the internal validity of the research.
The threat to internal validity related to instrumentation results fiom the use of
measurement instruments for which strong psychometric properties have not been
established. If these instruments do not meet traditional critena for psychometric
adequacy, i.e., reliability and validity, caution must be applied in the interpretation of the
results. For example, Parfrey (80) developed a new Atfèct scale to compare transplant
patients to in-centre haemodial y sis patients. The reliability and validity of t his scale was
not assessed in the published report. Therefore, interpretation of the treatment group
differences obtained using this single measure may be questionable. Unreliable
instruments may also be less able to detect significant differences between groups, leading
to type 11 errors.
External Validity
Extemal validity concerns the generalizability of the research results beyond the
particular research situation. An important aspect of external validity is the
representativeness of the research participants to the popuIation of individuals with ESRD.
Representativeness is usually enhanced through the stratified selection of participants to
represent relevant dimensions of the population (e.g., gender or socioeconomic status).
Research designs attempting to enhance internal validity may compromise extemal
validity. The equivalent-group design attempts to strengthen internal validity by matching
research participants with respect to certain charactenstics that may differ between the
treatment groups and may also be significantly related to the dependent variable. External
validity is weakened, however, when the resulting treatment groups differ from the
population of individuals receiving these treatments. For example, this occurs when renal
transplant recipients, who are typically younger, are matched for age to in-centre
haemodialysis patients, who are typically older. Either the resulting renal transplant group
is older than the population of individuals who have received a renal transplant or the in-
centre haemodialysis group is younger than the population treated by this form of RRT.
Results such as these would raise questions as to the external validity of the study. In
general there is conflict between intemal and extemal validity. Enhancing one forrn of
validity usually diminishes the other. Interna1 validity is usually emphasized in preliminary
research where a relationship between two variables is originally exarnined. After the
relationship is established, it is examined in a broader context to evaluate its
generalizability in the population.
Many studies have exarnined differences in quality of life across RRTs. The
literature is very complex. Studies differ with respect to research design, rnethodological
rigour, and their assessrnent and control of case-mix differences between treatment groups
as alternative explanations for research findings. Therefore, it is difficult to form an
adequate synthesis of the research through irnpressionistic reading. A structured and
cornprehensive synthesis of this lit erature would assist in clari@ng the relat ionship
between RRTs and quality of Iife. There are two approaches to research synthesis,
qualitative and quantitative.
Oualitative or Narrative Research Synthesis
The traditional literature review process involves a narrative or qualitative review.
Such reviews, usually conducted by experienced researchers in the field, provide a
subjective overview of the research, draw general conclusions based on research findings
and temper these in light of the strengths and weaknesses of the research literature under
consideration. As the body of evidence expands and becomes more complex, as in the
research comparing quality of life across RRTs, it becomes more difficult to synthesize
using the traditional narrative approach. This is because it is difficult to make general
conclusions based on very different studies with confiicting results and different strengths
and weaknesses. To facilitate the review of large complex literatures, qualitativeharrative
reviewers often select a subset of articles to be included based on idiosyncratic criteria,
such as the use of "reliable" measurement instruments, adequate statistical tests, and use
of preferred experimental designs. The result is a smaller, more manageable set of findings
making it easier for the reviewer to make sense of sometimes conflicting research results.
The main limitation of this approach, however, is that some of the selection cnteria may be
arbitrary and criteria may be applied inconsistently across articles. The resulting subset of
articles may be biased, ofien in favour of the reviewer's perspective (1 15).
An example of a traditional narrative/qualitative literature review in ESRD is a
review by De-Nour (24). This review summarized "recent" research comparing CAPD to
haemodialysis and dialysis to transplantation regarding quality of life. Many different
elements of quality of life were examined, including fùnctional status (e.g., Karnofsky
index), employment status, subjective happiness, physical well-being, social well-being,
activities of daily living, and satisfaction with treatment. The article reviewed seven
studies comparing haemodialysis with CAPD, although two of them contained the sarne
group of subjects, and 1 1 studies comparing dialysis to transplantation. The results of
each study were summarized. At the conclusion of this article, differences between the
treatment groups had not been clarified. Suggestions were offered for future research,
including the use of prospective longitudinal studies based on vaiid and reliable
measurements. Although the author is one of the leading investigators concerning
psychosocial aspects of ESRD, this review was limited because only a small
unrepresentative sample of the literature was reviewed and the magnitude of difference
between treatment groups was not revealed.
Ouantitative or Meta-Analvtic Research Synthesis
Meta-analysis was developed to overcorne the limitations of traditional qualitative
or narrative reviews. This procedure strives to be comprehensive, quantitative, and
--
objective. A meta-anaïysis attempts to include al1 of the research conducted in the field of
interest (both published and unpublished). Studies should not be excluded based on
arbitrary criteria, such as type of research design. Study findings are transfomed into an
effect size, an estimate of the magnitude of the difference between treatment groups. An
effect size is standardized to facilitate the cornparison of findings using different
rneasurement instruments and varying statistical techniques. A summary effect size is
computed by combining the individual studies' effect sizes to give an overall estimate of
the magnitude of the di fference between treatment groups. A meta-analysis also allows
the reviewer to examine research characteristics and t heir relationship to the magnitude of
the treatment difference, such as the type of research design, the methodological rigour of
the study, or characteristics of the research participants (1 15).
Meta-analysis has many advantages over traditional narrative reviews and original
research. One of this procedure's advantages is its comprehensiveness. An attempt is
made to include al1 of the research conducted and, therefore, the results are more
representative of the true or population difference between the treatment groups than can
be achieved by a narrative review that is limited to a subset of the studies. Another
advantage is the use of effect-size estimates to indicate the magnitude of the treatment
difference. Traditional narrative reviews reIy on statistical significance (Le., p values)
which are only indications of the reliability of the observed treatment difference, not the
size of the difference (1 15). Usually the research included in a meta-analysis uses a variety
of different measurement instruments to evaluate the construct of interest, (e.g., emotional
distress) and this diversity can enhance the generalizability of the findings. For exarnple, if
- .
many studies found that renal transplant and haemodialysis groups differed in depressive
symptoms as assessed by the Beck Depression Inventory, the results may not be
generalizable to the broader construct of emotional distress but only to depression as
assessed by the Beck Depression Inventory. In contrast, if the groups were compared and
found to differ on many alternative measures of emotional distress then the results would
be more generalizable.
There are also disadvantages associated with meta-analysis due to its reliance on
previously conducted research. Many meta-analyses include only published research
because it cm be difficult to identie and obtain unpublished studies. c'Publication bias"
can threaten the results of a meta-analysis because statistically significant treatment effects
are more likely to be published than non-significant results (44,97,12 1 ). However, the
threat of publication bias can be evaluated by a method developed by Rosenthal (97) (to
be described later).
Another disadvantage of meta-analysis, is its reliance on the information provided
in published articles. Variables of interest may not be reported in the original articles or
the information may not be presented in a manner amenable to meta-analysis. For
example, in comparative studies of quality of life across RRTs, sociodemographic
information rnay be reported globally for the entire research sample but not separately for
each treatment group, preventing the comparison of treatment groups in terms of this
relevant information. There is also the possibility that the information may have been
transcribed incorrectly in the journal article but this type of error would be rare and
difficult to detect. The meta-analysis data extractor may also make a transcription error.
-- The accuracy of the data-extraction process can be enhanced by providing training to the
data extractor and by evaluating reliability.
An additional disadvantage of meta-analysis is the varying degree of
methodological Rgour across the studies selected for inclusion. To evaluate whether
poorer quality research yields different results than higher quality studies, the meta-analyst
can evaluate the methodological rigour of the individual studies and quantitatively examine
its relationship to the magnitude of the treatment differences.
Conductin~ A Meta-Anabsis
To conduct a meta-analysis the following procedure is recornrnended: a) speci@ a
research question and conceptually and operationally define the variables of interest; b)
identie articles to be included in the research; c) select a meta-analytic model, i.e., fixed-
or random-effects; d) select the appropriate effect-size estimator; e) develop and evaluate
a data extraction process to ensure reliability; f ) calculate effect sizes; g) summarize the
treatment differences, Le., summary effect sizes; h) evaluate the distribution of the effect
sizes, i.e., test of homogeneity; i) evaluate the heterogeneity arnong effect sizes (fixed-
effects model) and the relationship between the dependent variables and any variables of
interest through sensitivity analyses; j) evaluate the threat of publication bias; and k);
interpret the results. Note that item h is relevant for the fixed-effects mode1 only.
Research Question
Clearly defining the research question to be addressed is the first step of the
process. This question must represent a research question addressed in the literature often
enough to warrant a synthesis. Definitions of the construct to be examined and
LY
operational definitions of the dependent variables must be clearly specified to facilitate the
selection of research to be included in the meta-analysis. The construct is a theoretical
definition of the variables of interest, e.g., emotional distress is the negative emotional
response of individuals with difficult life situations, such as chronic illness, and is
characterized by negative affect, depressive symptorns, and anxiety. The operational
definition indicates how the construct can be measured, e.g., Beck Depression Inventory
(8), Center for Epidemiologic Studies-Depression Scale (122), State-Trait Anxiety
Inventory (1 17), or the Bradburn Mect Balance Scale - Negative AfTect subscale (13).
Identification of Research
After clarifjmg the research question and theoretical and operational definitions,
al1 of the literature to be included in the meta-analysis is identified in a two step process.
First, a pool of studies is generated from which relevant research can be retrieved, e.g.,
studies of quality of life in ESRD. The main sources of information usually include:
databases of experts in the field (when accessible); cornputer databases, such as Medline,
Psychlit, and Cinahl; and reference lists fiom literature reviews and related articles. The
Medline computer database represents over 6500 medical journals, Psychlit represents
over 1300 social science journals, and Cinahl represents over 650 nursing journals. An
extensive fist of key words is developed to search these databases to identie a pool of
literature fiom which relevant articles can be retrieved. Unpublished research is more
difficult to retrieve. Researchers can be contacted directly in an atternpt to find
unpublished research but this strategy is biased towards more established researchers. In
recent years, research registries have been developed (30). These registries contain
4"
information regarding research being conducted in certain fields (e.g., cancer and AIDS).
The author is unaware, however, of a registry in the field of Nephrology.
The second step invofves the selection of the relevant articles from this larger pool.
Specific inclusion and exclusion criteria are developed to assist in the identification of
articles to be included in the meta-analysis, e.g., for the present study articles were
included if they reported quantitative treatment comparisons and evaluated emotional
distress and/or psychological welt-being in ESRD. In meta-analysis, research participants
may only be included in the analysis once for each treatment effect examined; therefore,
the independence of research participants rnust be considered. Articles by the sarne
research group are compared to ensure that the research participants have not been used in
more than one publication by examining when, where, and how many subjects were
obtained. If more than one published article uses the same group of subjects, the articles
are counted as one study and the overlapping publications are used to gather al1 of the
required information.
The Meta-Analytic Model
The meta-analytic model guides the statistical procedures of the meta-analysis.
Many issues must be exarnined before selecting a fixed- or random-effects model. Some
knowledge is required of the magnitude of the "true" or population treatment difference in
the dependent variable and characteristics of the research examining this issue, including
research designs, measurement instruments, and the characteristics of the research
participants. The fixed-effects model assumes that the "true" or population effect size is
fixed, e.g., the difference between transplant and in-centre haemodialysis patients in
emotional distress is consistent across the population of individuals with ESRD. It also
assumes that each study examining this research question will provide an effect-size
estimate that varies randomly around the 'Irue" or population effect size. This assumption
is evaluated by a test of homogeneity (see below). The characteristics of the studies
included in the synthesis must be uniforrn or 'Yixed" with respect to the treatment of
interest, e.g., transplantation or dialysis, the research design, the research participants,
measurernent of the variables of interest, and the effect-size parameter. if some variability
does exist between studies, for example, with respect to the research participants, detailed
information must be provided in the published report to facilitate examination of the
relationship between study characteristics and the magnitude of the difference in the
dependent variable between treatment groups. The generalizability of fixed-effects meta-
analysis is evaluated in terms of its representativeness of a universe of similar studies
where the only research characteristic that is variable is the random selection of research
participants (49).
The random-effects mode1 assumes that the '?rue" or population effect size can
take on any value (Le., it is a random variable). Thus, the difference in emotional distress
between transplant and in-centre haemodialysis patients need not be uniform across the
ESRD population. This model assumes that the research included in the meta-analysis
represents a random selection from a universe of possible research. The random-effects
model does not hold fixed the study characteristics that may influence the magnitude of
the treatment difference. Therefore, a large number of uncontrollable factors will
influence the outcome of each individual research experiment and this between-study
"- variability can be taken into consideration by this model. For example, differences
between studies with respect to the research participants, setting, and measurement
instruments, can influence the magnitude of the observed treatment difference. The
generalizability of this model is to a universe of possible research examining the research
question (89).
The main difference between the fixed- and random-effects models is the variance
component for each study's efEect size and, therefore, the variance component for the
summary effect size. For the fixed-effects model, the variance of the individual effect sizes
stems exclusively fiom within-study variability, Le., variability around the measurement of
the dependent variable in the individual studies. For the random-effects model, the
variance has two components, within-study and between-study variability, i.e., the
variability around each study's effect size stems fiom the variability in the measurement of
the dependent variable alrd from the variability between studies in the research method,
research participants, or other factors. The random-effects model is equivalent to the
fixed-effects model when the between-study variability is negligible. When there is
significant between-study variability and the randorn-effects model is used, the variance
around the surnrnary effect size is larger resulting in a larger confidence interval. The
random-effects model, therefore, is more conservative than the fixed-effects model (1 09).
Effect-size Estimate
The next step in the meta-andytic procedure is to determine which effect-size
estimator is to be used. This choice depends on the measurement of the independent and
dependent variables in the individuai studies. The most commonly used effect-size
estirnators include, Pearson's correlation coefficient (r), Odds Ratio (OR), Hedges ' g, and
Cohen's d. The effect-size estimator r is used when the research involves continuous
independent and dependent variables, e.g., examining the relationship between age and
emotional distress. OR is used when the independent and dependent variables are
categorical, e.g., the comparison of smokers versus non-smokers in the development of
lung cancer. The effect-size estimators Hedges' g and Cohen's d are similar in that both
correspond to categorical independent and continuous dependent variables. Hedges ' g is
used when there is a clearly defined no-treatment control group, e.g., a randomized
controlled trial of a new pharmaceutical agent being compared to a placebo. Cohen's d is
used when there is no clear control group, e.g., the comparison of renal transplant
recipients with in-centre haemodialysis patients regarding emotional distress. The
methodological difference between Hedges' g and Cohen's d i s that the effect-size
cakulation uses the standard deviation of the control group for Hedges' g and a pooled
standard deviation (a composite from both treatment groups) for Cohen's d (1 00).
Data Extraction Process
The data extraction process involves recording relevant dependent and
independent variable information, evaluating the methodological rigour of the study, and
evaluating the reliability of the data extraction process. A standardized data extraction
protocol is used to obtain the information necessary to describe the studies and calculate
effect sizes for each of the published articles. Information recorded from the studies
includes: the full bibliographie citation, descriptive information regarding the treatment
groups, and information necessary to calculate the dependent variable effect sizes (see
- .
effect-size calcuIation, below). The fiil1 bibliographic citation includes the names of the
authors, the title of the article, and the fùll journal citation. Descriptive information
regarding the treatment groups is recorded, e.g., sample sizes; sociodemographic
information for the research participants, such as age, gender, education, employment
status, and marital status; and indicators of physical status, such as history of transplant
failure, diabetic status, serum indices, and CO-morbid conditions. Information necessary to
caIcuIate effect-size estimates for the indicators of the dependent variables must be
recorded (see effect-size calculation, below). For research articles not providing al1 of the
information necessary to calculate effect sizes for al1 of the variables of interest, the
authors can be contacted in writing to obtain the missing information (although this tactic
is not always possible or effective).
To evaluate the methodological rigour of the studies, widely shared research
reporting criteria of the type typically employed by journal editors and reviewers can be
used (5,39,118). The following features can be examined: a) statement of a research
question; b) directional hypotheses; c) statement of inclusion/exclusion criteria for
research participants; d) provision of sampling controls; e) use of established measurement
instruments; f) statistical analyses test the hypotheses as specified; g) strategy for treating
missing data; h) evaluation of case-mix differences between groups; i) statistical or . .
sampling control of case-mix differences; j) conclusions correspond closely to the
statistical results; and k) interpretation of results qualified by limitations of research. In
the present thesis, these features will be combined to form a checklist to provide an overall
indicator of rnethodological rigour. Other methodological features recorded can include
the type of research design, response rate of treatment groups, and the citation-index
impact score for the journal in which a report appears. The citation-index impact score
can be interpreted as a proxy indicator of the rigour and prestige of the journal. The
impact score represents the average annual number of citations for articles fiom the
journal. With caution, this impact score can be used as one indicator of the quality of the
research (108). Caution should be applied because the index score represents the average
citation rate for articles appearing in the journal for the entire volume-year. The impact
score is thus representative of the entire set or articles published in a given year but will
either over- or underestimate the citation fiequency of individual contributions.
Inter-rater reliability is used to evaluate the precision with which the data-
extraction fonn can be used and to ensure the objectivity of the data-extraction process.
