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Choosing Treatments
A Ph.D. thesis on preference elicitation from idea to implementation with
applications to health care decisions on treatment of low back pain
Mirja Elisabeth Kløjgaard
University of Southern Denmark
Faculty of Health Sciences 2014
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Foreword
You’re a difficult equation with a knack for heart evasion
Will you listen to my proof or will you add another page in
It appears to me the graph has come and stolen all the laughs
It appears to me the pen has over analysed again
- Sia “Academia”
I’ve never really struggled deciphering agendas or motivations - but academia is something else.
It is a world of ancient cultural behavioural patterns, which for an outsider - or beginner insider - can
seem irrational and unpredictable. These towers of knowledge that we call universities, leaves its
occupants so dedicated, driven and focused that they forget to look out of the window unto the world
outside. And as the air gets thinner towards the top of the tower, some even forget why they started
climbing its stairs to begin with, and stops remembering the steepness of the lower floors and the many
desperate climbers.
My navigation in academia has been disturbed by trials and detours along the way, but I’ve managed to
find some invaluable guides on my sometimes vertical climb. My helpers have pointed me in the right
direction at the right time and I would have never made it this far without them.
Thanks have to be directed to my changing team of supervisors from whom I’ve learned different
aspects of how to navigate the academic world. To Rikke Søgaard for never hesitating to push me
forward onto the narrow path of good academic behaviour, always in the quest of nothing but results
and never weary in attempts to get them. To Jan Sørensen for teaching me valuable lessons on the ever-
changing faces of academia. And last, but so definitely not least, to Mickael Bech for staying until the
bitter end and for abilities in putting out fires without starting too many new ones.
Thanks should also be directed to Professors Benny Dahl from Rigshospitalet and Tom Bendix from
Glostrup Sygehus for letting me do fieldwork in their hospital wards. To Jens Olsen who was my boss for
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the majority of my employment as a PhD student, for supporting my ideas and especially for co-
financing the Danish Choice Modelling Days.
A special thank you goes out to Professor Claus Manniche and staff at Middelfart Spine Center for
helping me collect my quantitative data and be thorough and professional while doing so.
I’ve also been blessed with help and advice from some major external capacities and thanks should be
directed to them too.
To my superhero, Professor John Rose. For letting patience and teaching abilities to the test, while
helping me understand and achieve my potential. For helping me remember what’s important in life –
and to breathe, when I didn’t. For following me - for a change - coming all the way to Denmark to shine
with glory upon the Danish Choice Modelling Day(s). And for being the most childish, but best Aussie
“rock” star ever. I’m proud to be an appointment groupie.
To Professor Stephane Hess for not judging me on that first email to “Stephanie”. For helping to make
the first ever Danish Choice Modelling Day into more than a silly idea, and for knocking at least some
econometric knowledge into my utterly imperfect head. For listening to my worries and insecurities and
make them seem less important – or at least more normal. For caring. And for taking time; to slow down
to enable me to (try to) follow, to comment on my work, answer my emails, run models, write papers,
correct my calculations - and to have fun!
To Rob Sheldon for sharing his taxi and offering me a stay at his company Accent Marketing Research
following that. To everyone else at Accent MR for making that stay a success and educational and fun!
Finally, a heartfelt thank you goes out to my colleague – and partner in crime - Line Bjørnskov Pedersen.
Had we not been sitting on that quay in Venice, suffered through the British summer, shared those
magic days and fractured bones at Disney World, eaten strawberries like supermodels, lived in style
under the Harbour Bridge or simply spent all those hours discussing, I would have never been able to be
where I am now. Thank you for never doubting me and always reminding me of what I’m good at. For
picking me up from the deep black holes and bringing me back down to earth when I flew too high.
Thank you for climbing the stairs with me, overcoming the biggest obstacles, yet still stand beside me.
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The sprint to the finish line was made so much easier with the support of all my cheerleaders of family
and friends, led on by a very special man, who always seems to surprise me with his capabilities. My
journey must have seemed beyond imaginable, but I appreciate the honest attempts of trying to
understand and all the help which has been given me. Whatever form it took - free meals, hour-long
phone conversations, bottles of wine or hugs when needed – the support is remembered and cherished.
Having the ability and possibility to aspire for a high-level of education and knowledge is a more rare
combination that what we might think having already achieved it. I often think of my luck having been
supported while taking my skills to the highest possible level. I don’t owe my accomplishments to rich
parents or extraordinary talent. I owe them to a system in which I deeply belief. I hope to someday
somehow return my debt.
All those thank you’s and deep thoughts aside, all there is left is to offer happy reading.
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Index
Foreword ....................................................................................................................................................... 2
1 Introduction ............................................................................................................................................... 7
1.1 Motivations ......................................................................................................................................... 7
1.2 Research questions ............................................................................................................................. 8
1.3 About back pain ................................................................................................................................ 10
1.3.1 Definitions .................................................................................................................................. 10
1.3.2 Low Back Pain (LBP) Patients ..................................................................................................... 11
1.3.3 Pathogenesis and treatment ...................................................................................................... 11
1.3.4 Danish Guidelines and policy-movements ................................................................................. 12
1.3.5 Patient pathways and decision making in a Danish context ...................................................... 13
1.4 About preference elicitation in health economics............................................................................ 13
1.5 Contributions of this thesis ............................................................................................................... 15
1.5.1 Thesis outline, detailed contributions and relation between background, empirical work and
discussions .......................................................................................................................................... 16
2 Methods and existing knowledge of preferences and LBP ...................................................................... 18
2.1 Stated Preferences ............................................................................................................................ 19
2.2 Preferences for treatment of Low Back Pain .................................................................................... 21
2.2.1 Patients’ preferences for treatment of LBP ............................................................................... 24
2.2.2 Patients’ preferences for shared decision making and HCPs characteristics or service delivery
............................................................................................................................................................ 25
2.2.3 Patients’ preferences’ effects on treatment outcome .............................................................. 26
2.2.4 Conclusions from literature ....................................................................................................... 27
2.3 Methodology ..................................................................................................................................... 28
2.3.1 Theoretical base for choice modelling ....................................................................................... 28
2.3.2 Designing a stated preference choice experiment .................................................................... 30
2.3.3 Statistical analysis ...................................................................................................................... 37
3 Empirical work.......................................................................................................................................... 41
3.1 Formulating the research question ................................................................................................... 41
3.2. Selecting attributes and levels ......................................................................................................... 41
3.3 Construction of tasks ........................................................................................................................ 43
3.3 Experimental Design ......................................................................................................................... 45
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3.3.1 Final experimental design .......................................................................................................... 45
3.4 Preference elicitation and instrument design .................................................................................. 46
3.5 Collection of data .............................................................................................................................. 46
3.6 Statistical Analysis ............................................................................................................................. 47
4 Designing a stated choice experiment. The value of a qualitative process ............................................. 48
5 Patients’ preference for treatment of low back pain – a discrete choice experiment .......................... 101
6 Understanding the formation and influence of attitudes in patients’ treatment choices for lower back
pain: testing the benefits of a hybrid choice model approach ................................................................. 101
7 Discussion and future perspectives ....................................................................................................... 102
7.1 Results and existing knowledge ...................................................................................................... 103
7.1.1 Limitations ................................................................................................................................ 103
7.2 Policy implications .......................................................................................................................... 108
7.2.1 Putting preferences to practice ............................................................................................... 108
7.3 The patient and the medical experts .............................................................................................. 109
7.3.1 Who is sharing decision-making? ............................................................................................. 111
7.4 Methodological issues .................................................................................................................... 113
7.4.1 Are we utility-maximising?....................................................................................................... 113
7.4.2 Heterogeneity .......................................................................................................................... 114
7.5 Concluding remarks ........................................................................................................................ 115
8 Conclusion .............................................................................................................................................. 116
9 Summary ................................................................................................................................................ 118
10 Dansk Sammenfatning ......................................................................................................................... 124
11 References for sections 1, 2, 3 and 7 ................................................................................................... 128
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1 Introduction
This thesis is a collection of journal manuscripts and an independent overview that adds to the
clarification of the issues dealt with throughout the PhD studies and sets a frame for the used methods.
The independent sections surrounding the included papers, includes a literature review of existing
knowledge on the topic of preferences for treatment of back pain as well as a critical discussion of the
gained results put into a context of novel research approaches in the field of modelling choices,
including suggestions of future research areas, naturally following the presented results.
This section begins with underlining the initial motivations for the field of study. It further introduces the
context of the health issue of back pain and clarifies objectives of the thesis and of the papers presented
in later sections.
1.1 Motivations
Back pain is major problem in most of the western world. According to the Danish National Health
Interview Survey, more than 35 % of the adult population suffer from back pain of different severity and
length 1. It is the third most reported deficit and is one of the most common reasons for absenteeism or
early retirement. Not only is back pain a major issue as it is now, there’s a tendency of increasing
prevalence and impact. Although most patients are treated outside the health care system a substantial
amount of patients are referred from general practitioners (GPs) to the hospital sector. This picture is
not only seen in Denmark, but is common throughout the western world.
A series of studies in- and outside Denmark has shown substantial costs associated with back pain for
patients, health care systems and society 2,3. Studies also indicate that treatment of back pain is
influenced by a range of challenges. Firstly, conflicting evidence with no clear indication of optimal
treatments has left health care practitioners (HCPs) with difficult choices. This has also been a factor in
observed variation in treatment strategies throughout the (western) world. Possibly as a result of the
lack of clear evidence-based guidance, perceptions of and attitudes toward patients and their disease as
well as the feasibility of treatments, has been shown to impact HCPs’ decision-making 4–14. The typically
long patient pathways involving numerous health practitioners, possibly affects decision making and
agency-relationships by adding to patients’ perceptions of optimal treatments and potential outcomes.
Secondly, it has been argued that patients’ perceptions and preferences can impact treatment outcomes
and should be incorporated systematically into decision making in the case of back pain – and possibly in
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similar cases – as preferences can help guide shared decision making ensuring better results. But to be
able to do so, knowledge on patients’ preferences is vital 15,16.
This Ph.D. study provides knowledge on preferences and drivers of preferences by using state-of-the-art
methods and by investigating the area of interest thoroughly and with novel approaches. It focuses on
eliciting not only patients’ preferences for treatment of back pain with help of both qualitative and
quantitative methods, but also looks into how perceptions and attitudes affect treatment choices. The
studies presented in the thesis give basis for both methodological developments as well as practice-near
development and help to fill gaps of knowledge.
Firstly, a series of introductory sections and the research questions and the scope of contribution are
presented. The remainder of this chapter is organised as follows: Section 1.2 clarifies and describes the
objectives of the Ph.D. study and hence this thesis. Section 1.3 further motivate and elaborate on the
field of study in an international and Danish context and provides a range of definitions. Section 1.4
presents how preference elicitations and in particular stated preferences (SP) are being and have been
used in health economic, providing the background for section 1.5 dealing with the contributions of this
thesis, the links between the empirical contributions (sections 4,5 and 6) and how the empirical work fit
into the current practices of SP in health economics and gives an overview of the sections and outline of
the thesis.
1.2 Research questions
The objective of the PhD study has been two-fold. Firstly, it was the aim to explore the preferences of
patients with back pain when facing a choice of trade-offs between treatment options, outcomes and
risk. Secondly, it was an objective to add to the methodological work in the field of preference elicitation
by aiming at contributing with added focus on qualitative work prior to gathering quantitative data on
preferences and by focusing on the complex formation of preferences for patients.
More specifically the project aimed at:
1. Investigating patients’ preferences for treatment of low back pain (LBP), and
2. Contribute to the field of preference elicitation in health care in two distinct ways.
a. By exploring how qualitative work prior to designing stated preference experiments has an
impact on each step of the design process, and
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b. by exploring the importance of and the process by which attitudes and perceptions are
influencing the choices made by patients
The objectives of the PhD study are met in different ways. A study was performed to gain knowledge on
preferences to be able to include these in the decision-making processes about optimal treatment.