Conducting independent analyses of 20% of the retrieved studies to examine inter-rater
reliability is common for meta-analyses when the sarnple size is approximately 100 studies
(123). For meta-analyses, in which the reviewer expects to find fewer than 50 studies,
two individuah might independently evaluate 15 randomly selected studies from the pool
to be included in the meta-analysis. This number of studies will ensure that there would be
an adequate number of studies to assess reliability (i.e., 20% of 50 studies would result in
only 10 studies which may be insufficient for estimating inter-rater reliability). For any
areas of disagreement between raters, the issue is discussed to help dari@ the extraction
process. To evaluate the methodological rigour section of the data-extraction process for
categorical data, a Kappa statistic between .4 and .75 indicates good reproducibility and
.75 or greater is considered excellent (63). To evaluate the reliability of the rernainder of
the information, percentage agreement is used (1 15). These standards will be applied in
the meta-analysis to be reported in the present thesis.
To compare treatment groups assessed by continuous dependent variables, e.g.,
RRT comparisons of emotional distress, the most appropriate effect-size estimator used to
indicate the standardized difference between treatment groups is Cohen's 6 (1 00).
Standardized formulas are available to calculate effect sizes (48'99,100). "Unadjusted"
and "adjusted" effect sizes can be calculated. Adjusted effect sizes control for the
potential overestimation of effect-size estirnates when sample sizes are small(100). Since
al1 of the studies do not report information in the same manner the following methods can
be used to calculate the effect size (Cohen's 4: a) the standardized mean difference
method (requiring means, standard deviations, and group sizes for each treatment group);
b) Cohen's d for categorical data (requiring the number of individuals in each treatment
group in each category of the variable); c) significance tests (requiring t or F statistics
[with 1 degree of freedom] and the sample size); and d) significance levels (requiring exact
p values and the sample size). In instances where significance tests or leveIs are reported
for repeated-measures or equivalent-group designs, the mean difference method will be
used to avoid the overestimation of the effect size (32). Effect-size estimates can be
approxirnated when studies report only non-specific results, i.e., "p < .OS ' or "no
significant differences". The standard formula, fiom which al1 other formulas are derived,
involves the subtraction of the second group's mean fiom the first group's mean, al1
divided by a pooled standard deviation (combination of the standard deviations of each
treatment group weighted by their sample sizes). A positive effect size indicates that the
first treatment group in the equation scored higher than the second group, a negative score
indicates that the second group scored higher than the first. To maintain consistency, the
order of the treatment goups should be held constant in calculating effect-size estimates,
e g , the mean score for the in-centre haemodialysis treatment group will always be
subtracted fiom that of t he rend transplantation treatment group.
Mean (Summary) Effect-Size Estimates
Rosenthal(99) suggested that mean effect sizes can be calculated for dependent
variables to compare the treatment groups when there are two or more studies comparing
the particular treatment groups but suggested a larger number to make the results more
meaningfùl (e.g., five studies). Each study is allowed to contribute only one effect size for
each dependent variable. Therefore, for studies using more than one measure of the
dependent variable, the effect sizes cm be averaged. This method of combining effect
sizes within a study yields a precise combined estimate when the two measures are
perfectly correlated but underestirnates the estimate when they are not. Therefore,
averaging multiple effect sizes to compute a single value for the dependent variable
underestimates the effect size but is more practical with the information provided in the
published articles (1 04). A more precise method for combining effect-size estimates
requires the correiation between the measures of the dependent variable (41) but this
infonnation is rarely provided in published studies.
Confidence intervals are calculated to evaluate whether the observed treatment
difference is significantly different fiom zero, Le., the nul1 hypothesis of no difference
between treatment groups. If the 95% confidence interval for the summary effect size
does not contain zero, then the observed treatment difference is considered reliably
different from zero, i.e., a statistically significant treatment difference is inferred to exist.
Under the fixed-effects model, tests of hornogeneity are conducted to evaluate the
normality of the distribution of the study effect sizes around the "true" or population
value. This test evaluates the hypothesis that the individual effect sizes are equal to the
underlying population parameter. The test statistic, Q, is used to evaluate hornogeneity.
Its significance is evaluated using the chi-square distribution with k-1 degrees of fieedom
where k is the number of studies. If heterogeneity exists, study characteristics (e.g., the
year of publication, type of research design, case-mix differences, and rnethodological
rigour) or other factors can be explored as potential sources of heterogeneity. This
procedure is not necessary when using the random-effects model as between-study
variability is factored into the individual variances of the effect sizes.
Sensitivity Analyses
Sensitivity analyses (also referred to as sub-group analyses) can be conducted to
investigate variability between effect sizes andor to examine the relationship between
independent variables of interest and the dependent variables. Procedures are available to
conduct sensitivity analyses for the fixed- and random-effects models but automated
standardized procedures using conventional statistical computer packages (e.g., SPSS) are
only available for the fixed-effects model (50,99). To determine if the type of research
design is related to the heterogeneity, a weighted anaiysis of variance is used where the
effect size is weighted (multiplied) by the inverse of its variance and the grouping variable
is the type of research design. Weighted anaIysis of variance is used because conventional
statistical procedures rely on parametric assumptions about the data that are not satisfied
for effect-size data (e.g., equal error components). In meta-analysis, individual studies
often have different sample sizes and, therefore, effect-size estimates will have different
error variances (50). To determine if methodological rigour is related to the magnitude of
treatment effects, correlations between methodological rigour and dependent-variable
effect sizes can be calculated. Where significant relationships exist, corrected effect sizes
can be calculated using the regression equation (1 15). For example, if a significant
relationship is found between methodological ngour and the dependent variable, a
regression procedure can be used to calculate "corrected" effect-size estimates that
control for differences across studies in methodological rigour. To evaluate the impact of
case-mix differences on the dependent variable, case-mix variables can be coded as to
whether or not the treatment groups differ with respect to each. Weighted analysis of
variance can again be used to evaluate whether effect-size parameters differ between
studies in which the treatment groups did or did not differ with respect to the case-mix
variable. If a significant interaction is obtained, surnmary effect sizes and homogeneity
tests should be calculated for each of the case-mix variable categories.
Publication Bias
Publication bias is a threat to the external validity of meta-anaiytic results when the
analysis relies exclusively on published studies. It concerns the fact that statistically
significant research results are more likely to be published than statistically nonsignificant
results (44). Including only published research in a rneta-analysis may, therefore, inflate
estimated treatrnent differences. To evaluate whether meta-analysis results are threatened
by publication bias, Rosenthal(97) developed a procedure that estimates the number of
unpublished studies averaging nonsignificant results that, if included in the meta-analysis,
would make the sumrnary effect size nonsignificant (Le., p = .05). This number of
unpublished studies is referred to as the "Fail-safe number." It is calculated by dividing the
square of the sum of the z scores for each effect size (i.e., effect size divided by its
standard error) by the square of the critical z score for the desired significance levet (e.g.,
z =1.96 forp = .OS) and then subtracting the number of studies. To evaluate the strength
of the 'Yail-safe number3' or the confidence that the results would not be threatened by
publication bias, Rosenthal developed a "tolerance level" -- the number of unpublished
studies that one might reasonably expect. The "tolerance level" is equal to five times the
number of studies included in the meta-analysis plus 10. If the fail-safe number is Iarger
than the tolerance level, the meta-analysis results are unlikely to be threatened by
publication bias. In general, a maIl summary effect size, e.g., Cohen's d < .20, based
upon a small(< 10) heterogeneous sample of studies is likely to be threatened by
publication bias.
Interpretation of the Results
lnterpretation of the results by percentile rank for standardized mean-c
effect-size parameters is similar to the binomial effect size display used for correlational
effect-size parameters, i.e., r (1 03). Using the normal distribution, the percentile rank can
indicate how a person scoring at the median in one treatment group would rank in the
other treatment group for the particular variable (1 15). To determine the percentile rank
for a positive effect-size estimate, the estimate is located in the first column of the normal
. a distribution table and the corresponding number in the second column is transfomed into
the percentile rank. When the effect-size estimate is negative, the value in the third
column is transformed into the percentile rank. For example, a effect size of .60
comparing renal transplant recipients to in-centre haemodialysis patients on psychological
well-being would indicate that a person scoring at the median (i.e., the 50th percentile)
within the transplant group would report a level of psychological well-being higher than
that reported by 73% of the individuds in the haemodialysis group (i.e., the median
percentile score in the transplant group equals the score ranked at the 73rd percentile in
the haemodialysis group).
Summary
ESRD and its treatment by transplantation or dialysis imposes differing lifestyle
disniptions necessitating different degrees of adaptation by the individuals affected.
Treatment modalities introducing substantial tifestyle disruptions may negatively impact on
emotional well-being. There are also economic implications regarding treatment modality
selection because of the differing costs associated with each treatment. Unofficial publicly
fùnded health care policy appears to prefer that patients utilize the least expensive
treatment modalities but does not address the possibility of differential quality of life
impact. Many researchers have examined the differential impact of RRTs on patient
quality of life. The research is scattered throughout the published literature in medical,
social science, and nursing journals. Randomized controlled studies are impractical in this
field because physical and social criteria are involved in treatment allocation (e.g.,
haplotype matching for kidney transplantation). As described above, research exarnining
this issue tends to use one of three different naturalistic designs, independent-groups,
prospective repeated-measures, or equivalent-groups designs. The non-random assignment
of patients to treatments results in non-equivalent treatment groups available for
comparative studies. Additionally, the research differs with respect to methodological
rigour (as described previously). Other methodological weaknesses of research in this
area include difEerential assessrnent of, and control for, case-rnix differences between
groups and measurement instruments of unknown psychometric adequacy. The result is a
literature with inconsistent and, sometimes, conflicting results. The preferred method of
evaluating and surnrnarizing this literature is to conduct a meta-analysis comparing quality
of life across renal replacement therapies. A meta-analysis has advantages over traditional
qualitative reviews in that the procedure is comprehensive, representative, and
quantitatively precise. It allows an evaiuation of how much case-mix and other validity
threats (e.g., methodological rigour) may affect the research findings. The meta-analysis
to be reported in the present thesis will examine differences in quality of life across
treatrnent modalities for ESRD by evaluating the extent to which these may be accounted
for by: a) differential effects of the treatrnent modalities thernselves; b) case-mix
differences between treatment groups; and c) differences in methodological rigour across
studies.
Chapter 2
Cornparison of QuaIity of Life Across Renal Replacement Therapies:
A Meta-Anaiysis
End-stage renal disease (ESRD) is an irreversible life-threatening condition that
requires renal replacement therapy (MT) to maintain life. There is disagreement in the
field of Nephrology as to which of the available RRTs, transplantation or dialysis, affords
patients the best quality of life. Generally, transplantation is believed to be the best
treatrnent as it allows the patient to return to a lifestyle that more closely resembles his or
her pre-renal failure situation. In contrast, because dialysis requires many lifestyle changes
associated with ongoing treatment it is thought that the quality of life it affords is poorer.
Differences in quality of life across RRTs have been studied by many researchers.
Although there is growing consensus that transplantation is associated with a better
quality of life, the research findings remain inconsistent and it remains unclear whether this
superiority is due to the nature of the treatment or pre-existing non-treatment differences
between groups.
The treatment modalities for ESRD, including renal transplantation, in-centre
haemodialysis, CAPD, and home haemodialysis, have different characteristics, including
where they are conducted, duration and fkequency of treatment, dietary restrictions, and
medication requirements. These result in differing degrees of lifestyle disruption. For
example, in-centre haemodialysis is associated with severe dietary constraints (1 O),
intederence in social and vocational activities (1 l,27), and poor physical health (36).
Conversely, renal transplant recipients do not experience this leveI of lifestyle disruption as
their primary treatment afier transplantation involves taking immunosuppressive
medications and maintaining an adequate dietary intake (85). Due to the differing degree
of lifestyle disruptions between RRTs, they rnay differentially impact on quality of life
(27'28).
Ouality of Life
The construct of quality of life is cornplex, consisting of objective and subject
indicators. Traditionally, quality of life was evaluated using economic and social
indicators including, income, housing, education, crime, and employment (3). Researchers
observed that as the circumstances of the population improved, e.g., cleaner water, better
housing, and fewer families below the poverty line, the degree of life satisfaction and
happiness reported by the population decreased (17). To clarifjr this discrepancy,
researchers became interested in further examining subjective indicators of quality of life.
This subjective approach is concerned with the individual's sense of well-being, including
their cognitive evaluation of life and general affect or happiness (13,17). One of the
advocates of this approach, Bradburn (1 3), proposed that subjective well-being can be
separated into two components: positive and negative affect. His research demonstrated
that positive and negative affect are separate and unique components of subjective well-
being as he identified non-overlapping sets of variables that related differently to positive
and negative affect. For example, gender differences are evident in the reporting of
negative affect but not for positive affect.
Much of the quality of life research in ESRD has focused on subjective indicators
of quality of life, specifically psychological well-being and emotional distress.
Psychological well-being, similar to Deiner's (3 1) and Campbell's (1 7) definitions of
subjective well-being, usually encompasses an evaluation of positive emotions, rnood or
affect, such as happiness, and the cognitive appraisal of life, as in life satisfaction.
Psychological well-being is not merely the absence of emotional distress. Commonly used
instruments that measure this concept include the Index of Well-being (1 8), the Bradbum
Affect Balance - Positive Af5ect subscale (13), the Profile of Mood States - Vigour
subscale (69), and the Psychosocial Adjustment to Illness Scale (74).
Studies of emotional distress usually encompass negative mood or affect, such as
unhappiness, and negative psychologicai States, such as depressive syrnptoms and anxiety.
Emotional distress does not require a clinical diagnosis of depression, anxiety, or mood
disorder and is not merely the absence of psychological well-being. Comrnonly used
measurement instruments include the Beck Depression Inventory (8), the Bradburn AfEect
Balance - Negative Mect subscale (13), and the Center for Epidemiologic Studies
Depression Scale (87). Psychological well-being and emotional distress are distinct fiom
personality characteristics, such as self-esteem and coping skills.
Generic and disease-specific quality-of-life measurement instruments are used in
studies evaluating the impact of chronic illness. Generic instruments are general measures
of quality of life which evaluate many aspects of life that are ofien affected by different
illnesses. Therefore, they are applicable to many different illness populations. Disease-
specific instruments assess experiences unique to an illness and often evaluate the impact
of the illness on daily functioning. Their main advantage is that they are more specific in
their evaluation of the impact that a particular condition cm have on quality of life (57).
46
Their main limitation is that they ofien contain items assessing rnedical or physical
syrnptoms of the disease which are not intrinsic to subjective quality of life One approach
to measurement may be more beneficial than the other depending on the purpose of the
research. For example, if the purpose of the research is to compare the quality of life of
two different illness populations, then a generic measure would be more appropriate.
Alternatively, if the purpose of the research is to compare the quality of life of individuals
with the same physical condition who are receiving different treatments, then a disease-
specific instrument may be more sensitive to subtle differences between the groups. Some
researchers advocate the use of generic and disease specific quality of life measurement
instruments in combination, thereby assessing general fiinctioning and the specific impact
of the illness (57). For the purpose of this meta-analysis, generic and disease specific
measurement instruments will be included.
Limitations of Current Research Literature
The research examining differences in quality of life across RRTs is characterized
by many limitations that threaten the validity of findings. The two main limitations include
poor assessrnent of and control for case-mix differences (e.g., differences in general non-
renal heaith) between treatment groups as an alternative explmation for research findings
and varying degrees of methodological rigour across studies. Individuals are typically
preferentially selected to receive one of the available RRTs (e.g., people who receive a
renal transplant are usually younger, male, white, and in better physical health than
individuals who receive in-centre haemodialysis (5 139,116). Therefore, the treatment
groups may differ on non-treatment related variables (i.e., case-mîx), such as
sociodemographics and physical status, which are related to quality of life
(4,27,36,46,53,78,8 l,84,124). Failure to rule out non-treatment differences as
explanations for observed quality of life differences between treatment groups threatens
the interna1 validity of the experiment. Because it is not possibIe to assign individuals at
random to alternative RRTs, this limitation is common in ESRD quality of life research.
Another limitation is that studies Vary widely in methodological rigour.
Methodological rigour concerns technical features of a study that affect either the intemal
or external validity of experimental results and their interpretability. Lipsey and Wilson
(66) found that the degree of methodological rigour, including the type of research design,
research participant selection procedures, instrumentation, external validity, and statistical
conclusion validity, resulted in both over- and under-estimation of treatment effects
depending upon the field of research.
Thorouph Review of the Literature
A thorough review of the literature can help to clarif) the differences in qudity of
life findings across RRTs. Traditional narrative reviews ( eg , (24)) have been published
but due to the difficuIty in integrating a large number of studies ofien a select or non-
representative sarnple of published articles are included, potentially reflecting the bias of
the reviewer (1 15). Additionally, narrative reviews rely on significance tests to summarize
treatment group differences and, therefore, cannot comment on the magnitude of
treatment differences. Meta-analysis is a form of literature review that allows a
comprehensive and quantitative synthesis of the research. It is comprehensive as an
attempt is made to include al1 research examining the question of interest. Effect-size
. -
estimates are used to indicate the magnitude of the difference between the treatment
groups regarding the dependent variable. Additionally, meta-analysis provides the
opportunity to investigate the relationship between other variables or study characteristics
and the magnitude of the treatrnent difference, for exarnple, examining the impact of
research design, methodological rigour, or case-rnix differences.
Research Ouestion
A rneta-analysis was conducted to examine differences in quality of life across
treatment modalities for ESRD. It specifically addressed the following research question:
1s there a difference in quality of life across the treatment modalities for ESRD and to
what extent are these differences explained by: a) differences across the treatment
modalities (e.g., increased fieedom and decreased iiiness intnisiveness following successfùl
renal transplantation as compared to maintenance dialysis); b) case-mix differences across
treatment groups; and c) differences in methodological rigour across studies.
Method
Study Identification
Multiple sources were consulted to identify as much of the published literature as
possible conceming differences in quality of life in ESRD across RRTs. The following
databases were searched using an extensive list of key words (e.g., renal, kidney, renal
replacement therapy, emotion, psychology, depression, and satisfaction): Medline,
Psychinfo, Cinahl, and the reference database of an expert in the fieldaThe following
databases were searched using an extensive list of key words (e.g., renal, kidney, renal
replacement therapy, emotion, psychology, depression, and satisfaction): Medline,
4 Y
Psychinfo, Cinahl, and the reference database of an expert in the field. The keywords
were combined into two categories: a) concerning ESRD (e.g., kidney, renal, rend
replacement therapy, etc.) and b) concerning quality of life (e.g., emotion, happiness,
satisfaction, depression, anxiety, etc.). The results of these two search combinations were
combined so that the final pool of studies contained an element of the first and second set
of key words. The reference lists of retrieved and review articles were examined for
additional publications not identified by the search strategy. This synthesis concentrated
on published research because unpublished research is more difficult to identify and
retrieve systematically .
inclusion/Exclusion Criteria
Studies were included in the analysis if they met the following criteria: a) they
reported at least one quantitative cornparison between at least two modes of renal
replacement therapy involving psychological well-being, emotional distress, or "quality of
Me" in general (this term was included as it often contains psychological well-being and
emotional distress as subsets of the instrument); b) used one of the following research
designs: prospective repeated-measures, equivalent, or independent-group design; c) the
research examined adults (1 8 years or older); and d) the studies or their authors (when
contacted subsequently by the present author) provided the information necessary to
calculate directly or to estimate effect size (s). Studies were excluded if they met one or
more of the following criteria: a) the research participants were asked retrospectively to
compare their life on their current treatment to that of their previous treatment (Le., how
does your life with a transplant compare to your life on dialysis before transplantation?); b)
treatments other than conventional RRTs were evaluated, such as drug trials or multi-
organ transplantation; and c) the quality of life rating was made by someone other than the
patient (e.g., nurse or family member).
In meta-analysis, research participants may only be included in the analysis once
for each dependent variable. Therefore, the independence of research participants was
assessed. Articles with sirnilar or overlapping authors were examined to determine if the
same group of subjects was used in more than one publication by examining when, where,
and how many subjects were obtained. If more than one published article used the same
group of subjects, the articles were counted as one study and al1 of the publications in the
series were used to obtain the required information. For research articles that did not
provide al1 of the information necessary to calculate effect sizes for the variables of
interest, the authors were contacted in writing to request the missing information.
Data Extraction
A standardited data extraction protocol obtained the information necessary to
describe the studies and calculate effect sizes (see effect-size calculation below) fiom each
of the published articles. Inter-rater reliability evaluated the precision of the data
extraction form. Conducting independent analyses of 20% of the retrieved studies to
examine inter-rater reliability is cornrnon for meta-analyses of approximately 100 studies
(123). For this meta-analysis, approximately 50 studies were expected. Two individuals
independently evaluated 15 randornly selected studies fiom those selected for the meta-
analysis. This number of studies was selected to ensure that there would be an adequate
number to assess reliability (i.e., 20% of 50 studies would result in only IO studies which
3 1
may not be sufficient). Whenever disagreement arose between raters, the issue was
discussed to help dari@ the extraction process. For the categorical methodological rigour
evaluation, a Kappa coefficient between .40 and .75 was considered good reproducibility
and .75 or greater was excellent (63). For the remainder of the data extraction,
percentage agreement was used (1 15).
Meta-Analytic Mode1
The fixed-effects model was the pnmary mode1 adopted as it was assumed that
treatment group differences with respect to emotional distress and psychological well-
being are relatively uniform in the population of individuais with ESRD, e.g., renal
transplant recipients are generally believed to be happier than in-centre haemodialysis
patients. Additionally, automated standardized procedures using conventional statistical
compter packages ( e g , SPSS) are available for the fixed-effects model to evaluate
sources of heterogeneity if they exist (50). It was acknowledged that study characteristics
would not be fixed or held constant in the included studies but that study characteristics
would be exarnined as sources of effect sue heterogeneity. The more conservative
random-effects model would be used to surnmarize the results if the source of
heterogeneity was not determined.
Effect-size Calculation
Effect-size estimates, objective indicators of the magnitude of the difference
between treatment groups, were calculated using standardized formulas (48,99,100).
Cohen's d was used to indicate the standardized mean difference between treatment
groups. Unadjusted and adjusted effect sizes were calculated. Adjusted effect sizes
control for the overestimation of effect sizes when sample sizes are small(100).
Because information is not reported unifody across studies, the following
methods were used to calculate effect sizes: a) the standardized mean difference method
(requiring means, standard deviations, and group sizes for each treatment modality); b)
Cohen's d for categorical data (requiring the number of individuals in each treatment
group in each category of the variable); c) significance tests (requiring t or F statistics and
degrees of fieedom); and d) significance levels (requiring exact p and N values). For
repeated-measures and equivalent-group designs, significance tests or levels were not used
to avoid serious overestimation of the effect size (32). When al1 of the information
necessary to calculate individual effect sizes was not available, they were estimated using
methods described by Ray (90). For studies reporting non-specific significance levels, the
approximate t statistic was used. An effect size of zero was assigned when studies
reported that treatment groups did not differ significantly but did not report more precise
results. Studies were excluded fiom the analysis if none of the above approaches was
possible (13 studies were excluded fiom the present analysis). Each study can contribute
only one effect size for each dependent variable. Therefore, when a study used more than
one measure of the dependent variable, the effect sizes were averaged (104). This method
of combining effect sizes may underestirnate the effect size but is more practical with the
information provided in the published articles.
Statistical Analyses
The following statistical analyses were conducted for each of the treatment
comparisons. First, a summary eRect size was calculated for each dependent variable for
which there were 5 or more studies comparing particular treatment modalities (99). To
determine if the sumrnary efEect size was significantly different fiom zero, i.e., no
treatment difference, the 95% confidence interval was calculated. If the interval did not
include zero, the treatment cornparison was considered to be statistically significant.
Second, the fixed-effects mode1 test of homogeneity was conducted to assess the extent to
which the sample of studies shared a common population effect size, Le., were nonnally
distributed. To evaluate the threat of publication bias, Rosentha13s (97) fail-safe number
and tolerance levels were calculated.
To examine sources of heterogeneity the following sensitivity analyses were
conducted. First, weighted analysis of variance (effect size weighted by the inverse of its
variance) and correlations examined whether the type of research design, total sample size,
year of publication, and method of effect-size estimation were related to the magnitude of
the treatment effect. These research characteristics are comrnonly examined as sources of
heterogeneity and were specified a priori for the present study (49).
Second, the relationship was exarnined between case-mix differences and degree of
methodological rigour (assessed by 1 1 These research characteristics are comrnonly
exarnined as sources of heterogeneity and were specified a priori for the present study
(49). criteria developed for this meta-analysis plus the citation index impact score) with
the magnitude of treatment differences. To examine case-mix differences, sensitivity
analyses involved categorizing the studies as to whether the treatment groups differed with
respect to each of the case-mix variables. Weighted analyses of variance evaluated
whether effect-size estimates differed across studies that did and did not differ with
respect to the case-mix variable. Degree of methodological rigour was examined using
correlations.
The magnitude of the effect size was interpreted using percentile ranks and
cornparison with clinically relevant research effect-size estimates. Using the normal
distribution, the percentile rank can indicate how highly a person scoring at the median in
one treatment group would rank in the other treatment group for the particular variable
(1 15).
Results
Identification of Published Reports
The literature search strategy identified 3265 studies. This author examined the
titles and abstracts of these references to identiQ studies that met the inclusion/exclusion
criteria. Fifty-nine (1.8%) published articles were retrieved to be included in the meta-
analysis. See Table 1 for a description of this research, including the authors and year of
publication, the type of research design used, and the rneasurement instruments used to
evaluate the dependent variables. The other studies identified by the search strategy were
discarded for the following reasons: a) 276 l(84.6%) were irrelevant, i.e., did not evaluate
quality of life; b) 95 (2.9%) were discussion articles, i.e., review articles, editorials,
comments, letters, or abstracts; c) 6 1 (1.9%) were dmg studies, e.g., erythropoietin or
cyclosporin; d) 25 (0.8%) examined children; e) 25 (0.8%) concerned s u ~ v a l or physical
symptoms; f ) 190 (5.8%) were non-comparative, i.e., quality of iife was examined but
treatment groups were not compared; g) 13 (0.4%) were non-independent, i.e., the results
were published in another article; h) 36 (1.1%) were excluded for other reasons, including
retrospective approach.
For studies not providing al1 of the information necessary to caiculate effect-size
estimates, the authors were contacted in writing and asked to provide any or al1 of the
missing information. Of the 44% that responded, 41% (or 18% overail) were able to
provide additional information and 18% (or 8% overall) indicated that additional
information would be sent but no additional information has been received to date. After
the authors were contacted, 13 additional studies were excluded fiom the meta-analysis
because the information necessary to calculate effect-size estirnates for the dependent
variables was not included in the original studies and was not provided by the authors
when contacted. These studies are identified in Table I by an asterisk (*) and were
excluded fiom the analysis for the following reasons. Four studies were excluded because
quality of life in general was assessed without providing information regarding the
psychological well-being or ernotional distress subscales (3 5,8 lY105,lO6). An additional
four studies were excluded because information necessary to calculate effect sizes was not
provided (61,64,73,78). One study was excluded because the sample sizes for the
treatment groups were not evident (65). Four additional studies were excluded because
the only information provided was: a) a paired t-test (1 24); b) a repeated-measures p value
(82); and c) F-tests for more than two groups (14,82). One additional study was excluded
fiom the analyses because the treatments compared were in-centre haemodialysis and self-
I ame I . aummary aoie or micies inciuaea in ivlera-maiysls.
Dependent Variables Study
Cmtril Lifc Satisfaction Life Satisfaction
Auer. J., 1990 IDG I I CN>D=8 1 1 D=78
Rescarch Design
1 1 1 LXc Happiness
Participants
Campbell Well-being - general affect and life satisfaction Spitzer subjective quality of life Affect scale (developed for study)
Bihl. M.A., I Malchai I CAPD= 1 8 I Overail Quality Of Life 1988 CHn= 18 - PsychologiciiVspiritual
Bonncy, S., 1978
IDG Kupfer-Detre Systern - psychological status (distress)
Rradburn M e c t Balance - positive and negative scales Campbell Well-being - general affect and lire satisfaction
Brerner. B.A., 1 IDG
Bremer, B.A., 1995
Rradburn Affèct Balauce - positive and negativr: scales Campbell Well-being - general affect and life satisfaction
Cliristemen, A.J., 1994
Bcck Ikpression Inventory 1 Cognitive Depression Inveutory
l'une 'f rade 0 i T Spitzer Quality Of Lifc Indes
Churchill, D.N., 1987
Devins, G.M., 198 1
Dc-Nour,A.K., IIIG I I RT=11 1980 IID=20
IDG
P~ycliological condition
Rcck Dcprcssion Inveritory
Devins. G.M., 1983-84
Composite afïect scorcs - positive and negritivc
1,ik salisf'action Positive a l k t I4opelcssuess
Participants 1 Dependent Variables
- Design
Eicliel, C1 .J., 1986
IDG CAPD = 30 1XD=35
Haldrec Stress Scalc - psychosocial subscale
* Esmatjes, 1994
RT= 1 0 Spitzer Quality of Life HD= 1 O
- - -
Evans. R.W.. 1985
Campbell Well-bciug - general affect - life satisfaction
Gala. C., 1990 IDG CAPD = 15 CM)= 13 M D = II
Zung Depression Scale Zung hxiety Scale
Glass. C.A.. 1987
Depression Anxieîy
W m . K.W., 1994
IDG Beck Depression Inventory State Trait Anxiety Inventory PANAS - positive and negative mood
Gudex, C .M.. 1995
IDG Overall Disîress - depression - anxrieîy
Iiaq, I., 199 1 Matched RT = 20 I-II) = 20
MMPI - Amiety and Depression
-- -
Prospective 1-JD to RT = 7 Sichess Impact Profile - psycholagical subscrile
CAPD= 14 Hamilton Depression HD= 17 Standar&.cd Psychiatrie Interview
I Iathaway, D.K.. 1994
Iordanidis, P., 1993
IDG
Johnson, J.P., 1982
Bradbuni M e c t Balance - positive and uegative Campbell Well-being - life satisfaction and geucral &ççt
Kalman, T.P., 1983
General Hcalth Questionriaire Hopelcssness
Ceueral Quality of Life - ovcrall satisfaction Depressed inood
Depression Ansieiy
Koch, U. 1990
a) IDG b) Prospc~ tive
I uepcnaenr v anams Design
* Laupaçis, A.. 1996
Prospective KTQ - emotional KDQ - depression Sichess Impact Profile - emotioiial. psychosocial The-trade-off
* Liudsay, R.M. 1982
Livesley. W.J.. 198 1
IDG - -
MI-IQ - depression - amiety
Lok, P., 1996 CAPD = 8 HU = 5G
Quality of Lik Index (Me satisfaction)
5 Amiety Measures Lucas. RA.. 1974
Mahdavi. R. 1995
Prospective Psychological 1 Iealth
CAPD= 16 Profile of M d States - TMD CFID=l6 - depression
- vigour
Maida, C.A., 1991
Matched
* Meers, C., 1996
Matched SF-36 - Emotional Well-being
Molzahn, A.E., 1996
IDG RT = 96 CAPD = 30 CHTI = 52 1-IHD = 37
Campbell index of well-being Canûil Lifc Satisfaction
* Moreno, F.. 1996
PD-41 Sickness Impact Profile - psychosocial HD = 891
Morris. P.L.P., 1988
IDG Cieneral Health Qucstiomaire - Psychological distrcss Life Satisfaction
* Oldenburg, I l . , 1988
IDG Psychological Adjustuieut to Illncss Sçale - psychological fùnctioniug SCL-90
I'arfrcy, P.S., 1989
Campbell kvell-bcing - gencral affect and life satisfaçtioti Spitzer - subjective Quality of Lifc AEcct scale (devcloped for study)
Spitzer Quality of Life - emotional subscale
* Park, II., 1992
IDG
B ai r i r ipa i i ra
-
Design
* Park, 1.11.. a) IDG b) Prospwtive
Psychological Adjustment to Ilhcss Scale, Reck Depression Inventory, Statc Trait Ansiety Inventoxy, Sichcss Impact Profile. SCL-90R
Depression Ansiety
Geueral liealth Questionnaire Mental Healtii Lndex - overaIl well-being - psychological distrcss
SCL-90R - Depression, Ansiety, and Positive emotions
Procci, W.R. Matched 1980
RT= 16 Reliavioural Impairment (psychological)
RT=9 IPAT Anxiety Test Rabinowitz, S., II>G 1980
Rodin, G., 1985-86
Prospective (IDG also available)
Sickness Impact Profile - psychological subscale 13wk Depression Invcntory
* Rosenbaurn, ID<; E.A., 1985
CAPD=46 Quality of IAe C m 2 0 8
* Russell, J.D., Prospective 1992
Sacks, CR., 1990 1 Bcck Depression Inventory / Cognitive
Deprcssion Invtmtory
Psychological Adjustment to Illncss Scale - total - psychologicol disbess Brief symptom inventory - total, depression, amicty -. .. . .
Lifc Satisfaction Life Iiapphess Dcprcssion
Sïcdat. Y.K., 1 IDG
l.ID= 177 Dcprcssion and Amicty ? RT = 141
RT=9 1 Bradbum Happiucss CAPD=5 1 0 Campbell Weil-being C F P 8 3 1 1-Iaupiness Scalc (Sinmons)
1 Design 1 a) Matched a) CI ID =29, b) Matched I-ltW29
b) CN>D=34 CI .D34
Tome, W.S., D G I C D 3 2 1 978 HHD= 1 I
CAPD=22 ~;;~er"_M.. 1 IDG 1 f@29
* Wolcott, D.L.. Matclied CA-33
Zchrer, CL., I Prospective EID to RT = 16 1994 1
Psychological Adjustrnent to Illness Scale - psychologiçal distress Brief Syrnptom Inventory - deprcssion, anxiety
MMPI - 13 scales
Profile of Mood States- anxiety, dçpression, vigour, total mood disturbance Quality of Life (O- 10) scale
SF-36 Mental Health
I 1 m
IDG = Independent Group Design; RT = Renal Transplantation; CHI> = In-centre Haemodialysis; CAPD = Continuous Ambulatory Peritoneal Dialysis; HHD = Home Haemodialysis; HD = Haernodialysis (sub-type not specified). * indicates studies that were not included in the analyses because, due to missing information, effect size estimates were not calculated directly or estimated.
- -
care haemodialysis and there were too few of these comparisons to summarize (70). In
the end, there were 76 treatment comparisons of emotional distress and 65 of
psychological well-being.
The results of the thirteen excluded studies can be qualitatively summarized.