Doing so, is becoming more acceptable among doctors because knowledge about the patient’s general
expectations and preferences can guide the choice of treatment and, in some cases, even improve the
outcome of treatment as is described in section 2.2.3. Hence, quantifying preferences and exploring
trade-offs may be very helpful for patients and HCPs. The first research question is answered by a review
of literature presented in section 2.2 and empirically in section 4 and 5. The results are obtained using
stated preferences methods, which makes respondents state preferences by investigating their choices
of hypothetical, but realistic scenarios. The method is presented in section 2.1 and elaborated on in
section 2.3. Making investigations using stated choices requires solid work prior to quantitative data
gathering. This is agreed upon by researchers in the field, yet overlooked in literature 17. The work not
only helps ensuring a good and valid design, but also informs the experimental design by producing prior
knowledge on preferences, an approach widely used in the field, especially if sample sizes can be
expected to be low. The research question 2a. dealing with informing designs by qualitative work is
dealt with in sections 3 and 4. Existing knowledge on LBP patients and in stated preferences research
shows a great need for investigating differences in preferences in patients. These differences should not
only be acknowledged, but also investigated in regards to what drives them. This helps enlighten how
different individuals can be helped to receive the best possible and individualised treatment based on
the most informed prospect from both the patient and HCP. Modelling decisions in light of attitudinal
driving factors has yet to break through on the health economic scene, and the research question not
only provides interesting empirical knowledge on preferences, differences in preferences and factors
driving these, but also provides a highly usable and novel methodological contribution. The research
question 2b. is mainly dealt with in section 6, but also gave rise to a range of topics of discussion dealt
with in section 7.
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1.3 About back pain
Before introducing a detailed outline of the thesis, this section presents a range of definitions of some of
the terms used in this thesis and provides insight into the health issue of low back pain. This section also
shortly introduces the area of back pain in a Danish context. It introduces the group of patients,
international evidence for treatment and the Danish guidelines in the area. It also touches upon patient
pathways in Denmark and in presents a framework in which decisions on treatments can be seen.
1.3.1 Definitions
Back pain is an undefined term covering a range of different disorders. Suffering from back pain is not
always diagnosable which makes it hard to establish accurate measures of prevalence which is
completely dependent on the definition used. Hence a number of terms are used to characterize back
pain. With aging the spine changes and discs degenerates, which for the majority of people does not
cause any pain or problems. The changes in the spine are highly individual and degenerated discs can be
observed in young people and be without symptoms, while the same degenerations might cause severe
symptoms for others. One term used for back pain is thus degenerative disc disease, which is the term
used in the paper in section 4. However, as disc degeneration is not always present in patients with
lower back pain, non-specific low back pain is a broader term used to characterize all patients with pain
in the lower back that cannot be attributed to any recognisable known specific pathology i.e. infection,
tumour, osteoporosis, fractures, deformities etc. Hence this term more precisely characterise the
patients studied in this thesis, as a specific pathology other than normal degeneration was not present
in the patients. The change of term from section 4 to the following sections is simply a mirror of being
more precise in the spine-specific terminology.
A wide range of HCPs are dealing with the patients with lower back pain. These include doctors, like
neuro- and orthopaedic surgeons and rheumathologists as well as x-ray/MRI scan technicians etc. but it
also includes physiotherapists, masseuses, chiropractors, pain-coaches etc. Hence the term used to
characterize the group of therapists is HCPs.
When dealing with low back pain, which suffers from the imprecise terminology extrapolation of
research is also made difficult. This along with similar factors of challenges in defining patient groups,
exact content of (especially non-surgical) treatment, referral and care-taking differences and similar area
variation, makes comparison of studies very difficult 15. Hence the results presented in this thesis
primarily represent the setting in which they were collected and the patients who answered the
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questionnaires. Although this is a limitation of scope, it is no different from all other research performed
in this field.
1.3.2 Low Back Pain (LBP) Patients
Most people will experience low back pain in their lifetime. Although a lot of resources are spent within
the health care system on patients with back pain, most people suffering from back pains do not seek
assistance from doctors or other HCPs. Some evidence point to perceived disability as the main driver
for seeking care 18. Back pain patients come in all age groups with similar prevalence in children and
young adults as later on in life 19. But number of back pain related consultations rise with age 20,21.
Negative effects on ability to work and pursue everyday activities as well as limits to quality of life is
more commonly reported in adults and elderly people 22,23 and although many patients experience
recovery or periods of no symptoms, LBP is characterised by its recurring nature 24–26. Some literature
point to LBP severity being connected to socio-economic factors as higher ratings of pain severity and
disability is somewhat associated with employment status, self-rated health status and use of health
care services 27.
In Denmark, about 20 % of the adult population answer they suffer from back pain while 11 % in this
group claim to have actual spine disease 1. There’s a clear tendency of lower socio-economic status and
LBP 1 and a strong connection between low self-assessed health and LBP 28. People suffering from LBP
are more likely to be smoking and women 28. People who experience LBP have a substantial overuse of
health care resources compared to the normal population, especially concerning GP visits and hospital
treatments. An extensive use of out-of-pocket treatments from HCPs like chiropractors or
physiotherapists and prescriptive medicine is also detected. Further a production loss due to LBP-related
sick-leaves is estimated to be between 1.8-2.6 billion DKR/year – not including costs of pensions due to
LBP.
1.3.3 Pathogenesis and treatment
The exact factors causing acute low back pain remain unknown. Some patients have modifications or
transformations of their spine which could explain symptoms and literature has found an association
between degeneration of lumbar discs and pain 29,30, but as similar changes to the spine can be observed
in symptom-free persons it cannot be the full explanation 31. Some patients do not have apparent spinal
changes, and clinical examination often does not explain reported pains and effects 32.
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Some literature point to different genetics factors as possible explanations for back pain and disc
degeneration 33–39 but heredity is still widely discussed 40,41. Similarly lifestyle factors like smoking and
obesity have shown some connection to low back pain 42,43 while lifestyle factors related to working
position, lifting or carrying or other physical activities have not been proven to be causal to LBP 44–51.
Patients’ attitudes and beliefs toward LBP 52 or lack of appropriate coping 53 has however been
associated with chronicity and severity of impact of symptoms. Similarly studies have shown that HCPs
are also directed by beliefs and personal opinions when advising patients, which is also indicated as a
driver for surgery rates and the dominant variations of rates 9,54–56.
When deciding on what treatment to provide, patients with LBP is often scanned using MRI 57 despite
decisions not being taken based on scan results 58. Treatment is often commenced with some sort of
patient education on staying active etc. 59,60 along with pain-killers 59. This is followed up by cross-
disciplinary care, patient education and rehabilitation or sometimes surgery 15. The use of surgery in the
treatment of LBP has been widely discussed and rates are generally considered to be too high in most
settings 10,61. The critique of use of surgery in the treatment of LBP considers the lack of evidence
showing superiority of surgery compared to non-surgical interventions 62,63 but studies comparing the
two treatment modalities are often biased due to unclear selection criteria and substantial cross-over of
patients, which is in some cases caused by overrated perceptions of advantages of surgery among
patients 62,64,65.
1.3.4 Danish Guidelines and policy-movements
In Denmark, the Ministry of Health and the Danish Regions, who are in charge of hospital care, revised
the guidelines for treatment of back pain and spine disease in December 2010. The revision was
motivated by the increasing surgery rates and underlined a need for prioritization and improved referral
practices 66. The guidelines underlines that the unclear evidence and equal results upon 1-year follow-up
of surgical and non-surgical treatment options, makes choosing the right candidates for surgery central.
Indication for surgery is defined as a combination of s described and observed symptoms, anamneses
and MRI-scans. Even if the indication is positive, the guidelines underlines that all non-acute and no red-
flags patients need to test non-surgical options in 6-12 months before surgery should be performed 66.
Following the guideline revisions, the Regions committed themselves to ensuring at least a 3 month
non-surgical treatment of patients with low back pain. The diffusion of this treatment option has yet to
reach all Regions but the Region of Southern Denmark, in which the empirical data presented in this
thesis has been collected, had already formulated guidelines complying with the Ministries’67. The
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revised guidelines does not include considerations of patient preferences, but it is evident that an
essential shift in treatment pathways and practices calls for an investigation on how the patients and
HCPs involved make decisions on treatments and what preferences in relation to the decision-making
are at stake.
1.3.5 Patient pathways and decision making in a Danish context
In Denmark, patients with back pain generally have long pathways and try a range of different
treatments, often incurring substantial (out of pocket) costs. Patients can be referred to specialists or
hospital via their GP. They can also pursue a range of therapists like chiropractors, physiotherapists etc.
mostly covered out of pocket. If a patient has an additional, supplementary private health insurance,
they can sometimes skip the specialists or therapists or even the gatekeeping GP and receive treatment
on a private hospital or with a private specialist, who again can refer to any hospital. The patients who
serve as respondents in the studies included in this thesis were captured at a large, public spine clinic.
The patients had had long treatment pathways, but had not yet been treated at hospital level and were
unaware of hospital experts’ judgment on their disease and diagnosis. Hence, the patients had not
engaged in decision-making regarding treatment at a hospital yet.
1.4 About preference elicitation in health economics
SP is often used when where market choices are severely constrained by regulatory and institutional
factors, such as in health care 68. But since SP methods are used in a range of research fields which have
all used different terminologies, often for the same method or feature. Terms like conjoint
measurement, stated preferences, discrete choice-modelling, discrete choice conjoint analysis, paired
comparisons, choice experiments, pairwise choice or discrete choice experiments are all used for the
preference eliciting method used in this thesis. Throughout the thesis the term “stated preferences“ is
mostly used but the literature used to enlighten the methodology stems a wide range of fields of
research.
Studies of SP using choice modelling methods have increasingly won impact in health economic
research. The SP approach has grown rapidly in health economics in the past years and its usage is more
widespread than ever before, both geographically and with regards to the health related questions it
tries to answer 17.
A review by de Bekker Grob et al. 17 shows that the development in the use of SP in health economics
from 1990-2000 (baseline) compared to from 2001-2008 has been mostly in terms of geographical
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spread of researchers applying the methods, use of more and more advanced software packages
creating choice sets and more use of more advanced models investigating choices. The fields of
application has also grown from a more specific focus on patient’s experiences to where applications
include the valuation of labour-market choices for HCPs, utility weights for QALYs and clinical decision
making and priority-setting. The review also shows that designs and models are still fairly simple and
mostly doesn’t allow for differences in taste and focus solely on main effects.
Updating the search from de Bekker Grob et al.17 using the same search terms, same database and
limitations shows that the tendency of more applications is still true. The search in de Bekker Grob et al.
17yielded 682 possible references with 114 meeting inclusion criteria, while the updated search,
presented in table 1, gives 992 results with 279 references meeting inclusion criteria. Thus it seems fair
to conclude that the number of applications of SP-methods to health care is still rising.
Table 1. Literature search on SP in health care.
Search string in Medline Limitations Results
(((((((((discrete choice experiment) OR discrete choice experiments) OR discrete choice modelling) OR discrete choice modeling) OR stated preferences) OR stated preference) OR conjoint analysis) OR conjoint choice experiment) OR conjoint studies)
2008-2014 Humans
992 Sorted on abstracts (using inclusion criteria: Health care and not eg. Food or nutrition or traffic safety AND attribute-based) = 279
The updated search also show that the applications topics are still manifold and the work on e.g.
estimating utility weights for QALYs is still growing 69–78 as is the work focusing on HCPs preferences for
jobs, especially in rural settings and in Africa 79–84. Also, studies on priority-setting is still increasing 85–89
as is the health topics covered including diabetes 90–100, cancer 101–105 and end-of-life care 106–108.
More advanced models taking heterogeneity into account, especially mixed logit models, are more
commonly used 83,91,104,109–124 as is Latent Class models 75,125–144. Other methodological advantages are
also being explored more often, e.g. looking into different compensatory rules 145, number of choice sets
146 and cognitive ability to engage in SP-studies e.g. among mental ill 147 etc.
A range of studies state the use of qualitative methods to inform design 70,78,106,118,123,148–158.However, no
studies in the updated search are concerned with exploring how the qualitative work actually changes or
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guide the design and although 3 studies deal with the topic of back pain 159–161, one deal with scoliosis 161
one with lumbar surgery 159 and only one directly with LBP 160.
The empirical work in this thesis thus builds on what has become a growing field of research in health
economics and adds in depth knowledge of qualitative methods and how they can guide designs of SP-
studies, more practical and empirical knowledge of patient preferences for the major public health issue
of LBP and by taking the methodological development already happening to the next level in terms of
more advanced modelling.
The contributions of the thesis are presented in more depth in the following section.
1.5 Contributions of this thesis
This thesis makes several contributions to the field of modelling health care choices in general and
preferences in regard to treatment for low back pain in particular. As seen in the previous chapter the
use of SP-methods in health is growing and more effort is put into not only providing knowledge of
preferences, but also on methodological issues. This thesis already provided an overview of updated SP-
work in health and further provides a thorough and systematic review of the literature and existing
knowledge of the context in which the empirical work presented in thesis is part. Thus, it becomes
apparent how the contributions are part of a broader context while providing useful insights and novel
techniques. It builds on existing knowledge by adding an important layer and contribution to the
literature on preference for treatment of LPB and it extends the modelling capacity of future health care
researchers wanting to pursue looking into preferences by introducing the Integrated choice and latent
variable/hybrid models in health care SP-research and by exploring the potential benefits of this model.