Esmatjes (35) compared renal transplant recipients with in-centre haemodialysis patients
and did not observe a significant difference in quality of life. Park (8 1) reported
significantly higher quality of life in the renal transplant recipients compared to in-centre
haemodialysis patients. Russell ( 106) evaluated quality of life prior to and afier receiving
a renal transplant and observed a significant improvement afier transplantation. Laupacis
(64) reported a significant decrease in emotional distress in a sub-set of individuals who
had received rend transplantation. Reporting median scores, Kutner (6 1) observed higher
levels of depression in renal transplant and in-centre haemodialysis patients compared with
home haemodialysis patients. Park (82) compared rend transplant recipients and in-centre
haemodialysis patients regarding anxiety and depression using independent group and
prospective repeated-measures designs. The independent group design revealed
significant differences across renal transplant, in-centre haemodialysis, and normal control
groups. The prospective design revealed significant decreases in depression and anxiety
after transplantation. Rosenbaum (1 OS) reported no significant difference between CAPD
and in-centre haemodialysis patients in general quality of life. M e r controlling for
significant case-mix differences, Moreno (73) did not find significant differences in
psychological well-being between haemodialysis and CAPD patients. Oldenburg (78)
reported that increased emotional distress was significantly related to duration of
--
treatment and location of dialysis, home versus hospital. Lindsay (65) did not observe
significant differences in depressive symptoms or anxiety between home haemodialysis and
CAPD patients. M e r matcfiing CAPD patients to in-centre haemodialysis patients,
Wolcott (124) did not observe a significant difference in total mood disturbance.
Significant differences were reported across rend transplant, in-centre haemodialysis, and
CAPD patients regarding emotional distress and psychological well-being (14). As
already noted, insufficient quantitative details precluded the inclusion of these findings in
the meta-analysis.
The reliability of the data-extraction process was acceptable. The two independent
extractors agreed 79% of the time regarding the extraction of the case-rnix and dependent
variable information. For the 2 1 methodological rigour items, the kappa coefficients for
items two through six and item eight ranged fiom -82 to 1 .O and were deemed excellent.
Kappa was not calculated for items one, seven, and ten because one of the reviewers
selected only one code, The second reviewer agreed 88% of the time regarding these
items. Good agreement was found for item nine (kappa = .44) and poor agreement for
item 1 1 (kappa = -.023). Item I l corresponded to whether the results of the study were
qualified by limitations of the research method. This may have been too difficult to
evaluate objectively .
Treatment Comparisons of Emotional Distress and Psycholo~ical Well-Being
The results for the treatment cornparisons for emotional distress and psychological
well-being are presented in the following order. First, the surnmary results for the fixed-
effects mode1 for each treatment cornparison are presented, including sumrnary effect sizes
and their statistical significance (Le., the effect size is significantly different fiom zero, or
no treatment effect, when the 95% confidence interval does not include zero).
Additionally, the magnitude of significant treatment differences are interpreted using
percentile ranks to compare how the median person in one treatment group would score in
the other treatment group. Tables 2 and 3 present surnmary statistics for each of the
treatment cornparisons regarding emotional distress and psychological well-being,
respectively. The tables provide mean effect sizes, variances of the effect sizes, 95%
confidence intervals (CI), homogeneity test results, percentile ranks, fail-safe numbers and
tolerance levels to evaluate the threat of publication bias.
Second, sources of heterogeneity (i.e., systematic variability across the studies
providing effect-size estimates) are examined across al1 treatment comparisons and then
individually for each of the treatment comparisons. Table 4 presents evaluations of
heterogeneity across al1 treatment cornparisons for emotional distress and psychological
well-being. The type of research design, method of effect-sue estimation, methodological
rigour, citation index impact score, total sample size, and year of publication were
evaluated as potentiai contributors to the observed heterogeneity. Tables 5 and 6 provide
this information for each treatment cornparison for ernotional distress and psychological
well-being. Additionally, case-mix differences between treatment groups are examined as
potential contributors to the observed heterogeneity. Tables 7 through 12 summarize the
case-mix differences for each of the treatment comparisons, providing surnmary effect
sizes and 95% confidence intervals to indicate the magnitude and the significance of the
treatment group differences. The results of the sensitivity analyses, evaluating whether
case-mix differences between treatment groups were sources of heterogeneity, are
presented as well. Additionally, Tables 13 and 14 highlight the availability of case-mix
information for each of the treatment comparisons for emotional distress and
psychological well-being respectively. The numbers and percentages of studies providing
information regarding the specific case-mix variable is provided for each treatment
comparison and across al1 treatment comparisons for each dependent variable.
Additionally, the number of studies providing information regarding five or more of the 1 O
case-mix variables is provided. Finally, summary results using the random-effects model
are presented because significant sources of heterogeneity were not determined and this
model controls for between study variability.
Third an evaluation of the threat of publication bias is presented for each treatment
comparison regarding emotional distress and psychological well-being. The final two
columns of Tables 2,3, 15, and 16 provide fail-safe numbers and tolerance levels
respectively for each treatment comparison for each dependent variable under the fixed-
and random-effects models.
d-Faects Mode1
Successful Renal Transplant vs. In-centre Haemodialysis
For the treatment comparison of successfiil renal transplant versus in-centre
haemodialysis, 24 studies exarnined emotional distress and 16 examined psychological
well-being. Successful transplant patients reported significantly less emotional distress
(mean ES = -.40, n = 24, 95% CI: -.49 to -.3 1) and better psychological well-being (mean
ES = .64, n = 16, 95% CI: .55 to -73) than in-centre haemodialysis patients. These results
were, however, significantly heterogeneous (evaluated below).
Regarding emotional distress, the percentile rank corresponding to the effect size
was 34. This indicates that the person who scored at the median (50th percentile) of the
transplant group reported a level of distress that was equal to or greater than the distress
reported by only 34 percent of the in-centre haemodialysis group. In other words, 66%
of in-centre haemodialysis patients reported levels of distress greater than or equal to 50%
of the successfil transplant recipients. The percentile rank for psychological well-being
was 74 indicating that the median person in the transplant group reported a 1eveI of
psychological well-being equal to or above 74% of those in the in-centre haemodialysis
group.
Successful Renal Transplant vs. Continuous Ambulatory Peritoneal Dialysis (CAPD)
Ten and 1 1 studies compared successfil renal transplant and CAPD patients with
respect to emotional distress and psychological well-being, respectively. Successfùl
transplant recipients reported significantly less emotional distress than CAPD patients
(mean ES = -.26, n = 10,95% CI: -.40 to -. 13) and more psychological welI-being (mean
ES = .48,11= 11,95% CI: .37 to S9). These results were, however, significantly
heterogeneous.
The percentile rank for this treatment cornpanson regarding emotional distress was
40 indicating that the median person in the transplant group reported more emotional
distress than 40% of those in the CAPD group. In other words, 60% of those in the
CAPD group reported more emotional distress than the median person in the successfiil
transplant group.
Table 2. Summary Results of Treatment Cornparisons of Emotional Distress (Fixed-effects Model).
Cornparison (n)
CHD (24)
CAPD (1 O)
HHD (7)
CAPD v. CHD ( 1 7)
CAPD v. I-MD (7)
cm v. HHD (1 1 ) n = number of studies sumrnery results based upon. * Effect sizes significantly different from zero. RT = Renal Transplant, CHD = In-centre Haemodialysis, CAPD = Continuous Ambulatory Peritoneal Dialysis, HHD = Home Haemodial y sis.
rnean Variance 95% CI Percentile Fail-safe Tolerance ES Rank Number Level
-.40*
-.26*
-. 17
-.O6
.O021
.O047
.O077
.O03 6
-.49 to -.3 1
-.40 to -.13
-.34 to .O1
-.17 to .O6
128.1
38.5
22.4
38.9
,001
.O01
.O01
.O1
34
40
43
48
436
3 1
2
9
130
60
45
95
Table 3. Surnrnary Results of Treatment Compansons of Psychological Well-being (Fixed-effects Model).
n = number of studies summery results based upon. * Effect sizes significantly different fkom zero. RT = Renal Transplant, CHD = In-centre Haemodialysis, CAPD = Continuous Ambulatory Peritoneal Dialysis, HHD = Home Haemodialy sis.
Fail-safe Number
699
202
67
20
6
9
Comparison (n)
RT 17. CHD (1 6)
RTv.CAPD(11)
RT v. HHD(7)
CAPDv.CHD(17)
CAPDv.HHD(7) l
CHDi?.WWD(7)
Tolerance Level
90
65
45
95
45
45
95% CI
.55 to .73
.37 to .59
.24 to S 2
.O5 to .24
-.27 to .O6
-.33 to -.O9
mean ES
.64*
.48*
.38*
.14*
-.IO
-.21*
Variance
.O020
.O03 1
.O049
.O023
.O069
.O039
Percentile Rank
74
68
67
56
46
42
Homogeneity
Q
58.7
24.8
39.9
19.7
11.0
21.9
PC
.O01
.O2
.O01
.50
.20
.O1
- -
Regarding psychological well-being, the percentile rank was 68, indicating that the median
person in the transplant group reported more psychological well-being than 68% of those
in the CAPD group.
Successful Renal Transplant vs. Home Haemodialysis
Emotional distress and psychological well-being were compared seven times each
across successfùl renal transplant and home haemodialysis groups. Successfid transplant
patients reported significantly more psychological well-being than home haemodialysis
patients (mean ES = -38, rt = 7,95% CI: .24 to .52) but did not differ in ernotional distress
(mean ES = -. 17, n = 7,95% CI: -.34 to .O 1). These effect sizes were also heterogeneous.
The percentile rank for psychological well-being was 67 indicating that the rnedian
person in the transplant group reported more psychological well-being than 67% of those
in the home haemodialysis group.
Continuous Ambulatory Peritoneal Dialysis (CAPD) vs. In-centre Haemodialysis
Emotional distress and psychological well-being were compared 17 times each
across CAPD and in-centre haemodialysis groups. CAPD patients reported significantly
more psychological well-being than in-centre haernodialysis patients (mean ES = .14, n =
17, 95% CI: .O5 to .24). There was no significant difference regarding ernotional distress
(mean ES = -.06, n = 17, 95% CI: -. 17 to .06). The emotional distress efTect sizes were
significantly beterogeneous but the psychological well-being effect sizes were not.
The percentile rank for psychological well-being was 56, indicating that the median
person in the CAPD group reported more psychological aell-being than 56% of those in
the in-centre haemodialysis group.
Continuous Ambulatory Peritoneal Dialysis vs. Home Haemodialysis
EmotionaI distress and psychological well-being were compared seven times each
across CAPD and home haemodialysis patients. CAPD and home haemodialysis patient
groups did not differ significantly regarding emotional distress or psychological well-being
(mean ES = .21,n = 7,95% CI: -.O0 to -41 and mean ES = -.IO, n = 7,95% CI: -.27 to
-06, respectively), although the difference in emotional distress approached significance.
These effect-size estimates were not heterogeneously distributed.
In-centre Haemodialysis vs. Home Haemodialysis
EmotionaI distress and psychological welI-being were compared across in-centre
and home haemodialysis groups 1 1 and seven times, respectively. In-centre haemodialysis
patients reported significantly more emotional distress than home haemodialysis patients
(mean ES = .26, n = 1 1,95% CI: .10 to .42) and less psychological well-being (mean ES
= -.2 1, n = 7,95% CI: -.33 to -.09). Psychological well-being effect-size estimates were
significantly heterogeneously distributed although emotional distress effect-size estimates
were not.
The percentile ranks for emotional distress and psychological well-being were 60
and 42, respectively. This indicates that the median person in the in-centre haemodialysis
group reported more emotional distress than 60% of those in the home haemodialysis
group. The median person in the in-centre haemodialysis group reported more
psychological well being than 42% of those in the home haemodialysis groups. In other
words, the median person in the home haemodialysis group reported more psychological
well-being than 58% of those in the in-centre haemodialysis group.
Evaluation of Weterogeneity
As noted, the effect-size estimates for many of the treatment comparisons were
heterogeneous, indicating that some nonrandom variability also characterised the effect
size distributions. The following research characteristics were examined as potential
sources of effect-size heterogeneity: research design; method of effect-size calculation;
sample size, methodological rigour (i.e., scale developed for this meta-analysis and citation
index impact score), year of publication, and case-mix differences between treatment
groups. Table 4 sumrnarizes the resuits for five of these six potential sources of
heterogeneity for each dependent variable across al1 treatment comparisons. Tables 5 and
6 surnmarize these results for each of the treatment comparisons.
Research Characteristics
The research designs included the independent-group design, equivalent- or
matched-group design, and prospective repeated-measures design. The results indicated
that the emotional distress and psychological well-being effect-size estimates did not
significantly Vary by the type of research design when comparing al1 three design types or
when comparing the independent group design with the other two designs combined.
Similar results were observed for each independent treatment cornparison (see Tables 5
and 6).
Method of Effect-Size Estimate Calculation
Effect-size estimates were either calculated directly or estirnated. Direct
calculations of effect size included the standardized mean difference method, Cohen's d
for categorical data, t or F(1df) statistics, or exact p values. Effect-sizes were estimated
when only non-specific p values (e-g., y < .O5 or "there was no significant difference
between treatment groups") were reported. Tables 4, 5, and 6 summarize these results
indicating that effect-size estimates did not significantly Vary with respect to the method of
estimation across the pooled or individual treatment comparisons for either dependent
variable.
Methodological Rigour
The methodological rigour of the study, as assessed by the scale developed for this
analysis and the citation index impact score, did not significantly relate to the effect-size
estimates when examined across al1 treatment comparisons (see Table 4). Significant
correlations were observed between the citation index impact score and emotional distress
in the renal transplant versus CAPD (r = -82) and CAPD versus in-centre haemodialysis
comparisons (r = -.69) (see Table 5). The reliability of these correlations was threatened,
however, by the small number of studies ( I I = 7) (1 01).
Additionally, the total sample size and year of publication did not significantly
relate to the effect-size estimates when examined across al1 of the pooled treatment
comparisons (see Table 4). Year of publication was significantly related to emotional
distress in the CAPD versus home haemodialysis treatment comparison (r = .78). Again,
the reliability of this correlation was threatened by the srnaIl sample size (11 = 7) (101).
Case-Mix Differences
Case-mix differences between treatment groups were examined as potential
explanations for the heterogeneity of the effect-size estimates. Tables 7 through 12
summarize the case-rnix differences for each treatment comparison, including summary
1 Design Characteristic
Research Design*
Table 4. Relationshi~ of Research Design Characteristics to Dependent Variable Effect Sizes Across Al1 Treatment Compariso
Independent Group
Equivalent
1 Emotional Distress (n = 76)
1 Prospective
Psychological Weil-being (n =: 65)
Effect Size Estimate
Direct
Estimated
Methodological Rigour
Citation Index Impact Score
Total Sarnple Size
1 Year of Publication median = 1988 range 1974-95
r = -.09, p = .46 median = 1989 r = -. 15, p = 2 3 range 1980-96
* Independent group versus "other" (equivalent and prospective) design for emotional distress, F(1,75) = 1.20, p = .28, and for psychological well-being, F(1,63) = -49, p = .49.
Table 5: Relationship of Research Design Characteristics to Emotional Distress for each Treatment Cornparison.
Effect Size Estimate
-
Independent Group
Equivalent
Prospective
RT vs. CHD (n = 24)
Research Design
RT vs HHD (n = 7)
RT vs. CAPD (n = 10)
17 (71%)"
3 (13%)
4 (17%)
Direct
Estimated
Methodological Rigour
Citation Index Impact Score
CAPD vs. CHD (n = 17)
1 O (1 00%)
- -
19 (79%)'
5 (21%)
1 D = 3 7 1 D = .O25 1 p = .540 1 p = .O57 1 p = .706 1 p = .27f
m = 6.4 SD = 1.88
r (24) =.29 p = .168
m=2.1 SD = 1.95
r (16) = .16
CAPD vs. HHD (n = 7)
7 (100%)
-
-
8 (80%)~
2 (20%)
CHD vs. H (n = i l )
m = 7.1 SD = 1.79
r (10) =-.28 p = ,443
m = 1.5 SD= 1.19
r (7) = 3 2
6 (86%)'
1 (14%)
11 (100%
- -
15 ( ~ 8 % ) ~
2 (12%)
-
rn = 8.3 SD= 1.38
r (7) =-.36 p = .428
m = 1.2 SD = .58
r (4) = .46
7 (100%)
- -
14 (82%)'
3 (18%)
m = 6.9 SD = 1.50
r(17)=-.18 p = ,485
m = 1.7 SD = 1.05
r (7) = -.69
6 (86%)"
1 (14%)
8 (73%)
3 (27%)
m = 7.9 SD= 1.57
r (7) =-.37 p=.411
m = 1.2 SD = .58
r (4) = .29
m = 7.2 SD = 1.9
r (1 1) = -. p = ,955
m = 1.0 SD = .61
r (6) = .5
Year of Publication median 1987 range 1978-95
r = -.28 p = .18 t
RT vs. CAPD (n = 10)
CAPD vs. CHD CAPD vs. HHD CHD vs. HI (n = 7) RTvsHHD 1 (n = 17) (n = 7) 1 (n = 11)
median 1988 range 1984-95
median 1988 range 198 1-95
l
median 1990 median 1989 median 198 , range 1984-95 range 1984-95 range 1974-
Table 6: Relationship of Research Design Characteristics to Psychological Well-being for each Treatment Cornparison.
Effect Size Estimate
Direct
Estirnated
RT vs. CHD (n = 16)
Research Design
Methodologicai Rigour
Independent Group
Equivalent
Prospective
Citation Index Impact Score
RT vs. CAPD (n = 11)
15 (94)"
RT vs HHD (n = 7)
11 (100)
CAPD vs. CHD (n = 17)
7 (100)
-
CAPD vs. HHD (n = 7)
- -
-
CHD vs. HH (n = 7)
15 (88)b
2 (12)
- 1 (6)
7 (100)
-
1 1 (1 00)
-
- - -
1 1 RT vs. CHD 1 RT vs. CAPD
1 Total Sarnple Size 1 rn = 186 1 m = 227
Year of Publication
RT vs HHD (n = 7)
SD = 258.3
r = .36 p = .17
median 1989 range 1980-96
r = .33 p = .21
r = -.35 p = .44
median 1988 range 1984-96
CAPD vs, CHD (n = 17)
SD = 254.9
r = -.O2 p = .95
median 1989 range 1984-96
r = .O3 p = .92
r = .O8 p = .76
median 1990 range 1984-96
CAPD vs. HHD (n = 7)
CHD vs, HI (n = 7)
r = -.16 p = .73
median 1988 range 1984-96
r = -.O9 p = .85
median 198 range 1984-
effect sizes and their 95% confidence intervals. To facilitate sensitivity analyses, case-mix
variables were coded "O" when the treatment cornparison did not significantly differ with
respect to the variable, "1" when they did significantly differ, and "2" when the case-mix
variable was not assessed. Sensitivity analyses were conducted to determine if the effect-
size estimates varied with respect to the categorized case-mix treatment group differences.