The thesis builds it empirical work on the generic step-wise process by which SP-projects are usually
done, and focuses on the importance of qualitative work and how this informs designs and guide initial
steps in the process with great advantages. This is done in a cohesive process, where one step leads to
another - as is the case for the empirical chapters, with the first paper presenting the qualitative work
that went ahead of the design used to elicit preferences in the second and third paper, and with the
second paper looking into patients’ preferences for LBP with some depths, further elaborated upon in
the third paper, which also presents and discusses the benefits of highly novel modelling approaches.
Lastly this thesis discusses some of the present and forthcoming issues important for future work of
preferences in regards to LBP and SP in general.
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The next section presents the outline of the thesis and details on the contributions
1.5.1 Thesis outline, detailed contributions and relation between background, empirical
work and discussions
The thesis contains 10 sections. The first 3 sections deal with the background and methods while section
4-6 presents the empirical results in paper-formats. Finally section 7-10 contains discussion, conclusion
and a Danish and English summary.
Section 1 has the purpose of presenting the research area of interest and provides the reader with
thorough and systematic background knowledge to facilitate an understanding of the challenges of
treatment of low back pain and the context in which decision are made. It highlights the present and
historical use of studies on preferences in health care and the contributions of the thesis and
interdependency of the three empirical contributions.
Section 2 provides a systematic and thorough description of existing knowledge of preferences in
relation to LBP and thus helps to answer the research question of eliciting preferences for treatment of
low back pain, based on existing knowledge. It further describes how preferences are hypothesized to
influence or shape decision-making or even outcomes of treatments. Section 2 further provides a
deeper description of the stated-preference-elicitation method of choice modelling based on a step-
wise process of generating SP-studies and including an introduction to qualitative methods used in that
process.
Section 3 introduces empirical details on each step of the process, highlighting the use of qualitative
work that went into the process, and thoroughly describing all design measures taken in the empirical
work from the formulation of a research question to statistical analysis.
Section 4 starts the empirical contributions with the first paper presented in the thesis. The paper is
about the design of a stated preference or discrete choice experiment, which involves a process of
developing, testing and optimizing the experiment questionnaire. This process is important for the
success of the experiment and the validity of the results, but it is often not reported thoroughly. The
paper demonstrates how qualitative work can significantly impact and guide a design, making it clear
that a less thorough qualitative process would have resulted in a less useable and valid design. It reports
on fieldwork in clinical departments in Danish hospitals supplemented by qualitative interviews with
patients and doctors to determine relevant attributes and levels for the design.
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The paper provides a methodological contribution to the field of designing stated preference
experiments by answering the research question on how qualitative work prior to designing stated
preference experiments have an impact on each step of the design process.
Section 5 presents the examination of patients’ preferences for treatment of LBP. The paper build on
the design developed by the process described in sections 2, 3 and 4. It argues that since treatment
decisions are distorted by conflicting evidence the inclusion of patient preferences in decision- and
policy making should be emphasized. It builds on results from the DCE conducted across consecutive
patients referred to a regional spine centre. Respondents (n=348) were invited to respond to 6 choice
tasks each presenting two hypothetical treatment options and an opt-out option. Treatment attributes
included treatment modality, risk of relapse, pain reduction and increases in ability to perform activities
of daily living. In addition, waiting time to treatment effect was used as a payment vehicle. Analysis was
performed using Mixed Logit models. A sub-group analysis further explored the willingness to wait by
dividing respondents into socio-demographic and disease related categories.
As expected, results show that respondents assign positive utilities to positive treatment outcomes and
disutility to higher risks and longer wait for effects of treatment. Respondents also showed a disutility
towards surgical intervention and were on average willing to wait two years for effect of treatment to
avoid surgery, while they were willing to wait almost three years for effect of treatment, if the
treatment made them free of pain. The mixed logit model captured significant heterogeneity within the
sample for outcomes regarding pain reduction and ability to pursue activities of daily living and in
relation to treatment modality. The sub-group analysis showed differences in willingness to wait,
especially in regards to treatment modality and particularly in groups of high/low pain at time of data
collection and respondents with an ex-ante preference for surgery/non-surgery. The results indicated
that pain relief is the most important factor in choice of treatment, followed by treatment modality and
ability to perform activities of daily living while risk of relapse was the least important factor. This is not
only valuable information for practitioners in their communication with patients and everyday decision-
making, but also important information for policy makers who seek to ensure a utility-maximizing mix of
treatment options for patients. The section empirically answers the first research question by
investigating patients’ preferences for treatment of low back pain.
Section 6 presents the paper exploring the formation and influence of attitudes in patients’ treatment
choices for lower back pain and testing the benefits of a hybrid choice model approach, building on the
data used in section 5, generated from the design described in sections 2, 3 and 4.
18
The paper argues that, possibly particularly in scenarios where clinical evidence is limited or not clear
cut, perceptions that patient forms, either through past experience or through discussion with other
patients and/or medical experts, will play a major role in shaping decisions. The paper thus investigates
the potential benefits in capturing the role that perceptions and attitudes may have in explaining
treatment choices made by patients by including a latent attitude in a joint model of the formation of
perceptions and of the choices. The findings from this model are then contrasted to structures allowing
for simple random heterogeneity.
The results show that while the hybrid structures provide some further insights into the formation of
attitudes, and some gains in efficiency, the overall results remain largely unaffected. Interestingly, the
commonly used socio-demographic variables did not prove to be of significant influence, while
recommendations of surgery from GPs or friends and family had a positive impact in simpler models,
while recommendations from friends and family are of even greater importance when looking at the
hybrid model. Results thus suggest that patients are significantly influenced by peers and less so by
professionals.
Section 7 provides a discussion of the results presented in relation to existing knowledge. It adds to the
discussions of limitations presented in the papers in sections 4-6 and further provides a discussion of the
validity of the stated choice method. The section also gives ideas to and discusses how results can be
put into practice, by formulating guidelines or developing decision-aids. Further, a discussion of the
relationship between patients and HCPs and on influences by peers on individuals’ decisions is
presented, by introducing novel approaches to thinking about choices, derived from literature outside of
health economics. The section ends by discussing alternative decision rules to standard choice modelling
theory and gives further insight to the methodological issue of heterogeneity.
Throughout the section, ideas for future research are presented.
Section 8 contains concluding remarks and finally section 9 contains a summary of the thesis in English
and section 10 a summary of the thesis in Danish.
2 Methods and existing knowledge of preferences and LBP
This section present the methodology primarily used in this PhD study. In section 2.1 it introduces stated
preferences and methods used to elicit preferences and their use in health care. Further an overview of
existing knowledge regarding preferences and LBP is provided in section 2.2. This overview is based on a
19
systematic study of literature. Section 2.3 offers a description of the theory behind and methodology
relating to the use of choice modelling, highlighting efforts to capture and explain heterogeneity and
what to consider when generating SP-designs. Section 2.3 further introduces the step-wise process of
generating and analysing SP-studies, which is elaborated upon with empirical details in section 3.
2.1 Stated Preferences
We all make choices every day. What job to take, what partner to be with, where to live, what to eat,
what clothes to wear, what car to drive - to walk, bike, take the train or just stay home. All choices have
a range of possible outcomes or options. The goal of modelling choices is often to understand the
behavioural process behind choosing. In other words, to figure out why one option is chosen over
another, or several others. Modelling choices can also be used to compute welfare estimations or values
of willingness to pay. Regardless of the aim, the basic idea is that there are deterministic factors that
lead to or cause a choice being made. Some of these factors can be observed, and some of them are
hidden or unobservable.
Making a choice is impacted by individuals’ perception of the utility that the various alternatives have,
which can be broken down into the key characteristics an alternative consists of. Each alternative has
similar, but also different dimensions or attributes and each of these attributes can take on a range of
levels, and it is upon these that an individual base their comparisons or evaluations. Individuals weight
the different key attributes relative to another by comparing the level of quality or utility that an
attribute possess in a given alternative. Not only do individuals weight the utility of the attributes within
an alternative, they also trade-off levels of one attribute against levels of other attributes, implicitly
weighing and valuing both the attributes and the alternatives. These trade-offs lead to a development of
a preference for one alternative over another and ultimately results in that most preferred alternative to
be chosen.
It is assumed that any given individual makes choices based on a rational “calculation” of what gives
them the most utility (or least disutility), in other words chooses their most preferred option under
whatever constrains they might face.
When goods are bought and sold on a well-functioning, competitive market we can observe most of the
key attributes and the levels of these, directly and people reveal their preferences by their behaviour in
the market. Hence individuals’ preferences can be observed by observing buying and selling behaviour.
In many cases markets are biased by externalities or monopolies or information asymmetries or – as is
20
the case of most health care functions in Denmark, including (most) treatment for LBP, regulation by a
public authority. The regulation means that the market does not function in a way that allows real
competition as the e.g. the number of providers is artificially decided upon and prices are not paid out of
pocket and are also artificially set. In addition the asymmetry of information between patients and their
HCPs further complicates the market for delivery of treatment of LBP. In markets that does not allow for
unbiased observations of choices or direct revealing of preferences, these can be elicited by using
revealed or stated preference methods.
The hypothetical nature of stated preference (SP) techniques has given rise to criticism. This issue is
further discussed in section 6.1.1.1. Having respondents subjectively evaluate different treatment
options or the like, instead of observing behaviour on actual markets, potentially creates a risk of
respondents making random choices as they have no incentive to be considerate in their choice
behaviour. Hence there’s a risk of imprecise welfare measures or biased willingness-to-pay (WTP)
estimates in a both upwards or downwards direction 162,163. However, as explained, SP methods can be
used where preferences cannot be observed or where no market is in place, evaluating future possible
goods and it allows for the estimation of choices in the presence of alternatives that do not exist to the
total value of both use and non-use, of a good. Some researchers claim that valuing WTP either way is
problematic when dealing with publicly provided health care as it is provided free of charge to the
patient or user. Instead of price some researchers in health economics are using “time” as a “payment”
attribute, which has been established as a valid and sensible approach 160,164–168.
Further, a particular advantage of SP is that it allows the researcher to control the variation in
respondents and attributes valued ensuring an efficient design 169 170. In regards to the research question
in this thesis, it quickly became apparent that using SP would enable the inclusion of some key-
attributes as reductions in pain and ability to perform everyday activities that would have been
unobservable in e.g. registers. Taken all of the above advantages and disadvantages into consideration,
SP was deemed to be able to fulfil this projects aim of investigating patients’ preferences for treatment
of low back pain and hence a choice experiment evaluating hypothetical treatment options was
generated.
Before getting into detail with the SP method this section provides an overview of what is already
known with regards to preferences in relation to treatment of low back pain and how preferences
affects treatment choices.
21
2.2 Preferences for treatment of Low Back Pain
This section is built on a systematic study of existing literature.
The literature study had multiple purposes. It was used to inform the experimental design by given the
first hints towards possible attributes for the SP-study. Further, the purpose was to produce an overview
of existing knowledge on patients’ preferences in regards to multiple issues relating to treatments,
building the basis for evaluating gaps of knowledge and contributions to the field. Also, it shed light to
the context in which the empirical work was performed and investigated the effects that patients’
preferences might have.
A “preference” in a traditional SP way is a utility-measure based on trade-offs of other characteristics of
a good. In back pain literature a preference is not as clearly defined. A quick search in Medline using the
same search terms as in the search presented in table 1 in section 1.4 and pairing all the terms with “low
back pain”, resulted in only 40 hits, illustrating the lack of use of SP-studies in the field of low back pain
and the need to broaden the definition of a preference. Thus a new search strategy was made including
a much broader notion of a “preference” allowing studies on beliefs or expectations to be included and
aiming at finding studies looking into preferences for low back pain in a broader sense. This search
provided 602 results. The broad notion of preferences resulted in some noise in results and a rather
elaborate sorting process.
Table 2. Search strings in Medline
Initial search Final search
("Search (low back pain) AND ((((((((((((((((paired comparisons) OR paired comparison) OR pairwise choices) OR pairwise choice) OR stated preferences) OR stated preference) OR conjoint choice experiments) OR conjoint choice experiment) OR conjoint analysis) OR conjoint analysis) OR discrete choice model) OR discrete choice models) OR discrete choice modelling) OR discrete choice modelling) OR discrete choice experiments) OR discrete choice experiment)", 40)
("Search (((((((patient preference) OR patient preferences) OR patient belief) OR patient beliefs) OR patient expectation) OR patient expectations)) AND ""Low Back Pain""[Mesh]",602)
22
Inclusion criteria were divided in 3 categories based on the purpose of the search, including enlightening
existing knowledge on patients’ preferences for treatments and factors related to treatment, shared
decision making and the connection between preferences and treatment outcome.
Table 3. In- and exclusion criteria
Inclusion category Number of papers included in category
1. Preferences (in the broadest of senses) for
different treatment options including both
surgical and non-surgical and factors influencing
preferences
11
2. Studies looking at preferences for shared
decision making and HCP characteristics/delivery
etc.
10
3. Patients’ preferences effects on treatment
outcomes
37 (of which 23 deal with fear-avoidance)
Total 58
Exclusion criteria Guideline adherence/development (not including
patients’ preferences), investigations of
treatments (not regarding preferences thereof),
questionnaire validation, development or testing,
non-back pain, investigations of risk-factors,
practioners’ preferences unrelated to patient
outcomes, screening, language, quality or delivery
of care
Sorting was done in two rounds. First round was based on title and abstract and the second round was
based on full text. The process is described in figure 1.
23
Figure 1. Sorting of results. Figure is inspired by PRISMA 171
Only one study uses traditional stated preferences methods 160 while the majority of studies uses a
range of questionnaires based upon authors’ specific research question and a few uses qualitative
methods.
It is possible that the significant variation in study design and focus of the included literature is affecting
the conclusions drawn. Not all studies clearly define the objectives or comparators in focus, nor do they
define preferences. Also the method of validation of the surveys varies. But even if it varies greatly how
Records identified through database searching
(n = 602)
Scre
en
ing
Elig
ibili
ty
Ide
nti
fica
tio
n
Records after duplicates removed (n = 602)
Records screened (n = 602)
Records excluded (n = 472)
Full-text articles assessed for eligibility
(n = 130)
Full-text articles excluded, No full text available
(n =10) Measuring effects of
media campaign, questionnaire/guideline
development or adherence)
(n = 16) Language
(n=3) HCP’s
preferences/beliefs/knowledge
(n=24) Studies on quality or
delivery of care (n=17)
Not LBP (n=2)
Studies included in qualitative synthesis
(n =58 )
24
the knowledge is obtained, researchers agree on the importance of studies looking into preferences,
whatever the definition, in this field 172.
2.2.1 Patients’ preferences for treatment of LBP
Only a few studies touch upon patients’ preferences for treatment of low back pain using different
methods and uncovering different issues.
Yi et al. 160 useds a discrete choice experiment to investigate patients’ preferences for a pain
management program for LBP patients. The study concludes that smaller group sizes and low intensity
programs over longer time spans increased patients’ desire to participate. The exact content or provider
mattered less to patients while travel time to clinic was of importance. The study detects some
differences in patients’ preferences based on patients’ stated severity of symptoms. Yi et al. 160 suggests
that patients’ preferences for treatment are influenced by perceived severity of symptoms.
Two studies directly asked patients ex-ante what sort of treatment they prefer 173,174. In the study by
George and Robinson 175, patients are asked to choose which of the following 3 treatment options they
prefer; physical therapy only (18 out of 105), graded activity (39 out of 105) or graded exposure (7 out of
105). Patients can also state no preference (39 out of 105). The study investigates how the delivery of
the preferred treatment affected perceived satisfaction, finding that the patients with no preference
had lower expectations towards effects and reported larger improvements and satisfaction, suggesting
that strong ex-ante preferences can lead to less satisfaction with actual treatment. This finding is
supported by the study by Donaldson et al. 174 where patients are asked to state a preference for either
thrust or nonthrust manipulation interventions. Here, patients stating no preference also gained the
most in terms of pain relief at the 6-month follow-up. However the number of patients stating no
preference was low (8 out of 149). The study also investigates the potential differences in outcome for
patients receiving preferred option compared with those not receiving preferred option, finding no
statistical differences.
A range of studies look into preferences in relation what is important for patients when making choices
176–179. The qualitative study by Slade et al. 176 looked into patients’ preferences for exercise programs
using 3 focus groups of adult chronic LBP-sufferers. Results point to range of issues regarding
preferences for exercise treatment, highlighting that patients are driven by previous experience and
preferring helpful and empowering skills in HCPs in interactions with patients. If HCPs behave as
preferred and exercise programs are designed with consideration to patients past experiences with
25
exercise, patients are likely to participate in and benefit from such programs. Shaw et al. 178reports
results from a focus group of 23 individuals with LBP, showing that return to work and ability to pursue
everyday activity is of great matter to the informants, while Dean et al 179interviewing 9 LBP-patients,
show that patients are interested in integrating physiotherapy in everyday life, if it can be done in a
routine manner with limited time use. Finally the study by Liddle et al. 177 reports from three focus-
groups of 6 participants, suggest that patients prefer individually tailored exercise programs with
supervision and follow-up support.
These few studies looking into preferences for treatment are diverse in approach as well as topic. But
point to the importance of including patient preferences when designing or choosing treatments and
when formulating guidelines as stressed by Owens 172.
2.2.1.1 Factors influencing preferences
Three studies suggest how different factors influence patients’ preferences 180–182. Two of these studies
suggest that extended information to patients affects patients’ preferences leaving them more prepared
for self-management 182or generating better outcomes 180. Finally the study by George et al. 181 suggests
that patient education enhances patients’ ability to cope LBP. The three studies suggest that increasing
the communication to patients with LBP potentially shapes preferences and increases positive effects.
2.2.2 Patients’ preferences for shared decision making and HCPs characteristics or service
delivery
No studies on patients’ preferences for shared decision making was found, but a range of studies look
into patients’ preference for characteristics and involvement of an HCP 183–194.
Some of the studies measure patient preferences for the personalities, characteristics or abilities of
HCPs. Among these is a questionnaire-based study with 657 participants, investigating patients’
preferences for the skills or characteristics of HCPs, done amongst LBP patients choosing chiropractors
and physiotherapist by Bishop et al. 183 The study conclude that patients are concerned with perceived
technical abilities of HCP and that patients listens to and are guided by reputations of HCP. Farin et al.
184 report from another questionnaire based assessment, concluding that patients want trust and good
communication in their relationships with physicians and that the quality of which affects outcomes.
This is supported by Dima et al. 185 who reports from 13 focus groups, showing a preference for empathy
and expertise in HCPs treating LBP.
26
The studies by McCarthy et al. and McIntosh & Shaw and Verbeek et al. 186,187,194 all conclude that
patients prefer as much information as possible in regards to diagnosis and treatment options, but
McIntosh and Shaw suggest that barriers for taken time to provide this information exists, leaving
patients to access information from other sources 187. This results becomes even more interesting when
looking into conclusions from May et al. who based on 12 interviews, suggest that when HCPs exhibit
doubt, patients listen less to advice and prefer to stress and follow own expertise 191.
Two studies look into patients’ preferences for involvement in treatment, both suggesting that patients
prefer HCPs to take the lead both when it comes to physiotherapy suggesting potentially less successful
outcomes from treatment if patients re not actively involved and perform self-management 192,193.
Finally the study by George & Hirsh 190 suggest that while patients preferences can be met in regards to
how physiotherapy is delivered and by whom that does not necessarily mean that patients’ are content
with results of treatment. Thus, the authors stress the need for distinguishing between these factors in
studies measuring preferences and outcomes.
2.2.3 Patients’ preferences’ effects on treatment outcome
Theories on the possible connection of patients’ beliefs and expectations and effects on treatment
outcomes has flourished in health research and studies looking into patient preferences and effects on
outcomes or “preference-effects” where found in the search showing inconsistent evidence. Many of
these studies revolves around be beliefs and not “true” preferences. In fact, a large proportion of
literature is concerned with fear and how exhibiting fear limit the potential outcome of any treatment.
This has been confirmed in a range of studies 195–212. However a few studies contradict the statement 213–
216.The literature on fear-avoidance is interesting on its own showing the potential for enhanced
communication from HCPs regarding patients’ fear, however, the literature is not concerned with
preferences and how they affect outcomes and will not be further discussed in this thesis.
One study directly measures preferences and impact on outcome. In the study by Sherman et al. 217
patients receive acupuncture as treatment and are asked in advance if that is a preferred treatment.
Analysis look into differences in effectiveness in regards ti disability, finding no difference in outcome for
patients preferring acupuncture compared to patients not preferring acupuncture. Another study by
Moffet el al. 218 uses a randomized controlled trial of exercise as treatment for LBP, controlling for
patients ex-ante preferences measured by asking directly if a patient preferred exercise. The
intervention group showed better outcomes but this was not influenced by preferences.
27
A series of studies measure how patients’ feelings toward a treatment affect outcomes. Albeit different
settings and sizes all studies conclude that in patients feel strongly about a treatment and believe it will
work – outcomes are affected positively 219–225. Supporting this is results showing that patients ability to
live with pain, affects outcomes while expectations towards recovery and expected recovery time can
predict actual recovery time 226–228. Further, patients perceived credibility of and confidence in
treatment also affects outcomes 229,230.
It could be hypothesized that research addressing these potential preference-effects could suffer from
measurement bias as patient preferences as well as treatment outcomes are measured very differently
in the various trials. Also, a study by Oliveira et al. 231, find that patients with LBP need to see great
improvements in order to think non-surgical treatment is worthwhile, suggesting that if outcomes are
measured subjectively results might vary greatly from more objective measures. Further, the effect of
preferences, could potentially set in earlier than what is captured in these studies, regulating treatment
pathways, keeping some patients from ever using the health care system or ever getting referred to
different treatment options 220.
2.2.4 Conclusions from literature
The literature review point to a field acknowledging the fact that somehow patients’ preferences,
expectations and beliefs matter and can affect treatment outcomes.
Only one study uses traditional preference elicitation methods while the majority simply asks patients to
state their preference. When choosing treatments patients are influenced by previous experience and
the level of information can shape preferences.
Patients seem to prefer skilled and experienced HCPs who take them seriously and communicate clearly.
If HCPs exhibit uncertainty this can make patients lose faith and find information elsewhere.
Finally, it seems clear that somehow patients’ abilities, beliefs, expectations, trust and preference can
indeed affect their treatment outcomes, bearing in mind the possible biases from these studies.
Albeit the existing literature pointing to some issues (e.g. ability to pursue everyday activities) to
concern in an stated choice experiment investigating treatment preferences, a clear understanding of
driving factors is lacking. Further, it is clear that a gap of knowledge exists in regards to trade-off based
preferences for different treatment options and factors driving these. As a result the literature study
cannot be deemed to be sufficient to generate a SP-design measuring treatment preferences. Although
28
some of the uncovered issues were included in questionnaire. As stated in introduction research in LBP
is highly context specific and further work was therefore performed to inform and test the design used
in the empirical work. This is further discussed in sections 2.3.2 and 3 while the results of the empirical
work in regards to existing knowledge is discussed in section 7.1.
2.3 Methodology
As should now be clear, modelling choices or getting people to state preferences by observing
hypothetical choices is a valid and well-established way of obtaining useful knowledge about peoples’
motivations and wishes. Also, it has been made clear that in the field of LBP, preferences of patients’ can
have effects, not only with regards to decision-making or treatment choices but also with regards to
treatment outcomes.
This section describes the theories behind choice modelling in more detail and serves as the backdrop
for understanding the following sections which are concerned with issues in relation to designing a
choice experiment and modelling results.
2.3.1 Theoretical base for choice modelling
As touched upon in section 2.1 the theoretical base for modelling choices is based on theories of how
choices are made, what yields utility and how it can measured by combining observed components with
an unknown component and how a good or a choice can be described based on its characteristics or
components.
This section briefly introduces the basic theoretical concepts and introduces some basic notations which
will be elaborated on in section 2.3.3.
In a the simplest of choice experiments the respondent is asked to choose a single alternative from a set
of choices made from mutually exclusive alternatives 232. The choice set mirrors the complete or
exhaustive choices and the ordering of the alternatives has no effect on the respondents decision-
making. Each alternative is characterised by a sum of utility, specific to each respondent as the
respondents vary due to their characteristics.
The most vital assumption in choice experimentation is that of utility maximisation, deeming that a
respondent will only choose one alternative i over alternative j, if Ui>Uj.
29
If the true utility of a choice is called U and the factors observable to the researcher are given by V while
the unobserved factors are labeled Ɛ, we can express the behavioural process of choosing an alternative
(i), by a given individual (n), by a function of;
𝑈𝑖𝑛 = 𝑉𝑖𝑛 + 𝜀𝑖𝑛
The above equation is based upon the theory of Random Utility (RUT) which forms the basis of several
models and theories of consumer judgment and decision making 233–235as well as and the Lancastrian
theory of consumerism 236 which states that which proposes that utilities for goods can be decomposed
into separable utilities for their characteristics or attributes.
The function expresses utility as a function of known and unknown parts. What is known or observable
is the attributes (V) of a given alternative as well as the covariates such as sociodemographic variables or
taste of respondents. Hence, the researcher observes the attributes of an alternative and often also a
range of characteristics of the respondent, but not the total utility.