Six (5.8%) of the 120 sensitivity analyses were significant at p < .O5 but due to the large
number of comparisons, these must be considered skeptically.
The failure of case-rnix differences to account for the heterogeneity within the
distributions of effect-size estimates may be due to the relatively small numbers of studies
comparing rend replacement therapies with respect to emotional distress and
psychologicai well-being (i.e., the number of treatment comparisons for each dependent
variable range between 7 and 24). Additionally, few studies provided the information
necessary to calculate effect-size estimates for al1 of the case mix variables. Tables 13 and
14 display the number and percentage of studies providing information for each case-mix
variable for each of the treatment comparisons regarding emotional distress and
psychologicai well-being, respectively.
The treatment groups significantly differed with respect to many case-mix
variabIes. The renal transplant and in-centre haemodialysis groups differed, for example,
such that the renal transplant groups were significantly younger; more likely to be
employed; more highly educated; in better physical health; had a longer duration of ESRD;
had been in treatment longer; and were less likely to have experienced a renal transplant
failure (see Table 7). Compared to the CAPD groups, renal transplant recipients were
* indicates case-mix variable that significantly differs between the treatment groups.
Variable
Age
Gender
Marital Status
Empioyrnent Status
Education
Diabetic Status
Physical Disability (General)
Duration of Illness
Duration of Treatment
Previous Transplant Failure n = number of studies providing information necessary to calculate effect size.
n
22
23
14
mean ES
-.46*
.O6
.O6
95% CI
-.55 to -.37
-.O1 to .13
-.O5 to .17 I
14 1 .37* .26 to .48
.19 to .41
-.19 to.09
-.70 to -.62
.O9 to .35
.21 to .45
-.49 to -.15
9
10
20
9
11
7
Sensitivity Analyses
.30*
-.O5
-.66*
.22*
.33*
-.32*
Emotional Distress
m.
n.s.
n.s.
.O5 1
n. S.
n.s.
n. S.
n. S.
n.s.
n.s.
Psychological Well- being
.O72
n.s.
n. S.
n.s.
.O01
n. S.
n. S.
n.s.
n.s.
n. S.
Variable v Gender 1 10
Marital Status 1 7
Employment Status
Education
Diabetic Status 1 Physical Disability (General) 1 1 1
Sensitivity Analyses I
Emotional 1 Psychological Distress 1 WelCbeing
* indicates case-mix variable that significantly differs between the treatment groups.
Duration of Illness
Duration of Treatment
Previous Transplant Failure n = number of studies providing information necessary to calculate effect size.
2
4
6
.69*
.82*
-.28*
.41 to .97
.63 to 1.01
-.46 to -.O9
.O07
n.s.
n.s.
n.s.
n.s. n.s.
I aoie Y; L ~ S ~ L V I I X ~urnrnary mrorrriarion. I ranspianr vs. nome naemoaiaiysis (n =
- -
1 Previous Transplant Failure 1 - n = number of studies providing information necessary to calculate effect size. * indicates case-mix variable that significantly differs between the treatment groups.
Variable Sensitivity Analyses 1
1 Gender
1 Marital Status
1 Ernployment Status
1 Education
1 Diabetic Status
1 Physical Disability (General)
1 Duration of Illness
~ase-IVIIX aurrimary rriiur rriaiiu~i. Lumiriuuus ~ i ~ i u u i a ~ u i y rci iwricai
Dialysis vs. In-Centre Haemodialysis (n = 25).
- - - ---
Sensi tivity Analyses
Emotional Distress
Psychological Well-being
Variable
Age
Gender
Marital Status
Employrnent Status
Education
Diabetic Status
Physical Disability (General)
Duration of Illness
Duration of Treatment h
Previous Transplant Failure n = number of studies providing information necessary to calculate effect size. * indicates case-mix variable that significantly differs between the treatrnent groups.
95% CI
-.2O to -04
-.O8 to .12
-.O9 to .23
.O6 to .36
.10 to .39
.O3 to .38
-.O3 to .O6
-1.01 to -.55
-.70 to -.42
-.28 to . l l ,
n
16
20
12
1 1
10
6
1 8
5
13
7
mean ES
-.O8
.O2
.O7
.21*
.25*
.21*
.O 1
-.78*
-.56*
-.O8
Variable n rnean 95% CI ES
Dialysis vs. Home ~aernodial~sis (n = 9).
Sensitivity Analyses
*ge 7 .22* .O1 to .42
Gender 8 -. 16 -.36 to .O4
Marital Status ( 5 1 -.35 1 -.66 to .O3
Employment Status
Education
Diabetic Status
Physical Disability (General) 7 .18* .O1 to .34
Duration of Illness 2 -.37* -.71to-.O3
Duration of Treatment 1 5 1 -.94* 1 -1.19 to -.70
-
- - - - - -
-
- - -
1 1 - n = number of studies providing information necessary to calculate effect size. * indicates case-rnix variable that significantly differs between the treatment groups.
Previous Transplant Failure 5 - 17 -.39 to .O6
Variable 95% CI 1 Sensitivity Analyses
Emotional Distress
Psyc hological Welt-being
n. S.
n. S.
n.s.
n.s.
Gender
Marital Status
Employment Status
Education
Diabetic Status
Physical Disability (General)
Duration of Illness
Duration of Treatment
Previous Transplant Failure 1 I
n = number of studies providing information necessary to calculate effect size. * indicates case-mix variable that significantly differs between the treatment groups.
significantly younger, more likely to be male, more likely to be employed, in better
physical health, had a longer duration of ESRD, had been in treatment longer, and were
less likely to have experienced a transplant failure (see Table 8). The renal transplant and
home haemodialysis groups differed significantly as the renal transplant patients were
significantly younger, in better physical health, and were less likely to have experienced a
transplant failure (see Table 9). Compared to in-centre haemodialysis patients, CAPD
patients were significantly more likely to be employed, more highly educated, more likely
to have diabetes, had a shorter duration of ESRD, and had been in treatment for less time
(see Table 10). CAPD and home haemodialysis patients differed in that CAPD patients
were significantly older, in poorer physical health, had a shorter duration of ESRD, and
had been in treatment for less time (see Table 1 1). In-centre and home haemodialysis
patients differed in that in-centre patients were significantly more likely to be female, less
educated, in poorer physical health, and had been in treatment for less time (see Table 12).
m-Effects Mode1
The random-effects model may provide estimates of the treatment effects while
controlling for between-study variability in research characteristics. Under the random-
effects model, the variability around the effect size contains within- and between-study
variability and, therefore, incorporates the heterogeneity (i.e., between-study variability)
into the effect-size variance (89). It controls for variability in research characteristics
(e.g., research design and measurement) between the studies but cannot control for non-
treatment differences between the treatment groups (e.g., case-mix) because they are
confounded with the reported group means employed to calculate effect sizes. Tables 15
Table 13. Availability of Case-Mix Information for Treatment Compansons on Emotional Distress.
Variable RT vs. RT vs. RT vs. CAPD vs. CAPD vs. CHD CAPD HHD CHD HHD
(n = 24) (n = 10) (n = 7) (n = 17) (n = 7)
Agea 18 (75) 6 (60) 6 (86) 12 (71) 6 (86)
Sex ) 1 9 ( 7 9 ) 1 8(80) 1 6(86) 1 15(88) 1 6(86)
Marital Status 12 (50) 6 (60) 5 (71) 9 (53) 4 (57)
Education 8 (33) 4 (40) 4 (57) 7 (41) 4 (57)
Employment 12 (50) 4 (40) 3 (43) 8 (47) 2 (29)
Previous Transplant 6 (25) 5 (50) 5 (71) 5 (29) 4 (57)
Duration of Illness 7 (29) 1 (10) 1 (14) 3 (18) 1 (14)
Duration of Treatment 9 (37) 3 (30) 4 (57) 9 (53) 4 (57)
number (percentage) of studies providing information necessary to calculate efFect size for the case-m
CHD vs. All HHD Cornparisons
(n = 11) (n = 76)
--
u variable. RT = Renal Transplantation; CHD = In-Centre Haemodialysis; CAPD = Continuous Ambulatory Peritoneal Dialysis; HHD = Home Haemodialysis.
O /
and 16 summarîse the treatment comparisons using the random-effects approach for
emotional distress and psychological well-being, respectively. Similar mean effect sizes
and significance levels were obtained as observed for the fixed-effects approach. The
main difference was that the random-efTects results were more likely to be threatened by
publication bias (Le., only two of the 12 treatment cornparisons were not threatened by
publication bias as compared to four for the fixed-effects model). Using the random-
effects approach in this meta-analysis can also be misleading because the treatment groups
being compared differ significantly with respect to case-mix. Case-mix differences must,
therefore, be mled out as an alternative explanation for observed treatment-group
differences in emotional distress and psychological well-being.
Publication Bias
The threat of publication bias was evaluated for each of the treatment comparisons
regarding emotional distress and psychological well-being for both the fixed- and random-
effects models. Under the fixed-efects model, four of the 12 (33%) treatment
comparisons were not threatened by publication bias. Regarding emotional distress, only
the renal transplant versus in-centre haemodialysis cornparison was not threatened by
publication bias as the fail-safe number (436) was substantially greater than the tolerance
level(130) (see Table 2). Regarding psychological well-being, the treatment comparisons
of renal transplantation with each of the three dialysis types were not threatened by
publication bias (see Table 3).
Under the random-effects model, the results of 10 (83%) of the 12 cornparisons
were threatened by publication bias. The comparisons of in-centre with home
n = number of studies summery results based upon. * Effect sizes significantly different fiom zero. RT = Renal Transplant, CHD = In-centre Haemodialysis, CAPD = Continuous Ambulatory Peritoneal Dialysis, HHD = Home Haemodialysis.
Cornparison (n)
RT V . CHD (24)
RT V. CAPD (10)
RT V . HHD (7)
CAPD V . CHD (17)
CAPD V . HHD (7)
CHD v. HHD ( I l )
95 % Cl
-.71t0-.15
mean ES
-.43*
-.29*
-.2 1
-.O9
.O9
.16*
Percentile Rank
33
Fail-safe Number
33
-.55 to -.O3
-.49 to .O7
-.29 to . 1 1
-.25 to .43
.O7 to .24
Tolerance Level
130
2
O
O
7
70
39
42
46
54
.56
60
45
95
45
65
1 UY.Y . W . --.m..-*-- I - - - - -cv Y- - - ---------- Y U * . . r u "W." " ' ' J W"W"~'W..' . . "' "Y"' &
(Random Effects Model).
Comparison (n)
RT v. CHD (16)
RT v. CAPD (1 1 )
RT V . HHD(7)
CAPD v. C m (1 7)
n = number of studies summery results based upon. * Effect sizes significantly different fiom zero. RT = Renal Transplant, CHD = In-centre Haemodialysis, CAPD = Continuous Ambulatory Peritoneal Dialysis, HHD = Home Haemodialysis.
I
95% CI
.46 to .78
mean ES
.62*
CAPD V . HHD (7)
CHD V . HHD (7)
.53*
.66
.14*
Percentile Rank
73
-.O6
-. 19
.30 to .76
-.O9 to1.4 1
.O4 to .25
Fail-safe Number
20 1
-.45 to .34
-.73 to .35
Tolerance Level
90
70
75
56
48
42
49
O
12
65
45
95
O
O
45
45
/ V
haemodialysis regarding emotional distress and renal transplant with in-centre
haemodialysis regarding psychological well-being were not threatened by publication bias.
Therefore, under both the fixed- and random-effects rnodels the results of the treatment
cornparisons that were threatened by publication must be interpreted cautiously.
Discussion
There is some disagreement among nephrologists as to which of the available
RRTs affords patients with the best quality of life. Nephrologists who manage ESRD
programs may believe that the treatment in which they specialize is related to the best
quality of life outcomes and these beliefs may influence their recommendations t o patients
regarding selection of treatments. There are additional implications for the allocation of
the treatment modalities. For exarnple, there are significant differences in the financial
cost of the alternative treatments. Unfortunately, the literature on which professionals
base their opinions is inconsistent, indicating that the quality of life differences across
RRTs are not as clear as one rnight have thought. This inconsistency is related to the fact
that many different dependent variables are examined, including emotional distress,
psychological well-being, survival, and vocational rehabilitation. Additionally, this
inconsistency may be related to the methodological limitations of this research including
the comparison of non-equivalent treatment groups. Al1 these factors contribute to the
difficulty of synthesizing this literature. Therefore, a meta-analysis was undertaken to
examine this issue more thoroughly while also clarifying the effects of research
characteristics and non-treatment differences between groups on the research findings.
Fixed-Effects Mode1 Results
Using the fixed-effects model, the literature to date comparing RRTs with regard
to quality of life indicated that successfully transplanted patients consistently reported
more psychological well-being and less emotional distress than dialysis patients,
collectively. CAPD patients reported more psychological well-being than in-centre
haernodialysis patients but did not differ with respect to emotional distress. CAPD
patients did not significantly differ fiom home haemodialysis patients. Home
haernodialysis patients reported more psychological well-being and less emotional distress
than in-centre haemodialysis patients. The majority of the treatment cornparisons were
heterogeneous, indicating that there was systematic variability across the studies in
relation to t heir effect-size estimates. Under the fixed-effects model, it is inappropriate to
rnake generalizations based on heterogeneous effect-size estirnates as the heterogeneity
indicates that al1 of the estimates do not stem from the sarne underlying population
treatment difference. Sensitivity (or sub-group) analyses are conducted to determine
sources of heterogeneity. If sources of heterogeneity are not identified, the random-
effects model is applied because it takes between-study variability into consideration in its
variance component.
Evaluation of Heterogeneity
Potential sources of heterogeneity, including research characteristics, were
investigated t O determine if t hese characteristics were associated systematicall y wit h larger
or smaller treatment effects. Heterogeneity was not explained, however, by differences in
the type of research design employed, the method of effect-size calculation, the
- -
rnethodological rigour of the study, total sample size, or year of publication. The
treatment groups being compared differed significantly with respect to case-mix variables
but significant variability in effect-size estimates was not explained by sensitivity analyses
(ie., categorizing the studies as to whether or not the patient groups differed with respect
to case-mix variables). There may have been inadequate power to observe significant
variability due to the relatively small number of studies comparing treatment modalities
regarding emotional distress and psychological well-being and the even smaller number of
studies providing sufficient case-mix information. Sirnilarly, given that the majority of
comparisons used an independent-group design (88% of emotional distress and 95% of
psychological well-being comparisons) and the majority of effect sizes were calculated
directly (80 to 85%), the study's ability to detect systematic differences across alternative
research designs and effect-size estimation procedures rnay also have been constrained
excessively .
Random-Effects Mode1 Results
The random-effects model controls for variability between the studies in research
characteristics (e.g., research design, measurement, sample size, etc.) but cannot control
for non-treatment differences between the groups (i.e., case-mix) due to the fact that the
effect-size estimates were calculated from statistics based upon treatment-group
comparisons that were confounded with case-mix differences. Therefore, based on the
random-effects model, the following conclusions can be made fiom this meta-analysis.
When comparing in-centre haemodialysis patients with successfùl renal transplant patients
who were significantly younger, more likely to be employed, more highly educated, in
- -
better physical health, had a longer duration of ESRD, had been receiving RRT for a
longer time, and were less likely to have experienced a transplant failure, the successfùl
renal transplant group reported significantly less emotional distress and more
psychological well-being. Similar results were observed when cornparhg successfùl renal
transplant recipients with CAPD patients. The non-treatment differences between these
two groups were similar to those reported above except that in this cornparison, the
successful renal transplant recipients were significantly more likely to be male and the
groups did not differ significantly in level of education. When comparing successfùl rend
transplant recipients who were significantly younger, in better physical health, and less
likely to have experienced a renal transplant failure Mth home haemodialysis patients,
there was no significant difference with respect to emotional distress or psychological
well-being. CAPD patients, who were significantly more likely to be employed, more
highly educated, more likely to be diabetic, had a shorter duration of illness, and had been
receiving RRT for a shorter length of time than in-centre haemodialysis patients, also
reported significantly more psychological weil-being but did not differ with respect to
emotional distress. When home haemodialysis patients were compared to CAPD patients
who were significantly older, in poorer physical health, and had a shorter duration of
illness and treatment, the groups did not significantly differ with respect to emotional
distress or psychological well-being. When home haemodialysis patients were compared
to in-centre haemodialysis patients who were significantly less likely to be male, less
educated, in poorer physical health, and had a shorter duration of treatment, the home
haemodialysis patients reported significantly less emotional distress but did not differ
regarding psychological well-being. Therefore, the results of this meta-analysis revealed
how quality of life would differ across patient groups that differed with respect to
sociodemographics and physical health. This meta-analysis did not reveal whether quality
of life would differ if two individuals who were similar with respect to sociodemographics
and physical status were placed on two different foms of RRT. These results are
summarized in Table 17. Colurnn one indicates the treatment cornparison being made.