The observable part is defined as Vin = f (βn, xi,n, zn), where xi,n is the vector of measurable attributes of
alternative i as shown to respondent n and βn is a vector of parameters representing the taste of a
respondent n, which can be estimated from data and zn is a vector of sociodemographic variables. The
quality of how well the observable or systematic component of utility is estimated or identified depends
on how well the researcher succeeds in including the right attributes that influence choice. It is
therefore of great importance that researchers devote time and resources in advance of data collection
and analysis to focus on designing the best possible choice experiment including the right attributes.
This is dealt with in sections 3 and 4.
The unobservable part of the utility function, represented by the random component (Ɛ), expresses
omitted variables or measurement errors etc. The inclusion of this unobservable utility means that a
respondent’s choice is not deterministic and cannot be predicted precisely. Thus, the choice process
becomes probabilistic, in such a way that the alternative with the highest observed utility has the
highest probability of being chosen, which can be expressed as;
𝑃𝑖𝑛 = 𝑃(𝑈𝑖𝑛 = max𝑗𝜖𝐶𝑛
𝑈𝑖𝑛 ) = 𝑃( 𝑈𝑖𝑛 > 𝑈𝑗𝑛 ) = 𝑃( 𝑉𝑖𝑛 − 𝑉𝑗𝑛 > 𝜀𝑗𝑛 − 𝜀𝑖𝑛 )
30
Again, under the assumption that j≠i and Cn = choice setn
As seen from the equation above the probability of i being chosen over j is given by the probability of
the total utility of alternative i is bigger than the total utility from alternative j. Hence, the probability of i
being chosen over j is given by the probability of the difference between the random, unobserved
components Ɛjn,in being smaller than the difference between the systematic, observed components Vjn,in..
As it is irrelevant to a respondents’ choice, it is not the magnitude or absolute level of utility that is being
measured, but only differences in total utility, which allows for constants to be added to the utility of
each alternative without changing the decision rule.
The researcher also has to specify how the observed variables combine to drive systematic preferences.
Thus, the researcher must decide upon the formal relationship between the choices made by the
respondent and the included explanatory factors. The majority of the work in the area of choice
modelling uses a linear formulation, where 𝑉𝑖𝑛 = βn’xin. Often a constant is added to this specification to
capture the effect of any utility not included in the model so that 𝑉𝑖𝑛 = βn’xin + ci , where ci is specific for
alternative i. Since again only differences in the alternative specific constants matter, one constant is
often set to zero and all other constants are thus interpreted as the effect of any omitted factors relative
to the constant normalised to zero 169,232.
In the analysis of SP data a model is developed to explain the observed choices by estimating the
parameters and making assumptions about the unobserved, random parameters. This is explained and
discussed in more detail in section 2.3.3, whereas the following section will introduce and discuss the
process of designing a stated choice experiment.
2.3.2 Designing a stated preference choice experiment
Making a stated choice experiment can be described as a stepwise process from the formulation of a
research question to the presentation of the study results. This process is shown in figure 2 adapted
from ISPOR taskforce on Conjoint Analysis in Health Care 68.
This section will provide an overview of the design process from the start to the point where data has
been collected, including a thorough description and theoretical background of the overlooked
qualitative work that can help inform the first steps of the process and that are described in detail and
discussed in section 4, which is the first journal paper included in the thesis. This section is followed by a
description of the last steps in the process, namely the statistical analysis. This section provides the
theoretical basis for the stated choice experiments that have been designed and used in the journal
31
papers included in this thesis. The detailed process of the empirical work is described in section 3 and
discussed in section 7.1.1.
As seen in figure 2 a range of steps is required before an actual collection of data can begin. This process
is best informed by a triangulation of qualitative methods and quantitative testing. The goal of the
process prior to collecting data is to identify how individuals think about the evaluative process of the
problem one wants to investigate, i.e. what characterizes the choice situation, what attributes the
choice consists of and what factors to include in the components of the utility function. In the stepwise
design process, the selection of the components that are to be included in the experiment is the core
task. As introduced in section 2.1 the choice situation can be described in terms of its attributes and
levels, and selecting these, obviously requires careful consideration and testing. This process is often not
reported in detail in papers concerning results from stated choice experiments 17, but has been
dedicated some focus in research.
Figure 2. Step-wise process of SP-studies
32
The following sub-sections will provide an introduction to qualitative methods and their relevance for
stated preferences
2.3.2.1 Qualitative Methods in stated preference research
Qualitative research methods have a well-reported use in health services research and health research
aimed at patient or user experiences. However, it is not as common a practice in health economic
research 17. As qualitative methods works particularly well when the aim is to get an insight into people’s
experience or perceived value or understanding of e.g. a treatment choice, some researchers have
dedicated more focus to these methods in relation to designing meaningful and valid SP-experiments
237,238.
Qualitative methods include observations and field-work as well as interviews of groups or individuals.
In health research all of these methods are used. Generally qualitative methods are used in a more
inductive way and a not aimed at generalizability but at painting a fuller picture of the subject of
interest. Thus, the case selection is different from quantitative methods as representativity is less
central than choosing respondents with important stories or knowledge.
Observations are used to understand contexts or interactions. The observer blends into the background
of the situation he/she is observing and observes the situation unfold naturally. This can provide
important knowledge on eg. a doctor/patient relationship or shared decision making. Being part of a
situation and not disturbing or changing it, is an important issue to consider, when performing
observations239.
Interviews of groups or individuals are used to better understand peoples’ motivations, understandings,
thoughts and stories. Unlike observations the interviewer is a part of the conversation and has an active
role in the data collection process. Thus, the interviewer needs to guide the interview without letting
personal opinions disturbing the process and while insuring a feeling of trust and safety with the
respondent and insuring that the respondent answers all important questions. Interviews are of the
semi-structured meaning that the interviewer knows the subjects he/she wants to touch upon, but also
lets the respondents’ information and personality guide the interview. The topics are often formulated
in an interview guide. Interview guides and questions need to be formulated with the respondent in
mind. Patients can be in pain or otherwise distracted so interviews should be short and contain easy
language, while professionals are able to answer more specific and difficult questions.
33
When an interview has been completed a short protocol is formulated with a description of the
interview. Taped interviews are transcribed and analysed either theory-based (does the respondent
confirm a certain theory) or more explorative, philosophical ways (how is a phenomena talked
about/understood, how is the “world” of the respondent) 239.
In SP it is generally agreed upon that attributes and levels are best determined through some sort of
qualitative work and that this work is of great importance to the validity and quality of design.
However, while almost everyone uses this kind of work to determine attributes, level selection and
pre-testing of whole questionnaires, the qualitative process is only reported in one third of the
applied literature 17. It remains unclear whether this affects the validity of the experiments. And very
little research has been done into how qualitative processes lead to SP-designs or help inform design
processes.
The specific uses of the presented methods in this section is further elaborated on in section 3,
presenting the empirical work used in the papers in this thesis, while qualitative methods and their use
in SP is described and discussed thoroughly in section 4.
The following sections describe the next step of the design process.
2.3.2.2 Construction of choice tasks
Aside from the process and importance of choosing the correct attributes and levels, the researcher
needs to decide upon a range of issues which could possibly affect the outcome of the study and the
potential of the data to answer research aims. These issues include factors concerning the
administration of the survey, what questions to include beside the choice experiment, how to frame the
questions and what experimental design to use etc.
The construction of choice tasks includes consideration in regard to is each choice task should include all
or only some of the chosen attributes and how the choices are made – e.g. ranking, rating or choosing
best/worst out of a number of choice tasks. Traditionally the full number of attributes is included in
health economic stated preference studies 68 and respondents are asked to choose the alternative they
prefer. However, if the ultimate goal of choice experiment is to mimic the real world as closely as
possible, it is often more realistic to include an option of choosing not to choose in the experiment.
Choosing not to choose any of the presented alternatives can either be presented as choosing noting or
as choosing the current state a respondent is in. The two options yield different interpretations, as
choosing a current state often mean that the respondents status quo differs and should be interpreted
34
as such. Including an option to opt out however, can more easily be seen as a universal alternative,
equal for all respondents. However, the choice not to choose or to choose the current state is still
debated in literature and object to research 240. It has been established though that including a no-
choice option remarkably changes results 241.
In general, one should design stated choice experiments to allow for observation and modelling of no-
choice, whenever it is an obvious element of real market behaviour.
2.3.2.2.1 Label or no label? Adding labels to the choice scenarios means branding or naming the overall alternative. Labels are not
the same as attributes and does not vary or take on levels. A label captures a respondents feeling
towards a brand or name and hence avoids biasing the trade-offs between attribute levels. If labels
makes sense to the market situation in question and adds realism to the experiment it is advised to use
them. Using labels can require fixing some attributes or parameters to one labelled alternative if the
attribute is linked to the label 242. If all attributes are generic and the experiment evaluates different
configurations of a single alternative, labels might be biasing results as respondents might be unwilling
to make trade-offs and only focuses on the labels 243. As with the rest of the design process, it is
important that the researcher tests how labels/no labels are perceived by respondents.
2.3.2.3 The experimental design
The experimental design combines the attribute levels into hypothetical scenarios. This can be done in a
range of different ways, each with different advantages and disadvantages. The experimental design is
essentially the task of finding the best way to combine chosen attributes and levels in an efficient way,
ensuring a minimum number of choice sets and maximum information on preferences pr. choice made.
To date, orthogonal designs remain the most commonly used. Othogonality of an experimental design
refers to the correlation structure of the included attribute. A design is said to be orthogonal if all
attribute correlations is zero. Practically, this means that all attribute levels, are evenly spread over all
choice tasks and that combinations of levels does not show a correlated or positive/negative pattern.
Orthogonal designs has gained popularity within the all fields of research using stated choice
experiments, but lately the method has been challenged by the so-called efficient designs. Early on,
orthogonal designs were questioned as the best option for creating realistic choice scenarios 244 and
experimental designs began evolving, trying to suit the non-linear models often used for analysis in
stated choice experiments245–247. Since then, experimental design has evolved even further, especially in
35
regards to D-optimality or D-efficiency, which minimizes the determinant of the asymptotic variance-
covariance matrix of models, which minimizes the standard errors and thus maximizes reliability of
parameters estimated in models. D-optimal designs tries to maximize the information on tastes or
preferences obtained from each choice made by a respondent over all observations 248–258. D-optimal
designs have been proposed in a number of different ways, each with different assumptions as to how
to estimate what the variance-covariance matrix might look like, without having collected data.
Assumptions about a priori information also differs, and can be zero, fixed or unfixed values and even
uniform or normal distributions (in principal priors can take all sorts of distributions, but the Ngene-
software in which efficient designs are generated only allows for the two – so far). If distributions
around the prior parameters are assumed, a Bayesian approach is taken and the experimental design is
evaluated over a number of different draws. This simulation process can use different draws, with
Halton draws being the most common 242,259.Some researchers have suggested optimizing other
measures derived from the variance-covariance matrix, generating A- or S-efficient designs, considering
the trace of the variance-covariance matrix or the maximum t-ratio246,260.
When an experimental design has been found, a number of choice sets are generated. Different
researchers have interested themselves in the number of choice sets a respondent can manage and how
the number of choices affect preferences 261–264. In general, somewhere between 6-18 choice sets pr.
respondent is acceptable146,164, but this is clearly dependent on the characteristics of the respondent.
The number of choice sets found in the chosen experimental design might not fit with the number
manageable to the respondents; hence blocking the choice sets intro groups can solve the issue. In
efficient designs this is done based on minimum correlation between the blocking column and the
attributes columns.
2.3.2.4 Preference elicitation
Another issue to consider is how to best present the choice experiment within the survey. This is
especially important as most research is taking place without an interviewer to help explain the choice
tasks. The information about alternatives can attributes can be both written and/or visual. In general,
the more realistic a choice scenario is the better. This not only holds true with the choice of attribute
and levels, but also with how a choice scenario is depicted. If only text is used, this can be done in many
ways; bullet points, long explanations, one worded-attributes etc. Describing choice scenarios and
attributes/levels is a trade-off between accuracy and the cognitive burden for the respondent. Recently,
the easy access to graphics and multimedia has created a trend of including these in the choice
36
scenarios under the assumption that pictures speak louder than words and are less subject to
interpretation 265. Again, the feasibility of using graphics is dependent on the specific experiment, the
survey mode and costs. The use of graphics was not tested for the survey designed for the data
collection for the empirical parts, as none of the attributes in the design were easily translatable into
pictures.