The second column identifies the case-rnix variables that significantly differed between the
treatment groups. Column three is the mean effect-size estimate for the significant case-
mix difference. Columns four and five indicate the magnitude of the difference between
the treatment groups for emotional distress and psychological well-being, respectively.
Knowing that some of the case-rnix variables that differed across the treatrnent
groups were also related to quality of life outcornes compromises the interpretability of
these resuIts. For example, renal transplant patients reported less emotional distress and
more psychological well-being than dialysis patients but they also differed on other
variables including age and general physical health. Renal transplant recipients were
younger and in better physical health than dialysis patients in general. Previous research in
ESRD patients has indicated that older age and poorer physical health were associated
with increased emotional distress and decreased psychological wefl-being (4,6,27,28,46).
Therefore, the observed differences between renal transplant recipients and dialy sis
patients on emotional distress and psychological well-being cannot be attributed to the
treatments alone but may also or alternatively be attributable to these case-rnix differences.
Distress, and Psychological Well-Being.
Cornparison Significsnt" Case-Mix Differences Emotional Distress
Psyc hological Well-Being
Mean ES - -
RT vs CI-TD Age Employed Educated Physical Disability Duration of ESRD Duration of Treatm ent Experience RT Failure
RT vs CAPD Age Gender (%Male) Employed Physical Disability Duration of Illness Duration of Treatment Experience RT Failure
RT vs HHD Age Physical Disability Experience RT Failure -
Employed Educated Diabetic Duration of IIlness Duration of Treatment
CAPD vs C m
CAPD vs HHD Age Physical Disability Duration of Illness Duration of Treatment
Gender (%Male) Educated Physical Disability Duration of Treatment
:T = Renal Transplantation; CHD = Haernodialysis; CAPD Ambulatory Peritoneal Dialysis; HHD = Home Haemodialysis. " Mean effect sizes are reported only for case-rnix variables that differed significantly between treatment groups. * the dependent variable mean effect size estimate (d) differs significantly fion1 zero (O).
Interpretation of Effect-Size Estimates
The effect-size estimates produced in meta-analysis are difficult to interpret
because they have no absolute meaning. To facilitate an understanding of the magnitude
of these treatment differences in quality of life, the obtained effect sizes can be compared
to effect-size estimates of clearer clinical importance. Under the fixed-effects model, the
effect-size estimates ranged from -. 40 to -64. The distribution under the random-effects
model was similar and ranged from -.43 to .62. One study whose results were of
substantial clinical importance was the Physician Aspirin Study (1 19). This study was
terminated prematurely when partway through the trial, it was clear that the experimental
treatment was superior to the control condition. The experimenters believed, based on the
interim results, that the magnitude of experimental benefit was sufficiently large to render
it unethical to continue withholding treatment. The magnitude of difference (4 between
the experimental and control groups regarding prevention of heart attacks was .07. It was
a small but clinically important treatrnent effect (98).
Another example from research more directly relevant to Nephrology concems the
clinical validation of cyclosporin in renal transplantation. Cyclosporin produced treatment
effects (4 of -40 in preventing or delaying organ rejection and .30 in irnproving patient
s u ~ v a l after receiving a kidney transplant (20,98).
These two important research findings had treatment effects similar to or srnaller
than those observed in this meta-analysis comparing RRTs on emotional distress and
psychological well-being. For example, the smallest significant treatment comparison in
this meta-analysis was the comparison of CAPD with in-centre haemodialysis on
97
psychological well-being (mean ES = .14) and this treatment effect is more than twice the
size of the effect observed in the Physician Aspirin Study. The largest significant effect
observed in this meta-analysis was for the cornparison of renal transplantation and in-
centre haemodialysis patients on psychological well-being (mean ES = .62). This effect
was almost one quarter of a standard deviation (.22) larger than the effect of
cyclosporine's ability to prevent or de1ay the rejection of a transplanted kidney.
Therefore, the effect-size estimates observed in this meta-analysis should be regarded as
meaningiùl and clinically significant.
Clinical Implications of Research Findings
The clinical implications of this research concern the elevated levels of distress
reported by dialysis patients compared to transplant recipients. Previous research has
shown an increased rate of suicide and depression in dialysis patients (2,91). Therefore,
dialysis patients should be carefülly monitored and counselled for signs of emotional
distress. Additionally, the National Institute of Health 0 recornmends active and early
involvement of behavioural and mental health professionais in the clinical management of
kidney disease to enhance medical adjustment and preserve quality of life (1,26). Psycho-
educational interventions to facilitate adaption of ESRD patients to their illness and its
treatment have been developed previously (e.g., (12,88)) and are in line with the
recomrnendations of the NIH.
Benefits and Limitations of Meta-Analysis
This meta-analysis was informative but also had some limitations. The
contribution that this meta-analysis made was that it summarized the discrepant research
- -
findings and indicated the magnitude of the difference between the treatment groups on
emotional distress and psychological well-being. It ako summarized the magnitude of the
non-treatment differences between the comparison groups regarding sociodemographic
and physical status indicators. This importantly revealed that across the literature,
treatment groups differ fkquently and substantially, compromising the validity and
interpretability of results.
This meta-analysis was limited by its reliance on published literature, by the limited
amount of information provided in the studies regarding case-rnix differences between
treatment groups, and by meta-analytic methods that are still under development.
Published literature is more likely to report significant treatment differences and, therefore,
may infiate the estimated treatment differences in quality of life (44). The examination of
case-rnix differences between treatment groups as potential sources of heterogeneity was
handicapped by the poor reporting of case-mix information. Additionally, modem meta-
analytic procedures are still in the process of development thereby limiting the range of
statistical procedures routinely available.
PubIication Bias
Publication bias is a common criticism of meta-analytic reviews (44,97). Using the
fixed-effects rnodel, eight of the 12 (67%) treatment comparisons examined were
threatened by publication bias. The comparison of renal transplant recipients with in-
centre haemodialysis patients regarding emotional distress was the only emotional distress
comparison not threatened by publication bias. The rend transplant comparisons with the
three dialysis groups regarding psychological well-being were not threatened by
- -
publication bias. Alternatively, using the random-effects model, ten of the twelve (83%)
comparisons were threatened by publication bias. The comparisons of in-centre with
home haemodialysis regarding emotional distress and successfùl renal transplantation with
in-centre haemodialysis regarding psychological well-being were not threatened by this
problem. Thus it is possible that the "true" quality of life differences across RRTs may
have been obscured by systematic non-publishing of negative or contradictory findings.
Since many of the comparisons were threatened by publication bias, these results must be
interpreted cautiously. On the other hand, a unique feature of the research in this area is
that the studies often include multiple-treatment comparisons. Typically, only a sub-set of
the many treatment comparisons undertaken are found to be significant resulting in the
publication of at least some non-significant treatment comparisons. Therefore, the results
of this meta-analysis rnay not be as seriously threatened by publication bias as rnight
othenvise be expected.
One way to evaluate the impact of publication bias more reliably would be to
retrieve and compare unpublished results with the published studies included in this meta-
analysis. Unfortunately, retrieving unpublished research is difficult. In some areas of
research, registers are used to summarize the work being conducted ( e g , Cancer or
AIDS) (30). The author is unaware, however, of any research registers in the field of
Nephrology. Identifjing unpublished research through registers would then require
contact with the principle investigators to obtain the information necessary for inclusion in
the meta-analysis. It rnay be difficult to obtain information fiom the investigators as was
exemplified in this meta-analysis where of the authors contacted to provide additional
information, less than 50% responded to the request.
Methodological Rigour
The finding that methodologicai rigour did not significantly relate to the magnitude
of the treatment differences may also be related to publication bias. It is possible that the
quality of the published studies is significantly better than that of the unpublished research.
Therefore, there may not have been a wide enough range in study quality to examine this
relationship adequately. Another possible explanation is that the operationd definition
used may not have been sufficiently sensitive, reliable, or valid.
Research Design
Another research characteristic whose impact was difficult to examine was the
particular research designs employed. The majority of the studies included in the rneta-
analysis used the cross-sectional independent-group design, specificaily 88% and 95% of
the treatment cornparisons regarding emotional distress and psychological well-being,
respectively. Few studies used prospective or equivalent group designs. Therefore, it was
difficult to determine whether the type of research design was related to the magnitude of
the treatment difference.
Methodolo@cal Limitations of Meta-Analvsis
This meta-analysis was also limited by the methodologicai limitations of meta-
analysis. Statistical procedures for meta-analysis are still in the developmental phase. For
example, the method for calculating the effect-size estimate, Cohen's d, for categorical
data was not published until 1995 (48). In this meta-analysis sensitivity analyses were
used ta examine the relationship of case-mix differences with quality of life differences
- - -
across RRTs. Using sensitivity analyses, the studies were categorized as to whether they
did or did not differ with respect to the case-mix variables. The effect-size estimates were
then examined as to whether or not they varied across studies that did or did not differ
with respect to the case-mix variables. It would have been more informative to examine
whether the magnitude of difference between treatment groups regarding a particular
case-mix variable was related to the magnitude of the difference in the dependent variable
(e-g., correlating a physical status effect-size estimate with the emotional distress effect-
size estirnate). Unfortunately, to the best of this author's knowledge, comelation and
regression procedures are not available to compare one effect-size estimate with another.
Contributing to the problern of exarnining the relationship between case-rnix
variables and the dependent variables was the fact that many of the studies did not report
al1 relevant information regarding case-mix variables. Across al1 the treatment
cornparisons for each dependent variable, approximately 50% of the studies provided
information regarding at least one of the 10 potential case-mix variables (see Tables 13
and 14). Standardking the assessrnent and reporting of case-rnix variables will facilitate
fiïture meta-analyses to examine the relationship between case-mix and dependent
variables (this idea will be elaborated in Chapter 3).
Combining Effect-Size Estimates within a Study
Another potential methodological limitation concerns the procedure used to
combine multiple effect-size estirnates for one study. In meta-analysis, each study is
allowed to contribute one effect-size estimate for each dependent variable. For studies
that used more than one measure of the dependent variable, the results from these
measures must be combined to provide one estimate for the dependent variable. The
procedure used in this analysis was to average the estimates to provide one overall
estimate for the study. This is a conservative procedure and often underestimates the
magnitude of the treatment difference (1 04). A more precise procedure requires
correlations between the measures of the dependent variable (4 1). Unfortunately,
correlations are rarely provided in the published reports, making this procedure
impractical.
Further Evaluation of the Relationship between Case-Mix and Quality of Life
This meta-analysis identified areas where fùture research could facilitate our
understanding of quality of life issues in ESRD patients. More research is needed to
examine the relationship between the case-mix variables and quality of life. Usually, the
relationship between case-mix variables and quality of life is examined within studies
comparing treatment modalities. Therefore, the research may not be designed to provide a
thorough examination of this relationship. For example, the sarnple size rnay not be
adequate to detect significant relationships.
The following is an example of a study that could be conducted to fûrther evaluate
the relationship between one of the case-mix variables, physical status, and quality of life
in ESRD. First reliable and valid measurement instruments should be selected for the
independent and dependent variables. The End-Stage Renal Disease Severity Index is a
reliable and valid indicator of the severity of CO-morbid conditions in ESRD patients
(23,45). Semm albumin has been shown to be an excellent indicator of morbidity and
mortaiity in ESRD patients (67,79). The Center for Epidemiologic Studies Depression
- - -
Scale is a reliable and valid indicator of emotional distress in ESRD patients (29). The
Campbell Index of Well-Being assesses two elements of psychologicai well-being, affect
and life satisfaction, and is reliable and valid rneasure that has been used in a large scale
evahation of ESRD (28,36). Use of these measurement instruments will assist in reliably
and validly assessing the independent variable, physical status, and the dependent
variables, ernotional distress and psychological well-being. An adequate sample size must
be determined to facilitate the observation of a significant relationship between the
independent and dependent variables when one exists. A stratified random sarnple of
patients fiom each RRT, including rend transplant or any form of dialysis, will ensure that
each treatment group is represented equally. Additional case-mix variables should be
assessed as potential covariates. For example, age should be assessed as a potential
covariate because increasing age is typicaliy associated with diminished physical health.
Additionally, age may be significantly related to emotiond distress or psychological well-
being and, therefore, may confound the relationship between physical status and the
dependent variables. Correlation analyses can be used to examine the independent
relationships between physical status, emotional distress, and psychological well-being.
Regression analyses can be used to examine the relationship of physical status with
emotional distress and psychological well-being after controlling for sociodemographic
and other physical status variables that are significantly related to the dependent variables.
This study would tùrîher the understanding of the relationship between physical health and
quality of life in ESRD patients.
Assessment of Physical Status
This meta-analysis suggested that the relationship between case-mix variables and
quality of life in ESRD patients was not clear and identified problems associated with the
measurement of some of the case-mix variables. For example, general physical heaith
significantly differed between the treatment groups but this variable was measured in a
multitude of different ways. Many of these measures contained physical and psychological
components and, therefore, may overlap with the dependent variables. For example, the
Karnofsky Performance Status Scale obtains a physician's evaluation of the patient's
ability to perform everyday activities (58). Udortunately, physical and ernotional States
influence an individual's everyday functioning and, therefore, the assessrnent of physical
status is influenced by physical and emotional factors (55). Future research should use an
indicator of physicai status that does not overlap with emotional status (e.g., the End-
Stage Renal Disease Severity Index; (23)). The assessrnent and reporting of case-mix
variables will be discussed fbrther in Chapter 3.
Emotional Distress and Psychological Well-Being as Distinct Constructs
This meta-analysis highlighted the fact that emotional distress and psychological
well-being are distinct constructs in ESRD patients. The magnitude of treatment group
differences were different for emotional distress and psychological well-being. For
example, the comparison of rend transplant with in-centre haemodialysis groups revealed
effect-size estimates of different magnitudes for emotional distress (mean ES = -.43, p <
.05) and psychological well-being (mean ES = .62, p < .05). Additionally, not a11
treatment groups that differed with respect to one dependent variable differed with respect
1 u3
to the other. For exarnple, CAPD and in-centre haemodialysis groups differed with
respect to psychological well-being (mean ES = .14, p < .05) but did not differ in
emotional distress (mean ES = -.09, y > .OS). Therefore, fiiture research exarnining the
emotional response to ESRD and its treatment should include measures of emotional
distress and psychological well-being.
This meta-analysis increased Our knowledge regarding the differences in quality of
life across RRTs but it also identified some areas which need fürther clarification. It
clarified that treatment group differences in quality of life were evident and significant but
these differences were based on treatment groups that differed significantly on many case-
rnix variables that have also been shown to be related to quality of life. Therefore, this
meta-analysis was not able to illuminate how quality of life might differ between two
individuals sirnilar with respect to sociodemographic and physical status characteristics
receiving two different forrns of RRT.
The meta-analytic method was a good technique to summarize the research
comparing RRTs on emotional distress and psychological well-being. It was able to
identi@ treatment groups that differed significantly across al1 of the literature. Also, it
summarized the differences between the treatment groups included in research regarding
case-mix variables, i.e., sociodernographic and physical status indicators. Unfortunately,
this procedure was not able to detennine the magnitude of the treatment effect after
controlling for case-mix differences between the treatment groups. Future research will
have to be developed to facilitate this examination.
L V U
Future Cornparisons of Quality of tife across RRTs
Using the findings of this meta-analysis, fùture research examining differences in
quality of life across RRTs should try to rninimize the case-mix differences between
treatment groups to further our understanding of the independent impact of different
treatment modafities on quality of life. The ideal study would randomly allocate
individuals to receive one of the available RRTs, including renal transplantation and al1
forms of dialysis. Unfortunately, this design is unpractical due to the physical and social
prerequisites for the treatment modalities (e-g., a haplotype matched donor kidney is
required for renal transplantation and a home and home partner are required for a forrn of
home dialy sis).
Therefore, the next study should have the following characteristics to improve the
internal and external validity of the research. A multi-centre study conducted in Canada
and the United States including profit and not-for-profit treatment facilities in urban and
rural areas can enhance the external validity. Urban, rural, for-profit, and not-for-profit
treatment facilities represent the majority of the situations in which treatment can be
provided and will, therefore, enhance the generalizability of the research findings.
To enhance the internal validity of the research as many potential threats to this
validity as cm be identified should be specified a priori and considered in the design of the
research or assessed as potential covariates. This rneta-analysis identified that many of the
treatment groups differed on age, specifically, rend transplant recipients were significantly
younger than al1 dialysis patients. Additionally, increasing age has been shown to be
related to increasing etnotional distress in ESRD patients (4,27). Gender is another
a - ,
important consideration because there may be a differential allocation of men and women
to alternative RRTs (38,116) and there are gender differences in emotional distress (46).
Therefore, a stratified randorn sarnple of research participants by age and gender would
improve the equivalence of the groups and control for the possibility that these variables
may partially explain quality of life differences between treatment groups. Additional
case-mix variables that are significantly related to quality of life in ESRD patients and
differ between treatment groups should be assessed and controlled for including marital
status, education, employment status, diabetic status, duration of current treatment, and
general physical health (6,36,46,53,56,62,8 1,84,124). Physical status was measured in
many different ways in the studies included in the meta-analysis. Two reliable and valid
indicators of physical status in ESRD patients assessing CO-morbid conditions and risk of
morbidity and mortality are the End-Stage Renal Disease Severity Index and Serum
Albumin, respectively (23,45,67,79). Social support is one dimension that is rarely
evaluated as a potential covariate in comparative studies of quality of life in ESRD. Social
support has been positively associated with psychological well-being in ESRD patients
(16,111). Therefore, social support should be assessed as a potential covariate in
comparative studies of quality of life in ESRD. The Social Support Questionnaire is a
reliable and valid measure of social support (107). An additional potential covariate is the
experience of non-illness related stressfùl life events which can also affect emotional well-
being (28). Devins et.al. (28) developed a method for assessing the experience of stresshl
life events.