Similar to how to present the choice scenario, is how to frame the actual context of choosing. Framing
refers to the way in which the choice scenario is presented and what information is given to the
respondent before he/she makes choices. Framing has been shown to affect how respondents make
choices and hence the parameters of the utility function. However it is much less clear if/how framing is
only influencing the random, error component, or if it has actual effects on tastes. Some research
advocates that the latter is not the case 266–268. However, impacting the error term variance does impact
outcome, as a scale impact on the random component, also affects choice probabilities. Thus, framing is
impacting outputs, but it has yet to be shown how of if this affects measures of welfare.
2.3.2.5 Instrument design and data collection
Choice experiments are almost always part of a longer survey. Thus, researchers should not only take
the cognitive burden of the choice experiment itself into account, but see this as a part of longer and
possibly challenging survey. Hence the questions in the survey should be considered with care and help
enlighten important issues about the respondent and at the same time gather information possibly
affecting choices, while describing and presenting the attributes and the choice experimental context.
Some researchers have been concerned with the effects on results, possibly caused by the method by
which the survey is administered. In general, it is acknowledged that the survey mode can have an effect
on e.g. response rate or response quality and gathering data is usually time consuming and expensive. It
has been shown that verbal representation of a choice experiment facilitates the choice process of the
respondents, but interviewer-based data gathering in choice experiments is not among the most
commonly used methods. Possibly due to expenses 17. The internet has gained popularity as a quick a
cheap way to contact a large number of respondents, but it has been questioned if the quality of data is
up to standard. When comparing the validity of data gathered on the internet, via the phone or by
interviewers, researchers have not found any substantial differences in validity on a range of parameters
269–271. It is generally considered to be most important to choose the survey mode, most suitable for the
target group of interest, and if the general population is not it, an internet survey might not be most
37
feasible way. In health, it has been found that the most suitable, cheap way of surveying groups of
patients is by handing out paper questionnaires in hospital settings 272.
Besides survey mode and content, sampling strategies and ethical considerations are of importance
when designing the data collection. These issues and all of the issues discussed in the above section and
sub-sections, are further discussed in an empirical context in section 3.
The next sections deal with the statistical analysis of choice data.
2.3.3 Statistical analysis
When a design has been settled upon and data collected, it is time for analysis. A range of models are
used in analysing stated choice data. Recently a lot of work has gone into finding ways to model and
explain both scale and preference heterogeneity within respondents. This section will present the
simplest model and more advanced models taking taste heterogeneity into account and serves as a basis
for understanding the model used in section 4 and 5. Section 5 also provides a presentation of one of
the most advanced models, looking into heterogeneity.
The multinomial logit model (MNL) is the simplest model in the family of models used to analyse stated
choice data. Vital to the MNL model is the assumption of “independent and identically distributed
random utility”, as defined by Manski in 1977 235.This assumption of the distribution of the error-term
prevents taking correlation in errors across alternatives or observations, but for the same respondent,
into account – otherwise known as panel-effects. Also, the assumption of identically distributed errors
prevents a treatment of taste heterogeneity across respondents 169. Further, it has been showed that
the MNL model relies on the assumption of independence of irrelevant alternatives (IIA). This means
that in the MNL the probabilities of two different alternatives are independent of the existence of other
alternatives and of the attributes in the included alternatives. Thus, any change in probability of one
alternative being chosen, draw equally from probabilities of the collected included alternatives 232,273.
While the IIA assumption can proof to be realistic it has its clear limitations and it is important to test
the validity of the assumption, as suggested by Hausman & Wise 274, to avoid fitting a MNL model when
choice sets violate the IIA assumption, as this would lead to misleading results 275. However, most
researchers fit MNL models to their data as a point of departure for more advanced models to
investigate how the data fits under the most constrained options. Also MNL remain the most common
model for comparison when looking into how preferences can be explained by including allowing for e.g.
taste heterogeneity. But the limitations of MNL have led to the development of a range of models that
38
loosens the IIA assumption and takes panel-effects into account. One such model is the mixed
multinomial logit model (MMNL), which has been highly successful in the stated choice literature in
health economics in the recent years 276.
The MMNL model overcomes the limitations of the MNL model by loosening the IIA assumption
allowing for panel-effects, heteroscedasticity and maybe most importantly taste heterogeneity. This is
obtained by calculating the choice probabilities as an integral of MNL choice probabilities over an
assumed distribution of random terms. The utility function of a MMNL model is thus given by;
𝑈𝑖𝑛 = 𝑉𝑖𝑛 + 𝑛𝑖𝑛 + 𝜀𝑖𝑛
Where an additional vector of error terms nin results in an integral without a closed form, in need of
estimation with simulation. The MMNL probability is a weighted average of the logit formula where β
parameters are collapsed into a function of parameters and their distribution. Hence the formula is
evaluated at different values of β, with the weights given by the density f(β). The crucial part of the
model formulation is the specification of which coefficients to be randomly distributed and the choice of
distribution to use. These choices obviously affect the model outcome. Section 4 provides an example of
a MMNL model.
The MMNL specification can not only be used to vary β parameters, but also to vary what error-
component enter the utility of an alternative, known as an error-components MMNL. This allows for the
induction of controlled heteroscedasticity for one alternative. Also some researchers have used the
MMNL to incorporate deterministic heterogeneity in the random terms, either in the mean or standard
deviation, to relate variation in the random coefficients to observed attributes of a respondent 277.
One shortcoming of commonly used MMNL model is the lack of explanation it offers for the observed
taste heterogeneity. The model can simply show whether heterogeneity is present and for what
parameters, but just concluding people differ, is often not enough for policy recommendations. Of
course there are ways to get more information, like incorporating interaction terms. Some researchers
find that another model allowing for heterogeneity, the Latent Class model is more useful than random
parameters MMNL, as it assumes discrete distributions of β parameters taking a number of possible
values. This is useful if there are segments with similar tastes in the respondent population, as the
Latent Class model treats the segments as classes, to which observed respondent attributes can be
linked. However, this is only a valid approach if heterogeneity is discretely distributed. Recently more
39
advanced model structures, allowing for the inclusion of latent attitudes and characteristics of the
respondent to be included as a dependent variable in the utility function, hence providing estimations of
if/how these factors drive choices. This approach is described in detail in section 6, which also serves as
an application example.
2.3.3.1 Variance Scale Parameter and Marginal Rates of Substitution
All utility measures or coefficients derived from any random utility model are confounded by a scale
parameter proportional to the inverse of the variance of the error term in the utility function. The scale
factor affects the estimation of taste parameters or utilities, in such a way that the larger the scale, the
bigger the coefficient (and vice versa; the smaller the scale the smaller the coefficients). Because of this
effect, it isn’t correct to compare utility measures from two models. Observed differences or similarities
could be solely related to scale as the taste parameters are expressed in a scaled form. Coefficients
reflect the effect of each observed parameter relative to the standard deviation of the unobserved part.
As the scale parameter does not affect the ratio of two coefficients, researchers often estimate marginal
rates of substitution (MRS) to be able to compare tastes, models or compute willingness to pay-
estimates. MRS is simply the ratio between the marginal utility of two attributes, examining the relative
importance of attributes to one another or in other words looking at the change in an attribute required
to compensate for a change in another attribute, keeping the total utility constant. It is assumed that
some level of improvement in one good can compensate some level of worsening of another good,
leaving an individual on the same indifference curve. This compensatory decision making is the core of
consumer theory and is the theoretical basis for MRS.
Statistically the constant marginal utility implies a linear payment-attribute. Often price/costs is used as
a denominator in the ratio, to make the researcher able to estimate marginal willingness to pay for a
change in any other attribute. In health, a time attribute is sometimes used a payment-vehicle,
estimating willingness to wait for a change in any other attribute. The use of price/time as the
denominator in the ratio is generally motivated by the continuous linear treatment of the associated
attribute but it is rarely ever tested if this assumption holds. However, when tested it is often suggested
that linearity in the payment vehicle cannot be assumed. Hence, researchers should not simply assume
that the payment-vehicle behaves in a continuous and linear way, but check for possible violations of
assumption of constant marginal utility. If payment-attributes are inconsistent with conventional
assumption, it is of great importance to acknowledge the consequences of this on welfare estimates and
consider other ways to perform calculations, depending on the purpose of the task. Some suggest that
40
cheap talking the cost-attribute is a valid solution while other suggests recoding costs, with the possible
downside of adding to any hypothetical bias 278–280. However, since the MRS, if it is not calculated to
estimate marginal willingness to pay/wait, is simply a ratio, and in principle any suitable attribute can be
used as a denominator, when bearing in mind the theoretical and statistically premises and the
difference in interpretation of the trade-offs as it has been done in the paper presented in section 6.
This section presented a systematic review of the existing literature concerning preferences in regards
to LBP. It showed how the field is characterised with difficult choices with confusing evidence to inform
the choices. It also highlighted the gaps of knowledge that the empirical work in this thesis helps to fill.
Further, this section gave insight into the theory and methods relating to choice modelling and
presented the step-wise process by which the empirical work, presented in the following section, was
done.
41
3 Empirical work
As states the existing knowledge uncovered in literature and especially the lack of trade-off based
preferences studies within the field of LBP, set the ground for a thorough empirical work, providing the
background for the following three papers. The empirical work took place in different settings and using
a mix of methods. Each step corresponding to the stepwise process presented and described in section
2.3.2 This section describes the empirical process in detail and in accordance with the stepwise process
with special emphasis on step 2 – formulating attributes and levels as a lot of extra effort was put into
this step in particular. Much of the work described in this section is elaborated on in section 4. Thus,
this section should be seen as an additional insight into the empirical work, the results of which is
presented in sections 4,5 and 6.
3.1 Formulating the research question
Formulating the research questions was done on the basis of literature searches and studies identifying
gaps of knowledge as already touched upon in section 2.2.
As the research performed in this thesis was founded by a large research grant from the Danish strategic
research council, the research questions were presented and discussed at a bi-annual meeting with the
other partners under the grant. These partners included a range of HCPs and economists, all performing
research in lumbar disease. Discussions in that for a help shape and narrow the research question and
pointed towards low back pain as an interesting field, characterised by confusing evidence and
potentially highly influenced by preferences. Further, literature searches on SP literature helped uncover
methodological issues yet to be enlightened or discussed further.
3.2. Selecting attributes and levels
Observational studies were performed at a large hospital-based ambulatory clinic and during the
three- day observational stay a large number of patient consultations each lasting about 10-20
minutes, were observed. Consulting doctors, of whom there were 3 of different age and experience,
were informed that I was present to get a deeper knowledge of what type of worries or questions
their patients had. At the beginning of each consultation the patients were informed by the doctor
that I was present to look into how consultations worked for research purposes.
42
The observations were based on a guide, formulated with inputs from experts. The guide is
depicted below in table 2, and was also used as a framework for writing down the observed
characteristics.
Table 4. Observation guide
Focus Points to observe
Physical frame
- Room
- Atmosphere
Doctor’s presentation
of treatment options
- Framing
- Choice of words
- Body language
- ”Translation” to common language
Patient reactions and
interaction with doctor
- Non-verbal Communications
- Comments and questions
- Use of prior experiences
- Choice of words
- Listening ability
- Mood and mood changes (insecure, relaxed,
joking etc)
Patient characteristics - Gender
- Age
- Appearance
Facts - Medical history (prior surgeries etc)
- Consultation time
The observed patients were being informed about their treatment indications or had already received
treatment. The patients’ questions and thoughts in relation to choosing treatment and their
motivational explanations were observed. Furthermore, the doctors’ part in the decision-making
process was observed and was subsequently discussed with the doctor in the small breaks between
patients and at the end of each day.
The purposes of the observations were to gain a deep insight into the decision-making context patients
with back pain are faced with. Observing the meeting between a doctor and a patient gives a unique
possibility of seeing what actually happens when ´patients are informed about their options and chooses
one with the help of their doctors. Even though being present in the room potentially could affect the
43
patients or doctors behaviour, none of the doctors or patients seemed disturbed or distracted in any
way.
The observations were summed in a short narrative report and resulted in a draft list of attributes and
formed the basis for some of the topics in the interview-guide used to interview doctors.
In addition to observations, interviews with different HCPs treating back pain patients were performed.
The HCPs represented different approaches to treatment. The interviews were semi-structured, leaving
room for pursuing different topics of interest while ensuring that issues from the observational studies
were touched upon and knowledge to inform the tentative list of potential attributes was gained.
Further, the interviewed served as a way of gaining a deeper understanding of the medical issues in
regards to back pain. The interviews took place in the doctor’s offices and each lasted 1-2 hours. The
interview guide is depicted below in table 3.
Table 5. Interview guide
The interviews resulted in a reduced list of attributes with suggested levels, mirroring the medical
possibilities for each attribute.