The dependent variables, emotional distress and psychological well-being, should
- -
also be assessed by reliable and valid measures. The Center for Epiderniological Studies
Depression Scale has been shown to be reliable and valid measure of emotional distress in
ESRD patients (29). The Bradburn Affect Balance Scale, assessing positive and negative
affect, is a measure of emotional distress and psychoiogical well-being and has been used
fiequently to study ESRD patients (1 1,13,25,28). Another measure of psychological well-
being, including the dimensions of affect and life satisfaction, is the Index of Well-being
(1 8). It has been used fiequently in studies of ESRD patients (6,14,15,36,56,72,80,112).
To evaluate the degree of lifestyle dismptions associated with the RRTs and its
relationship with emotional distress and psychological well-being, the Illness Intrusiveness
Rating Scale can be used (25,27). It evaluates the degree to which the illness or its
treatment interféres with each of 13 Iife domains.
Therefore, to evaluate differences between RRTs regarding emotional distress and
psychological well-being the following procedure should be used. A stratified (by age,
gender, and M T ) random sample of ESRD patients fiom multiple centres should be
obtained to examine quality of life differences across RRTs. A power calcuiation should
be used to determine an adequate sample size to facilitate the observation of between
group differences in the dependent variables. Emotional distress and psychological well-
being should be assessed by the Centre for Epidemiological Studies Depression Scale,
AfTect Balance Scale, and the Index of Well-being which will allow two assessments of
emotional distress and psychological well-being. Lifestyle disruptions should be assessed
by the Illness Intrusiveness Ratings Scale. Case-mix differences should be assessed as
potential covariates using reliable and valid measurement instruments. Analysis of
covariance should be used to compare treatment groups on emotional distress and
psychological well-being while controlling for significant covariates.
Conclusion
This meta-analysis identified differences between RRTs regarding emotional
distress and psychological well-being. Significant findings were as follows: renaI
transplantation was associated with lower emotional distress and higher psychological
well-being than in-centre haemodialysis and continuous ambulatory peritoneal dialysis
(CAPD); rend transplantation was associated with higher psychological well-being than
home haemodialysis; CAPD was associated with higher psychological well-being than in-
centre haemodial y sis; and home haemodialysis was associated wit h higher psychological
well-being and lower emotional distress than in-centre haemodialysis. It did not find that
research characteristics (e.g., research design, methodological rigour, sample size, or year
of publication) or meta-analytic methods (e.g., the method of effect size calculation)
significantly related to the magnitude of the treatment differences. Also, this meta-
analysis, using sensitivity analyses, did not find that case-mix differences between
treatment groups were significantly associated with the dependent variable effect-size
estimates. It did reveal how different the treatment groups were with respect to case-mix
and highlighted how fiiture research could be improved to better address this problem.
Significant differences between treatment groups were evident but these groups also
differed importantly in non-treatment characteristics that may independently influence
emotional distress and psychological well-being.
Chapter 3
The Future for Quality of Life Studies in End-Stage Renal Disease
and the Role of Meta-analysis
The treatment for chronic illnesses is becoming increasingly cornplex and
demanding on patients and their families ( 1 1,27). Therefore, the impact of these
treatments on quality of life is becoming of greater importance. In end-stage renal disease
@SRD) it is believed that the available treatment rnodalities may differentially impact on
the patient's quality of life (28) and this belief has implications for treatment allocation.
The research exarnining this issue is threatened by methodological weaknesses. Randorn
allocation of patients to one of the available rend replacement therapies is impossible due
to the medical, social, and physical requirements associated with each of the treatments.
This results in widely differing groups of patients available to participate in research. The
main limitation of the research comparing quality of life across renal replacement therapies
(RRTs) is in their ability to control for non-treatment (i.e., case-rnix) differences between
the treatment groups as alternative explanations for quality of life differences. Three types
of research design are commonly used: independent-group, prospective repeated-
measures, and equivalent-group design. Each of these designs has strengths and
weaknesses in its ability to control for case-mix differences. Meta-analysis has the unique
ability to synthesize the research as well as examine the relationship of study
characteristics, such as case-mix differences, with the magnitude of the treatment
difference in quality of life. Unfortunately, case-mix differences are inconsistently assessed
and reported in the original articles limiting the possibility to incorporate such
considerations in the meta-analysis. The following is a discussion of the problem of case-
mix differences and recomrnendations regarding the assessment and reporting of these
variables to facilitate their examination as covariates for quality of life cornparisons across
RRTs in the original studies as well as in fiiture meta-analyses.
Case-mix differences between treatment groups are important considerations in
comparing quality of life across RRTs if a) the treatment groups differ with respect to
these variables and b) the case-mix variables are significantly related to quality of life. The
results of this meta-analysis suggest that the treatment groups differ with respect to many
case-mix variables (see Tables 7 to 12). Renal transplant recipients tended to be younger;
more likely to be employed; in better physical health; less likely to have experienced a
transplant failure; and have been receiving their current form of treatment for a longer time
compared to diafysis patients, including haemodialysis and peritoneal dialysis. CAPD
patients differed fiom in-centre haemodialysis patients in that they were more likely to be
employed; more highly educated; more likely to be diabetic; and had a shorter duration of
illness or treatment. They differed fiom home haemodialysis patients as they were older;
in poorer physical status; and had a shorter duration of illness and treatment. In-centre
and home haemodidysis patients differed as in-centre patients were more likely to be
female; less highly educated; in poorer physical health; and shorter duration of treatment.
Therefore, there are significant and substantively important case-mix differences between
the treatment groups included in comparative quality of life studies.
The relationship between these case-mix variables and quality life indicators in
ESRD patients is less clear. Usually these relationships are not the prima~y purpose of the
1 IL
research but are examined as potential covariates in comparative quality of life studies. In
this manner, inconsistent relationships between case-mix variables and quality of life have
been observed. For example, some research observed significant gender differences in
emotional distress in ESRD patients (46) while others did not (78). In the general
population, women tend to experience depression more fiequently than men (7,22,34,75).
Until this area of research in ESRD patients is examined in more depth, al1 identifiable
case-mix variables should be examined as potentid alternative explanations for observed
differences in quality of life across RRTs.
In this rneta-analysis, case-mix differences between treatment groups were
examined as potential sources of heterogeneity across effect-size estimates.
Unfortunately, small numbers of studies (range of 7 to 24) compared the different RRTs
regarding emotional distress and psychological well-being and only a sub-set of these
studies provided information regarding the case-mix variables. The most fiequently
reported case-mix variables were gender (80% of the emotional distress and 82% of the
psychological well-being treatment cornparisons) and general physical status (70% of the
emotional distress and 86% of the psychological well-being treatment comparisons). The
least frequently reported case-mix variables were diabetic status (24% of the emotional
distress and 32% of the psychological well-being treatment comparisons) and history of a
previous transplant failure (39% of the emotional distress and 43% of the psychological
well-being treatment comparisons). See the final columns of Tables 13 and 14 for the
availability of case-mix information across al1 treatment comparisons for emotional distress
and psychological well-being respectively. Therefore, there was not an adequate amount
a -
of information to reliably examine the relationship between case-mix and quality of Me
differences across RRTs.
To facilitate the evaluation of the relationship between case-mix variables and
quality of life differences across RRTs in the fùture, the measurement and reporting of
important case-mix variables should be standardized. Case-mix variables can be
considered in terms of at least two categories: sociodemographic and physical status.
Sociodemographic variables include: age, gender, marital status, education, and
employment status. Physical status indicators include: primary renal diagnosis, duration of
illness, length of time on current treatment, diabetic status, history of a previous transplant
failure, and general physical health.
There is wide variability in the assessment of general physical health. In the 59
publications (including 6 1 comparative studies) identified to be included in the meta-
analysis, physical status was measured in many different ways. It was measured directly
( e g , by assessing the nurnber and severity of CO-rnorbid conditions) and indirectly (e.g.,
by recording the number of days spent in the hospital during the past year).
Many studies reported more than one indicator of physical status. Forty-four of
the 61 (72%) studies assessed physical status using an average of 2.3 (SD = 2.08)
measurement instruments. Thirty-four studies (56%) provided information necessary to
calculate effect-size estimates for treatment group differences using an average of 1.8 (SD
= 1.75) measurement instruments. The most commonly reported domain of physical status
was CO-morbidity (e.g, an assessment of intercurrent non-rend physical health problems)
which was measured 13 times but only six studies (46%) provided the information
114
necessary to calculate effect-size estirnates. Serum indices (e.g., creatinine, albumin,
phosphate, and haemoglobin) were commonly measured and usually more than one serum
index was reported per study. Serum indices were reported by 11 studies with five (45%)
providing sufficient information for effect-size calculation. The rate of hospitalization was
reported in eight studies with seven (88%) providing sufficient information for effect-size
calculation. The Karnofsky Performance Status Scale was used by eight studies allowing
effect-size calculation by five studies (63%). Uraemic syrnptoms were assessed by
complex indices (e.g., assessing many of the syrnptoms; (28)) and single-item measures
(e.g., assessing only one symptom, i.e., fatigue; (61)). Seven studies evaluated uraemic
symptoms allowing effect-size calcuiation in four studies (57%). Fifteen studies used one
of 15 other indicators of physical status, including patients' perceived health status; blood
pressure; primary diabetic symptoms; End-stage Rend Disease Severity Index; a global
health rating; and others. Therefore, there is great variability in the assessment of the
general physical health of the research participants.
In addition to there being many different measures used to assess physical status,
some of these indicators are contaminated by psychosocial components ( e g , the
Karnofsky Performance Status Scale). This overlap in the assessment of physical status is
a problem when the researcher is atternpting to evaluate psychosocial differences between
treatment groups while controlling for physical direrences, e.g., this meta-analysis (93).
The End-Stage Rend Disease Severity Index (ESRD-SI) was developed to alleviate this
problem (23). It evaluates the presence and severity of IO illness categories common in
ESRD. This study has good reliability and validity as established in ESRD patients (23).
115
The reporting of case-mix variables is straight fonvard. These variables can be
either continuous (e.g., age) or categorical (e-g., gender). The following case-mix
variables are usually assessed as continuous variables: age, education (years), duration of
illness, duration of treatment, general physical status, and serum albumin. Education is
ofien categorized but providing education as the number of years completed will facilitate
cornparison across countnes with different educational systems. The means and standard
deviations for continuous case-mix variables should be reported for each of the treatment
groups being compared.
In some situations authors may artificially dichotomize continuous variables, e.g.,
to examine quality of life in different age groups. This can be a problem for meta-analysis
for two reasons: a) different cut-off points may be used by different studies and b)
dichotomizing a variable substantially reduces its correlation with another variable.
Therefore, heterogeneity may be inflated if some studies included in a meta-analysis
dichotomize while others do not (54).
The foilowing case-rnix variables are usually recorded in categories: gender,
marital status, employrnent status, history of a previous transplant failure, and primary
renal diagnosis. Gender is recorded as either male or female. The commonly used
categories for marital status include: married/common-law, separatedldivorced, widowed,
or single. The standard categories for employment status include employed, unemployed,
retired, and not working for pay because of other reasons such as volunteering, education,
and home-making. Regarding history of a previous transplant failure, this is often
recorded as the number or percentage of individuals in each treatment groups who have
experienced a transplant failure. The primary renal diagnosis can be categorized using the
most common diagnoses: diabetes, glomenilonephritis, hypertension, polycystic kidney
disease, pyelonephritis, lupus, interstitial disease, unknown cause, and other ( 1 9). For
each of the categorical variables, the number or percentage of individuals in each category
should be recorded for each treatment group. Table 18 surninarizes the present
recommendations conceming the types of case-mix variables that should be assessed and
the manner in which they should be reported.
Comparative studies of quality of life across RRTs are threatened by non-treatment
(i.e., case-mix) differences between the treatment groups which may also explain quality of
life differences across RRTs. Unfortunately, case-rnix variables are inconsistently assessed
and evaluated as alternative explanations for the research findings. Meta-analysis can
provide a unique seMce in that it can examine the relationship between case-mix and
quality of life differences across RRTs. In order to facilitate this examination, detailed
information regarding case-mix variables must be provided for each treatment group. The
information as suggested in the above recommendations must be provided to allow
caIculation of effect-size estimates. Therefore, fbture meta-analyses will be able to
examine the relationship between case-mix and quality of life digerences across RRTs and,
potentially, to control for these differences in the estimated quality of life effects of
alternative RRTs.
Case-Mix Variables Assessed Information to be Reported for each Treatment Group
Sociodemographic Information
-- -
Gender (% male) 1 Marital Status
Married/Common Law Separated/Divorced Widowed Single
Education (years) -
Employment S tatus Employed Unemployed Retired Student/Volunteer/Disability
Physical Status Information
Primary Diagnosis Diabetes Hypertension Glomerulonephritis Pyelonep hntis Polycystic Kidney Disease Lupus Interstitial Disease Unknown Other
Diabetic Status (% yes) n/%
Duration of Illness (months) d S D
Length of Time on Current Treatment (months) d S D
History of a Transplant Failure n/%
General Physical Health
Semm Albumin
rn/SD
m/SD
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Appendix A Data Extraction Form
1. Study Identification Number:
Study Demographics:
Authors:
First Author: a) Medical Doctor - NephroIogist b) Medical Doctor - Transplant Surgeon c) Ph.D. d) Nurse e) Other f ) Unclear
Journal: Year:
Research Funding Source: A) Government Peer Reviewed B) Foundation Peer Reviewed C) Industry D) Non-peer Reviewed E) Un-specified F) Unfiinded
Volume: Pages:
Reference manager number:
Year (s) Data Collected: Number of centres included: Type of centres included (circle al1 that apply):
a) University b) Private Hospital c) Public Hospital d) Other
10. Experimental Design:
Resrarch Design: W i c h type of resarch design was u . d (please circle one)?
a) indepcndent-group dmign b) equivalent-group design
Matchhg variables C ) ~peated-measures - prospective design:
I) dialysis to rmal transplantation ii) transplantation to dialysis (transplant failure) /ii) other modality switch: (spmif~)
Social Science impact score for joumal: is
Response rate for each treatment ~ o u p (recorded on nest page).
1. Was a rcsearch question stated?
2. Were directional hypoîbeses s t a td?
3. Were the hclusion/exclusiori critclia for subject selection iiidicated?
4. Were thm sampliiig conîrols (i.e., stratified or random smpling)?"
5. Werc established mcasurement instruments a~pl ied '?~
Anafysis:
6. ilid the statistical analyses test the hypotheses?
7. Was therc an esplicit strategy for handling missing data'?
Case Mi-Y:
8. Were case niix W e ~ u c e s exauiined betrvam goups?
9. If matnient groups difiéred ou case mix variables were tliey cotitrolled for statistically in îhe analysis?
Coticli~siotzs:
10. Did the couçlusioris correspond closely to the statistical results?
1 1. Was the interpretation of ihe rçsults qualirid by hitat ious of t l ic study?
Yes
Yes
Yes
Yes
Yes
Yes
Y es
Yes
Y CS
Y es
Y es
IJncfear
Unçlear
Uaclear
Unclear
tlnclear
Unclcar
1Jnçlear
IJnclear
Unclear
"StratiTid sample. suçh as selmthg participants by gcnder and age, randornly sampling from patient population, vetsus a sample of coiivenience, such as recruitment of al1 patients in a dialysis centre. I!;stablishcd mçasiuenicnt instnimcnts are widcly uscd in-Ftnunmts witb established rdiability, such as interna1
wnsistency as indicated by Chronbaç's alpha, and vdidity, such as çonsïruçt validity, as rcîierend iu previousIy yublished work or report4 cIirectiy.
11. Case mix - Sociodemographic Information bg Treatment Group:
Treatment group: 1 = Transplant (TP) 2 = Peritoneal Dialysis (PD)
(not diikrentiated) 3 = Continuous Atnbulatory PD (CAPD) 4 = Continuons Cyclic PD (CCPD) 5 = Intermittent PD (PD) G = Haemodialysis (ID)
(not differentiated) 7 = Centre I D (CHD) 8 = Home I-ID (HIID) 9 = Self-care liospital HD (SCHI)) 10 = Self-care satellite ceiitre (SCSC)
Sex: M = Male F = Female
Marital Status: 1 = MarriedKonunon-law 2 = Single 3 = Separated/Divorced 4 = Widowed
Employment Status: 1 = Working for P q 2 = Mornemalier 3 = Unemployed 4 = Retired 5 = StudentNoluuteer
Education Level Completed: i = P r h q Education 2 = Secon(1ary Education 3 = Coiiege DegreeKJudergraciuate Degree 4 = Post-Graduate Degree
Treatment 1 n 1 response rate 1 age 1 gender 1 Marital Status (Ydn) 1 education (yrs or level) 1 employment status (Wn)
nstruçtions: Provide the requested inforniation for each treaîment p u p . See coding manual for instructions.
Subject Sample I I
Case Mis Information I 1 I t
12. Case-mir - Physical Status Information by Treatment Group:
Diagnosis: 1 = Diabetes 2 = Glornenilonephritis 3 = Hypertension 4 = Polycystic Kidncy Diseast: 5 = Pyelonephritis G = Lupus 7 = Intmtitial Disease 8 = Urikuo\ili 9 = Other
Instructions: Provide the requested infornirition for each treatment group. See codiug mmual for complete instructions.