3.3 Construction of tasks
On the basis of literature, observations and discussions with experts a list of potential attributes and
levels was formed. This list was then qualified formed into tentative choice profiles and this along with a
number of clarifying questions was the formation for a series of semi-structured interviews. The
interviews took part with patients suffering from low back pain. The patient interviews were performed
•Education
•Job Description
•Experience
•Typical patients
Background information
•Medical Perspective
•Evidence - or lack thereof
•Decision-makingLow Back Pain
•Plausibility of attributes
•Importanceof attributes
•Levels for each attribute
Design of SP-study: Attributes
44
in a hospital ward with back pain patients awaiting HCPs decisions on treatment. Only a few hospitals
keep LBP-patients as inpatients during this period, but in order to get informants, interviews were
performed on one such clinic. The semi-structured interviews contained topics of disease history and
concerns/hopes as well as a test of the questionnaire made as a think-aloud exercise and followed by a
discussion of the different attributes and level and the length of the questionnaire. Each interview lasted
about 1 hour and were recorded and transcribed.
As it will be discussed in section 4, the interviews resulted in a range of changes to the design of and
especially to the framing of the choice experiment. The design was changed to include fewer choice
tasks and attributes while the opt-out option was kept.
Table 6. Final list of attributes and levels
Attribute Levels Hypothesis
Modality Non-surgical
Surgical
-/+
Pain level Same
Less
None
+
++
Problems with
ADL*
Same
Fewer
None
+
++
Risk of relapse 1 in 10
2 in 10
3 in 10
-
--
Time to treatment
effect
1, 3, 6, 12 months -
*ADL = activities of daily living
Different framing approaches were tested and discussed during the interviews, especially in regards to
how to present and describe the risk of relapse from any treatment and how to present the overall task
of making hypothetical choices. This again is further discussed in section 4.
45
3.3 Experimental Design
The above list of attributes and levels were then made into an initial experimental design. The initial
design was an optimal orthogonal design using 18 rows blocked in three with each 6 choice-sets. This
design was then part of a pilot-testing along with the rest of the questionnaire, the development of
which is explained further in section 3.4.
The pilot test took place during 3 weeks in the early spring of 2012 at Spine Centre of Southern
Denmark. This centre was chosen as the site of inclusion as it is the only centre treating spinal diseases
and back pain in the region of southern Denmark and thus has a large uptake and many patients coming
through each day. It also has both a surgical and non-surgical department making the choice of modality
real to every patient entering the centre. Further the centre has a large research unit and is in the
forefront of especially non-surgical treatment in Denmark. The centre was visited numerous times at
different stages of the data collection and an instructive meeting involving all personnel was held at the
initiation of the pilot test.
The pilot test included 17 respondents and the questionnaire was handed to each patient personally
with an explanation of the purpose of the test. The pilot data was then analysed using a simple MNL
model.
3.3.1 Final experimental design
The final experimental design was a Bayesian efficient design using 600 Halton draws and assuming
distributions around the priors obtained from the pilot-study. Effects were assumed to be non-linear for
treatment modality, ADL, pain, and risk and dummy-coding was thus used for these attributes. The
design included 3 alternatives of which alternative 3 was the opt out, and alternative 1 included a
constant. As with the pilot, all attributes were generic in alternative 1 and 2. The design used 18 rows
and d-efficiency as the optimizer and was blocked in 3. The experimental design is not only chosen
based on its more novel approach than standard orthogonal designs, but in particular because of
concerns about sample-size. Rose and Bliemer 260 notes that efficient designs not only maximize the
information on preferences obtained from each choice by a respondent, but also can decrease standard
errors of parameters in such a way that finding a more optimal design can decrease standard errors
more than increases in sample size.
46
3.4 Preference elicitation and instrument design
As stated the SP-experiment as part of a larger questionnaire that was development simultaneously with
the qualitative work aimed at informing the experimental design. The questionnaire thus used questions
from Dallas Pain Questionnaire (DPQ)281. DPQ is a 16-item instrument assessing four aspects of daily
living of chronic back pain patients; day-to-day activities, work and leisure activities, anxiety-depression
status, and social interest. Scale extremities are labelled with specific words (e.g. ‘never’/’all the time’).
The questionnaire also included a pain-scale and items from The Low Back Pain Rating Scale (LBPRS) 282,
which is a rating system designed to evaluate the clinical outcome of LBP patients. The pain component
of the scale is a six question scale divided into two groups of three questions about back pain and three
questions about leg pain. Each item is scored 0-10. The disability component of LBPRS consists of 15
questions evaluating the patient's ability to perform daily activities.
Further the questionnaire included a range of background questions and questions regarding experience
with and attitudes toward treatment and HCPs treating LBP. Many of these questions stemmed from the
literature review.
3.5 Collection of data
At the Spine Centre all patients referred by GPs are met by a nurse. The initial visit thus contains initial
anamnesis, a computer-based short form of the LBPRS and an MRI scan. Subsequent visits include a
multidisciplinary team consultation, in-depth anamneses and clinical examinations. Patients meeting the
inclusion criteria of suffered from neck or back pain for more than two months and not having neck-pain
or acute disc-disease was handed a questionnaire with a pre-paid envelop by the nurse. Thus, the
questionnaire was distributed before the patients had any knowledge about the diagnosis and
treatment path suggested by the experts at the centre. The results of their MRI scans were also
unknown. The questionnaire came with instructions on how to return it by mail and the purpose of the
study. The nurses noted the questionnaire number on their patient-lists that were then kept by the head
nurse and collected to be included in the database including all patients receiving a questionnaire.
Upon the end of the data collection background variables and LBPRS-data on the patients handed a
questionnaire, without returning it was retrieved from the hospital database, enabling a non-response
analysis looking into potential differences between responders and non-responders in regards to age,
gender and average pain.
47
During the data collection the Spine Centre was visited every second week. The visits were used to
remind nurses of sampling strategy and inclusion criteria and to bring more questionnaires if needed. As
the data collection was ended an “evaluation” of the process took place at a staff meeting.
At the end of data-analysis a final presentation of results was held in the summer of 2013.
3.6 Statistical Analysis
The statistical analysis if the SP-data differed in relation to the purpose of analysis and includes both
MNL, MMNL and hybrid models, results of which are presented in the papers in sections 5 and 6. The
focus of analysis was modelling heterogeneity and trying to explain the observed differences in
numerous ways. The full data set was used making an MMNL model and sub-groups for the paper in
section 5, while only respondents answering the whole questionnaire was used in the paper in section 6,
which presents the hybrid model structure and investigates if and how additional data can help explain
heterogeneity with greater power than MMNL models. Analysis were primarily made using Stata12,
while all models presented in section 6 are made using Ox.
The following sections will present the empirical work from start to end in three different papers, which
are then followed by discussion.
48
4 Designing a stated choice experiment. The value of a qualitative
process
Kløjgaard ME, Bech M, Søgaard R
Designing a Stated Choice Experiment: The Value of a Qualitative Process
Journal of Choice Modelling, Volume 5, Issue 2, 2012, Pages 1–18 DOI: 10.1016/S1755-5345(13)70050-2
(Due to journal’s copyright the paper is not presented in this version of the thesis)
101
5 Patients’ preference for treatment of low back pain – a discrete choice
experiment
Kløjgaard ME, Manniche C, Pedersen LB, Bech M, Søgaard R
Patient preferences for treatment of low back pain-a discrete choice experiment.
Value Health. 2014 Jun;17(4):390-6. doi: 10.1016/j.jval.2014.01.005. Epub 2014 Apr 21.
(Due to journal’s copyright the paper is not presented in this version of the thesis)
102
6 Understanding the formation and influence of attitudes in patients’
treatment choices for lower back pain: testing the benefits of a hybrid
choice model approach
Kløjgaard ME, Hess S
Understanding the formation and influence of attitudes in patients' treatment choices for lower back
pain: testing the benefits of a hybrid choice model approach.
Soc Sci Med. 2014 Aug;114:138-50. doi: 10.1016/j.socscimed.2014.05.058. Epub 2014 Jun 2.
(Due to journal’s copyright the paper is not presented in this version of the thesis)
103
7 Discussion and future perspectives
This section will discuss the results shown in sections 4-6 in relation to the existing knowledge and hence
the contributions of the present studies to the field. Further, a section on limitations – specific and in
general, is presented, which also serves as a discussion of validity and a suggestion of one topic of future
research, namely collecting revealed preference data in a health setting. This section also discusses the
possible practical use of the elicited patient preferences and how knowledge on preferences can be
brought more into medical decision making. Further, the patient and the relationship and influence
on/of the HCPs, is discussed. This is followed by a discussion on who actually shares decision making and
how it is influenced by peers, a discussion that follows up on results from section 6. Also, this section
discusses some more methodological issues that came across as especially important during the studies,
leading to this thesis. One such topic is the feasibility of assuming utility maximisation and another is the
importance of heterogeneity.
7.1 Results and existing knowledge
The results presented in chapters 4 and 5 and 6 feed into the existing literature presented in section 2.2
in a number of different ways.
Firstly, literature points to some connection between patients’ preferences and treatment outcomes.
This is very interesting in regards to the lack of use of sophisticated methods to elicit preferences. In
itself the results of the empirical work thus provide a useful insight into factors influencing actual
treatment outcomes for LBP patients. Not only by investigating preferences including how utility is
traded, but also by further exploring factors influencing preferences, adding the importance of attitudes
and perceptions to the factor influencing preferences uncovered by literature, i.e. information and
previous experience. The latter being confirmed by the study presented in section 6.
Literature also suggests that patients prefer being involved in decision making to a great degree. In
literature, decision-aid tools have been used as one way of facilitating the process. The issue of how
such decision-aids can be made to include patients’ preferences has been dedicated a separate
discussion in section 7.2.1.
7.1.1 Limitations
There are several limitations to the studies presented in this thesis, some of which are specific to each
empirical contribution and some more general to the field of SP.
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This and the following section thus deal with a range of limits surrounding the produced results, both
methodologically and in relation to the papers in sections 4-6.
As discussed in the introduction, an overall limitation is the lack of a clear definition of back pain related
disorders. A number of terms, diagnosis and categories are thus used to categorize LBP patients, making
literature searches challenging and potentially limited in terms of precision. Also, a rather extensive
range of HCPs deal with patients with LBP, which again could influence the systematic literature review.
Further, the lack of consensus on terms and imprecisions resulting from it, limits extrapolation of
research within the field. This along with similar factors like e.g. exact content of treatment and area
variation makes results presented in this thesis primarily representative of the setting in which they
were collected. This is a serious limitation of scope, characterizing not only this thesis but also research
performed in this field in general. Despite the limitation, this thesis does provide new insight into
literature and contributes to the field in numerous ways as is has been discussed in several sections.
The extrapolation issue is not only present in the description of existing knowledge, but also influence
the potential use of this knowledge as a basis for generating and designing new studies in the field. Thus
an extra effort was out into generating solid qualitative knowledge of the field in Denmark. As the
existing literature on both SP and LBP showed, albeit present, qualitative studies are most often
concerned with investigating specific treatments and not on how the qualitative methods can help
inform a design of a larger investigation. Thus the purpose of the qualitative study presented in section
4, was not only the qualitative work in itself. Had it been more data would have been collected.
Including both interviews, observations and tests of questionnaire. But rather, the purpose was both to
gain insights into important issues, behaviours and perceptions of patients and HCPs and to investigate
how much or to what degree the different qualitative methods help shape the design of the SP study.
However, it is a limitation that a relatively small number of interviews were performed with patients and
HCPs. This was due to the limited time frame in which the qualitative work took place and the ever
changing number of patients eligible for interviewing. However, as stated in the paper, including all the
described steps, leaves little doubt that the chance of not including an important attribute or add
inappropriate levels is obviously substantially smaller than if the process had been limited or based on
experts’ opinions or researchers’ opinions and literature 283. Also, the paper does provide an insight into
a much overlooked process of qualitative work prior to designing a stated choice experiment and deals
with several different methods to do so.
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The SP-based empirical contributions in section 5 and 6, contributes with important knowledge of
patients preferences of LBP. However, a couple of different limitations due to the data collection are
present in the studies.
Firstly, the survey mode itself might have been a limitation to as paper surveys demands the respondent
to be able to read and understand the survey and to post it after answering. The method was chosen
however, due to the target population and hence possible lack of internet and computer skills as well as
practical reasons, as the visits to the hospital was an open opportunity to distribute the surveys. Ideally
an interview based method or the like could have been used possibly affecting the response rate
positively. The respondents however, were given a phone number to call if needed, which some did.