Treatnient Group
Duration of current treatmen t
Previous transplant failure
Diagnosis (% or n) Duration of iilness
13. Case-mia - Othcr Physical Status Information by Treatment Group:
Indicate the measure of physical status uscd and the inean and standard dcviatiou andor statistical cornparison results obsenled for each group. See coding manual for mn~lzte instructions.
Pliysical Status Iudicator IPD CHD HHD SCHD SCSC mi\d 1 d 1 m/sd 1 niisd 1 d o d / misd
Groups cornparcd
Sta 1 Re:
Appendix B
Operational Definition of Psychological Weil-being
Bradbum Mec t Balance - Positive Mec t subscale Campbell Index of Well-being (Overall Life Satisfaction and General Affect) Psychological Adjustment to Illness Scale Index of well-being General well-being index Akinson life happiness Profile of Mood States (POMS) - vigor subscale SF-36 - emotional well-being sub-scale Mental Health Inventory - General Positive Affect subscale Other measures of psychological well-being with established reliability and validity
Operational Definition of Emotional Distress
Beck Depression lnventory @DI) Center for Epidemiologic Studies - Depression Scale (CES-D) Bradburn Mec t Balance - Negative AfFect subscale State-trait Anxiety Index Hamilton Psychiatnc rating scale Profile of Moods States (POMS) - depression, anxiety, and total mood disturbance SF-3 6 - depression sub-scale Sickness Impact Profile - emotional distress sub-scale Bief Symptom Inventory Beck Hopelessness Scale Mental Health Inventory - anxiety and depression Other measures of emotional distress with established reliability and validity
Operational Definition of Quality of Life
O Ferrans and Powers - Quality of Life Index 8 Quality of Life visual analogue scale
Spitzer Quality of Life scale 8 Time Trade-off Quality of Life 8 Other measures of quality of life with estabiished reliability and validity
Appendix C
Effect Sue Calculations and Statistical Analyses
1 . Unweighted or unadjusted effect size:
D, = (M, - M,)/S M, = mean for group 1 M, = mean for group 2 S = pooled standard deviation frorn groups 1 and 2.
2. Computing effect size fiom a significance test (eg t-test) and significance level: (F tests with 1 df in the numerator, substitute the square root of F for t in the following equations (ie. F = t2); F tests with > 1 df in the numerator, use the significance level approach while noting that these results do not represent contrasts)
B, =t Jm (unequai sampie sizes)
D, =21/@f (equale sample sizes)
Significance level:
z r=-
Categorical effect size calculation:
3. Weighted or adjusted effect size - calculations to correct effect size (di) for sample size. di = C,D
4a. Fixed-efFects mode1 variance (within study) of d or D
4b. Random-effects mode1 variance (within and between studies) variance of d or D
5 . 95% Confidence Interval for d or D
d = effect-size estimate k = nurnber of studies v, = fixed-effects (within study) variance
&zi2fi If the confidence interval does not include O, then the effect size is significant at a = .O5
6 . Cornputing overall or surnmary effect size (wi = UV,)
7. 95% confidence interval for d.
If the confidence interval does not include O, then the overall effect size is significant at a = .O5
Or alternatively calculate Z:
Z= Id. 116
if Z > 1.96, the overall effect size is significant at p<.05.
8. Test of Homogeneity (Fixed-effect s model)
If Q exceeds the upper tail critical value of chi-squared at k- 1 df, the observed variance in study effects is significantly greater than expected by chance if al1 studies share a common population effect size.
9. Assessing Publication Bias Fail-safe number (16)
Zi = di / SE,
Guidelines for toferance: k , > 5 k + 10
10. Analysis of Variance for effect sizes Run a weighted analysis of variance using SPSS (Windows), where the weight is the inverse of the variance for the effect size. Use the results of the weighted ANOVA.
1 1. Regression Analysis SPSS (Windows) weighted (using wi = l/v, as a case weight) regression analysis is used with the foilowing corrections: Corrected standard errors for the estimates:
The corrected F test:
Appendix D
Coding Manual For Data Extraction
Comparison of Quality of Life Across Treatment Modalities for End-stage Renal Disease:
A Meta-analysis
A. Research Question This meta-analysis will address whether there are differences in quality of life
across treatment modalities for ESRD as well as assess the extent to which these differences are explained by: a) treatment modality; b) case mix differences between treatrnent groups; and c) differences in methodological rigour across studies.
B. Definitions of Key Research Variables:
Quality of Life: Psychological Well-being and Emotional Distress This research project will focus on the subjective indicators of quality of life, specifically psychological well-being and emotional distress. Psychological well-being includes positive emotions, mood or affect, such as happiness, and the cognitive appraisal of life, as in life satisfaction. Psychological well-being is not merely the absence of emotional distress. Emotional distress encompasses negative mood or affect, such as unhappiness, and negative psychoiogical States, such as depressive syrnptoms and general anxiety. Emotional distress does not require a clinical diagnosis of depression, anxiety, or mood disorder and is not merely the absence of psychological well-being. Psychological well-being and emotional distress are distinct fiom objective indicators of quality of life, such as, vocational, social, and physical rehabilitation, and personality characteristics, such as self-esteem and coping skills.
Treatment Modalities for ESRD: This meta-analysis will compare the standard treatment modalities for end-stage rend disease on quality of life as previously defined. The standard treatment modalities are renal transplantation, haemodialysis, and peritoneal dialysis. Renal transplantation can be living related or cadaveric donation. Haernodialysis can be any of the following types: in-centre (staff care) haemodialysis, self-care-in-centre haemodialysis, home haemodialysis, or satellite-centre haemodialysis. Peritoneal dialysis can be any of the following types: continuous ambulatory peritoneal dialysis, intermittent peritoneal dialysis, or continuous cyclic peritoneal dialysis. Any of these treatment modalities will be included in this meta-analysis. If the treatment modality is non-standard, e.g. pancreas-kidney transplantation, this group will not be included in the meta-analysis.
Case Mix: Patients are not randomly assigned to rend replacement modality and, therefore, treatment groups may differ on non-treatment related variables. These case mix variables include sociodemographic and physical status variables. Sociodemographic variables include age, gender, marital status, education, and employment status. Physical status variables describe the treatment groups according to their primary rend disease (e.g., glomerulonephritis) and general non-renal health (e.g., CO-morbid conditions such as diabetes, hypertension, and infections).
Methodological Rigour: The studies exarnining differences in quality of life across treatment modalities for ESRD differ in degree of methodological rigour. Methodological ngour is an evaluation of the technical merit of an experimental design and has implications for the interpretability and generalizability of the research findings. Experimental evidence can be judged by two criteria, internal and extemai validity. The internal validity of an experiment concerns the extent to which the experiment rules out any possible alternative explanations for the research. External validity concerns the generalizability of the research results to the population of individuals treated for ESRD.
C, Operational Definitions Operational Definition of Psychological Well-being The following are examples of measures assessing psychological well-being:
Bradburn positive affect Campbell index of overdl life satisfaction Psychological adjustment to illness scale Index of well-being General well-being index Atkinson life happiness Profile of Mood States (POMS) - vigour SF-36 - emotional well-being sub-scale Mental health inventory - general positive affect Other measures of psychological well-being with established reliability and validity
Operationai Definition of Emotional Distress The following are examples of measures assessing emotional distress:
Beck Depression Inventory (BDI) / Cognitive Depression Inventory (CDI) a Centre for Epidemiologic Studies - Depression Scale (CES-D) a Bradburn negative affect a Stait-trait anxiety index
Hamilton Psychiatrie rating scale a POMS - anxiety or depression sub-scales
SF-36 - depression sub-scale a SIP - emotional distress sub-scale
Brief symptom inventory Beck hopelessness scale Mental health inventory - anxiety and depression
0 Other measures of emotionaf distress with established reliability and validity
Operational Definition of Quality of Life The following are examples of measures assessing quality of life:
Ferrans and Powers - QOL index QOL visual analogue scale Spitzer QOL scale Tirne trade-off QOL Other measures of quality of life with established reliability and validity SF-36 Total score
Operational Definition of Treatment Modality: The following are the treatment modalities for ESRD to be included in the meta-analysis:
Renal Transplantation (TP) Pentoneal Dialysis, sub-type not specified in study (PD) Continuous Ambulatory PD (CAPD) Continuous Cyclic PD (CCPD) Intermittent PD (PD) Haemodialysis, sub-type not specified in study (HD) In-Centre HD (CHD) Home HD (HHD) Self-care hospital KD (SCHD) Self-care satellite centre (SCSC)
Operational Definition of Case Mix: The following are the case mix variables of interest for this meta-analysis: Sociodemographics:
age gender marital status education level or years obtained employrnent status, in some studies this will be included as a dependent variable
Physical Status: Primary renal diagnosis duration of illness duration of treatment previous transplant failure other physical status indicators, e.g. Kamofsky physical performance, sickness
impact prof le - physical sub-scale, serum indices (i.e., semm creatinine, serum phosp horous etc.), and CO-morbid conditions.
Operational Definition of Methodological Rigour: The following features will be examined to evaluate the methodologicai rigour of each study: a research design: independent group design, matched group design, or repeated-
measures - prospective design (dialysis to transplantation, transplantation to dialysis, or other modality switch)
a citation impact score (social science or science citation impact score for journal) response rate for each treatment group
Design Features: a statement of research question a directional hypotheses a inclusion/exclusion criteria for subject selection a sarnpling controls a use of established measurement instruments Analysis:
statistical analyses test the hypotheses strategy for handling missing data
Case Mix: a case mix differences exarnined between treatment groups a case mix differences controlled for statistically in the analyses Conclusions: a conclusions correspond to statistical results a identification of limitations of the research which may qualifl research results
D. General hstructions for Data Extraction: The following is a guide to assist in the data extraction process. The data
extraction process consists of three main components: describing the study, copying numerical information directly fiom the study, and evaluating the methodology of the study. The description of the study required is the compete reference for the study, including the author, title, publication, etc. Copying information directly fiom the study uses tables for recording the data. These tables have been developed to be flexible to the marner in which the data is presented in the original study. It is important to record the information exactly as it is presented in the study. In rnost cases the tables will accommodate this information. There may be some situations where categorical variables were categorized differently in the study than specified on the data extraction form. In these situations, the definition of the categories can be changed on the data extraction fonn, e.g. if education is categorized differently in the study then the coder can change the existing categories on the data extraction form and record the information in the table. Anytime the categories for a variable are modified, provide the new category definitions on the data extraction forrn. For categorical variables circle n (number) or % (percentage)
to indicate the type of information reported. Also, report al1 statistical cornparisons reported in the article, significant or not.
To evaluate the methodological rigour of the study specific questions addressing different components of the study are asked. As you go through the study you will address these methodological questions and respond 'les7', "no", or "unclear." In most cases answering the questions will be straight forward and in cases where it is not, guidance will be provided in the coding manual. A response of ')les3' would indicate that the item was achieved in the study. A response of "no" would indicate that the item was definitely not achieved. A response of "uncIeary' would indicate that you are uncertain as to whether this item was achieved. For example, in the methodology, results, or discussion section, their may be a suggestion that sampling controls were applied but the author did not state that this was done or how.
E. Instructions: The following are detailed instructions for extracting data from the research studies by question or section.
Identification Number: Assign each study included in the meta-analysis a unique identification number. Authors: List al1 authors last narne folIowed by initiais, e.g. Devins, G.M. First author: Circle the profession of the first author if known. If the author has more than one degree, e.g. M.D. and Ph.D. or nurse and Ph.D., circle al1 that ~ P P ~ Y +
Title of article: Speci* the fidl title. Journal reference: Record journal narne, volume, and pages in appropriate places. Year of publication: Record reference year of publication. Research funding source: Record the source of fùnding for the research, if available, and circle the correct category, if known. Refman #: Reference manager file number for the study to be provided at a later date. Years data collected: For how many years and for which years was the data collected? Speci@ if known. Number of centres: How many treatment facilities, i.e., hospitals or treatment centres, participated in the research? Speci@ if known. Type of centre (s): Please circle al1 of the types of centres that participated in the research, if known.
10. Experimental design: The following section assesses the methodological rigour of the research design.
Research Design: Please circle the type of research design used. a) Independent group design, e.g. different people are in each of the treatment groups reported. b) Matched group design, e.g. the research participants in one treatment group
were matched to individuals in another treatment group on certain key variables. Specie the variables used in matching. c) Prospective repeated-measures design. One group of subjects were followed over time, e.g. assessinç qudity of life while patients were on dialysis and then again after they had received a transplant. SpeciQ the modality switch.
Social Science and Science Citation Impact Score for Journal: This information will be obtained at a later date.
Design Features: Please indicate for each of the following design features if they were achieved in the article. "Yes" wouid indicate that the design feature was achieved, "No" woufd indicate that the design feature was not achieved, and 'Vnclear" would indicate that you are uncertain if the feature was achieved or not.
Was the research question or research purpose stated? Were there directional hypotheses? e.g. We expect treatrnent group I to report a better quality of life than treatment group 2. Were there any inclusion/exclusion criteria for subject selection? e.g. Patients were approached to participate if they had been receiving treatment for ESRD for 3 months or more and if they did not have a history of mental illness. Were sampling controls applied? Sarnpling controls are used to select a research sample that optimizes the internal or external validity of the study. An example of sampling controls is stratified sampling, e.g. randomly selecting participants by gender and age from the patient population to resemble the population of individuals with ESRD. This is in opposition to a sample of convenience, such as recruitment of al1 patients in a dialysis centre. Were established measurement instruments applied? Established rneasurement instruments are widely used insttuments with established reliability, such as internal consistency as indicated by Chronbac's alpha, and validity, such as construct validity. Reliability and validity information may be provided by referencing previously published work or reporting directly. Did the statistical analyses test the hypotheses? e.g. If the hypothesis indicated a comparison of treatrnent groups on key variables were al1 of the cornparisons carried out? Did the author (s) present a strategy for handling missing data? e.g. for cases with missing data, were the cases excluded fiom the analyses or was a method, such as rnean substitution, used to replace the missing information? Were case mix diflerences exarnined across treatment groups? e.g. were the groups statistically compared on sociodemographic or physical status variables? Were case mix differences controlled for in the analyses? e.g. if case mix differences existed between groups, were these differences controlled for statistically by doing a procedure such as an analysis of CO-variance? Did the conclusions correspond to the statisticai results? e.g. did the conclusions discuss the statistically significant differences and the non-significant findings?
10.1 1 Were the research results qualified by limitations in the research? e.g. did the author acknowledge any potential weakness of the research which may necessitate qualifications in interpreting the results?
11. Case Mix - Sociodemographic information. Please complete the following table for the treatment groups reported in each study. Each row in the table corresponds to one of the ten possible treatment modalities for ESRD. Tnitials for each treatment modality are used in the table to economize on space. The fiil1 treatment modality name, with initiais, is presented before the first table. For each of the studies at least two treatment groups will be reporied (except in repeated- measures prospective design studies). Record the requested information in the appropriate row corresponding to the treatment group. For categorical variables, circle the n (number) or % (percentage) to indicate what type of information was recorded. n, Record the number of individuals (n) in each treatment group. Response rate, i.e., the number or percentage of individuals who participated in the research fiom the pool of individuals contacted for participation. Age, record the mean and standard deviation for each treatment group. Gender, record the number or percentage of each treatment group that are men or women. Circle % or n to indicate the type of information recorded. Marital status, record the number or percentage of individuals in each treatment group that are in each of the marital categories presented. Circle % or n to indicate the type of information recorded. Education, may be reported in years or by level completed. If education is reported in years, record the mean and standard deviation for the number of years completed for each treatment group. If education is reported by level completed, record the number or percentage of individuals in each treatment group in each of the identified categories. Circle % or n to indicate the type of information recorded in the categories. Employment status, record the number or percentage of each treatment group that are in each of the employment categories recorded. Circle % or n to indicate the type of information recorded. Please note that this information rnay appear as a dependent variable in some of the studies.
Case mix - Physical status information. The same procedure as in step 1 1 should be used here. Diagnosis, record the number or percentage of each treatment group that are in each of the diagnostic categories presented. Circle % or n to indicate the type of information recorded. Duration of illness, record the mean and standard deviation for each treatment group and indicate if the duration is in months or years by circling m (months) or y ( years) . Duration of treatment, record the mean and standard deviation for each
treatment group and indicate if the duration is in months or years by circling m (months) or y (years). Previous transplant failure, record the mean and standard deviation for the number of transplants experienced by each treatment group or record the percentage of individuals in each treatment group that have experienced a previous transplant failure. Circle d s d , n, or % depending upon which type of information is recorded.
13. Case mix - other physical status information. SpeciQ the measure used (see operational definition for physical status indicators).
For each treatment group, record the mean and standard deviation for each rneasurement instrument. If the groups were compared statistically, record the statistical results, including the test statistic, degrees of freedom, and significance level, e.g. t = 3.24, df = 34, and p<.O 1 . Also indicate the treatrnent groups being compared to achieve that statistical result, e.g. transplant versus in-centre haemodialysis. If a cornparison was made and the result was non-significant, record this information as well. In some studies the variables will be categorized instead of presenting means and standard deviations. For these studies record the categories and the number or percentage of individuals in each treatment group that fa11 into each of the categories.
14. Dependent variables: Emotional Distress, PsychoIogicat Well-Being, and Quality of Life Using the operational definitions provided earlier for each of these dependent
variables and the sarne procedure as part 13 (case mix - other physical status information) complete this section. If there are any indicators of emotional distress, psychological well- being or quality of life reported in the studies that are not included in the operational definitions, report these measures in the table as well.
F. Comments: This section can be used for any purpose. Any comments which can assist in the understanding of the study or the measurement instruments used can be recorded here.
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