Another factor influencing response is the matter of missing data or non-response. A matter that
becomes even more important in studies using latent variables as non-response to any question used to
form a latent variable affects results. Again, interviewer-assisted data collection might have helped as it
has shown to generate less missing data 284. However, interviewing the number of patients needed for
this research in the middle of a busy clinical setting would not only have been utterly expensive but also
potentially very difficult. In the paper in section 4 all respondents are kept in analysis, even if all
respondents did not answer all choice sets, however, in the paper in section 5, respondents with missing
data either in choice sets or in questions used to form the latent variable is removed. In general,
researchers do not like to remove data, and there are no golden rules as to how non-response needs
treating, but some researchers argue that respondents who exhibit this sort of behaviour can distort
analysis and significantly bias results and thus should be removed 285. How much non-response to a few
or more questions in a survey actually distorts or affects results is still an unresolved matter in literature.
Also, it has been discussed in literature the site of inclusion might influence on results. Data was
gathered at a hospital ward, which specializes in non-surgical treatment of back-pain with the possibility
of surgical referral. This could be a plausible explanatory reason for the preferences expressed by the
respondents. The tendency of patients preferring whatever the clinic in which they are surveyed offers,
suggests that patients in general might just prefer what they are given or expected to be given. As
stated, the timing of inclusion in this study was deliberately done so that patients were captured early in
hospital sector pathway and hence before having been influenced by hospital doctors. This was done to
accommodate for any effects of pre-existing notions of treatments paths and in that light the results
might better reflect the true preference of a back pain patient. However, most patients had seen other
HCPs and their GP numerous times before referral, and also patients had also received advice from
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friends and family. Thus patients are influenced by others by the time they were included in the SP
studies. However, this is most likely also the case for the patients surveyed in the existing literature.
In general another limitation of the thesis is the lack of uncovered knowledge in regards to patient-HCP
relationship and shared decision-making. Interesting future studies could further investigate how well
HCPs include, know and act upon patients preferences and uncover potential discrepancies in
perceptions and attitudes driving the preferences of the patients and HCPs. These issues are touched
upon in sections 7.2 and 7.3.
The limitations section continues with a discussion of validity of results from the SP-studies.
7.1.1.1 Are stated preferences valid?
Another limitation, in another kind of scope, feed into the discussion on the so-called hypothetical bias.
As mentioned, stated preference research has been criticized for its hypothetical nature. The predictive
validity of SP-experiments refers to the ability to predict actual market behaviour. In other words, SP-
experiments are often designed to mimic real market choices and a valid SP-study predicts what
respondents would actually choose in real life. Data from actual choices on markets is known as
revealed preferences (RP). Dealing with RP also suffers from a different issue, as one doesn’t have the
opportunity to choose attributes or their levels, but only the ability to observe the actual market. Some
researchers have tried to combine the two types of data in SP+RP models, to utilize the strengths of
both types of data 286.
Testing whether a SP-experiment is has a good predictive validity can be difficult. So far, researchers
have compared aggregate differences in model outcomes from SP-experiments and data gathered on
the real market 287–289. Although aggregate analysis gives a hint on whether hypothetical bias is present it
leaves researchers without knowledge on its prevalence, or why it occurs. This is still an issue to pursue
in research and could potentially be done in relation to the studies presented in this thesis, as proposed
in the end of this section. Whether or not a SP-experiment is able to predict true preferences, have
been described to be a matter of how much respondents care about or feel responsible for their
answers to the hypothetical choices. It has been argued that the lack of real consequence for the
hypothetical choices distort behaviour 290,291 and a range of studies have been looking into this lack of
consequence292,293. In one study respondents were informed that one choice would randomly be
selected as binding and this was hypothesized to be an incentive for being honest when choosing 290.
This approach is not feasible in some studies on health care, in particular when there is no market to
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observe, and other researchers have proposed other ways of trying to ensure external validity. One of
which is framing, making the respondent aware of the importance of truthful answers, another is
certainty scales, making the respondents state their certainty either after each choice set or after the
full number of choice sets. Most commonly the scale is a simple 1-10 numeric scale and it is used to
either remove or recode uncertain individuals to get a more precise measure of preferences and reduce
hypothetical bias. However, there is no consensus on when a respondent can be said to be uncertain
and different approaches or cut-offs will most probably result in different outcomes. Some studies
shows that a good cut-off on a 1-10 scale is 7 or 8 289,294.
In the survey used in this thesis a 1-10 certainty scale was included after the completion of the entire
choice tasks. This pose another problem, as it is uncertain whether respondents answer about certainty
of the final choice set or overall certainty – which again is probably different from choice set to choice
set. When removing respondents stated 6 or less on the scale no significant difference from the full
sample on MRS was found. However, the validity of using certainty scales to test for anything – and in
particular to lessen hypothetical bias is limited.
The predictive power of the studies presented in this thesis is unknown, but of interest, not only as a
methodological test or curiosity, but also to gain knowledge on the persistence of initial preferences and
how often these are changed. Further, following-up on actual treatment results, and testing whether or
not these were correlated with receiving a preferred treatment would be a valid contribution to the
range of studies presented in section 2.2.3, dealing with how patients’ preferences potentially affect
treatment outcomes.
Comparing SP to RP is rarely ever done in health economics (see 295 for a rare exemption), and it has
been suggested that finding imaginative solutions to test predictive value and external validity should be
an issue of research in health economics 296 possibly helping to yield more credibility to results obtained
from SP-studies in health 297.
The lack of a real market for treatment of LBP makes observations difficult and hence testing external
validity or predictive value of this study hard. An interesting future study following the presented work
from this thesis could be a register-based follow-up of treatments chosen for and by patient
respondents from section 5 and 6. This could be done by observing the actual treatment choices with
the preferences collected ex-ante giving inside into whether the cohort or the different segments did
receive their preferred treatment and providing further insight into any potential better clinical
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outcomes for those receiving their preferred treatment compared to those who did not. Obviously such
a study would need to take the clinical reference points for patients into account. The Danish social
security and health insurance system provides a unique opportunity of following patients and treatment
choices as each patient is use of health care resources is registered by the health authorities. This
unique source of data is already used extensively in health economic research in Denmark but has yet to
be used as a mean comparing SP-choices with real life decisions.
7.2 Policy implications
Results generated by preference studies, as those presented in sections 5 and 6, arguably have a role to
play in health policy, especially in making clinical guidelines 15,16. Formulating clinical guidelines has been
an important factor in modern medicine. Methods to do so, have evolved and is has been acknowledged
that a huge challenge in guideline formulation is to create widely applicable, but incorporates or respect
patient heterogeneity 16.
Lately, guidelines have tried to accomplish this, by becoming more of an active player in decision making
processes as tried with Map of Medicine or the American Guided Patient Support(GPS) and Medical
Decision Support (MDS) and by introducing guideline knowledge to patients as done in the literature
with videos 298–300.
However, it might be possible to bring preferences even closer to decisions, which is discussed in this
section.
7.2.1 Putting preferences to practice
As suggested, one way of incorporating preferences is to make preference-informed clinical guidelines
16. Acknowledging that differences in preferences might lead to differences in preferred therapy or
treatment and thus clinical guidelines in fields in which patients’ preferences has the possibility of
playing a prominent role should be preference-based. This approach has the benefit of easily bringing
preferences to the attention of the HCPs making treatment decisions. Guidelines are a common way of
bringing evidence into decisions and in most countries HCPs are bound to follow clinical guidelines.
However, even if health professionals all agree that informed choices and patient-centred care is
important and correct, and with the Grading of Recommendations Assessment, Development, and
Evaluation (GRADE) 301, being widely implemented, including in Denmark, this development is further
encouraged, it still proofs difficult to actually implement patient-centred choices in health care on a
more practical level 302–304. Greatly so, due to the great variation or heterogeneity within the target
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population. Hence, making preference part of a clinical guideline only offers a generic view of
preference for a target group, who not only differs, but also changes – sometimes faster than guidelines.
Therefore eliciting individual preference on a site of treatment has the potential of facilitation patient-
centred treatment and truly shared decision-making. Future research could be aimed at implementing
and evaluating a computer-based system. Such an approach would be more practical and patient-near,
individualizing the HCP-patient meeting, by surveying preferences at a clinic. So far, to my greatest
knowledge, this has not been implemented in any clinical settings in Denmark – or maybe even
anywhere, but the means to do so are available. SP-approaches are not only being used in research in
transport, environment and health, but to a great extent also in marketing. In marketing, eliciting
preferences needs to be quick and easy, especially if it happens, not as part of a survey, but as part of a
costumer profiling prior to buying a product. This has been used for a range of companies, who’ve
incorporated short SP-surveys on their websites to target products to a potential buyer. Letting patients
do something similar before a consultation might be beneficial for the communication between an HCP
and a patient and could help uncover any unrealistic preferences or preferences of which an HCP
doesn’t think of. Often, patients are put in front of a computer to enter their basic information and
incorporating some sort of preference-elicitation mechanism, could be easily done. Thus, a small task of
trading off potential treatment outcomes and a simple algorithm helping the HCP to see the importance
of different treatment or outcome characteristics could benefit the information
7.3 The patient and the medical experts
One important issue the literature is concerned with and that the empirical work touched upon is the
patient-HCP relationship. It is this relationship that determines treatment paths and has the potential of
taking preferences into account in decision making. Already the relationship is characterised by
asymmetry of information on both sides. Arguably, this gap can be broadened, when HCPs are not
guided by solid evidence and decisions but are influenced by attitudes and perceptions. It has been
shown that HCPs do not adhere to guidelines on LBP in their treatment suggestions or referral practice
305–309. Instead, they make decisions based on perceptions, feelings or attitudes. Decisions that are
sometimes to the detriment of patients or society, leading to e.g. inappropriate referral practices and
that definitely shape and influence patient pathways.
Further, literature point to a discrepancy between patients’ and HCPs’ preferences, not only in regards
to causation, management or communication, but also in relation to actual treatment 310–314. And even if
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it has been argued that mere congruence of preferences does not uncover whether HCPs are focused on
incorporating their patients’ preferences, this discrepancy is of some concern 315.
Different practice settings or differences between groups of professionals may explain some of the
observed trends 316. However, it seems certain that HCPs are highly driven by beliefs and perceptions in
their recommendations to patients 55,195,316 and that their different attitudes e.g. towards origin and
physiology of pain 317–319 or fear-avoidance beliefs 54,195,320 or even completely non-clinical aspects like
giving patients “peace-of-mind”309, are heavily affecting the decisions made by HCPs when treating
patients with LBP. It appears to be likely that HCPs preferences serves as barriers or in some instances
facilitators of good treatment outcomes for patients.
Another factor that is pointed out in both literature, my studies and by the clinicians, is that patients
most in favour of surgery are in some ways more desperate patients, with sick-leaves and low SES.
Media, also suggest that society puts pressure on these candidates as patients can lose sick-leave-
benefits if they don’t accept surgery 321 even though evidence does not suggest a (quicker) recovery or
any recovery for surgical candidates and further suggest that those benefitting most from surgery – if
any - are the well-functioning patients 322.
This suggests a very important task of communication not only from HCP to patient, but also from HCP
to societal institutions, as HCPs are not necessarily informed about a patient’s social situation.
Knowledge of these social impacts on preferences as presented in section 6, might be helpful in
overcoming asymmetry of information.
A final point is that of the lack of influence by the GP. It was expected from the results presented in
section 6 that patients would be guided by a surgical recommendation from their GP, but this was not
the case. This could suggest a changing role for the GP or maybe reflect a frustration from patients who
had all had numerous visits during their pathway to the expert-clinic. As noted, interestingly patients are
guided in their preferences by their friends and family, who even if they mostly hadn’t recommended
anything or had recommend non-surgical treatment, had a significant impact on patients’ preferences
had they recommended surgery. This influence by peers is acknowledges in decision making research
and work has been commenced to try to model decisions shared by more than one individual or made in
groups. This is the topic of the following section.
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7.3.1 Who is sharing decision-making?
One of the more interesting results found in the paper presented in section 6, was the influence of
friends and family on preferences. Even if the result is subject of the usual precautions of limited data
and statistical uncertainties, it seems very plausible that decisions on health are not necessarily taken by
the patient in question, but could be taken jointly with that patient’s family and/or friends trading
against one and another until an optimal “group solution” is reached. Also, this is the ideal process of
decision making between a patient and his/hers HCP. It has been recognized for some time that
decisions are not always simply made by one individual, but still, the literature on group based models
of choice is relatively underdeveloped. However, the subject of joint decision-making has started gaining
attention in the SP-literature in other fields than health economics, acknowledging that many decisions,
such as vacation choices are often made by a family/couple and not individually 323. This could also cause
the hypothetical bias already discussed, as results from analyses based on data obtained with the
traditional mode asking one individual as representative of an entity, could differ from real world joint
decisions.
How the interactions of individual members of a group or entity, influence the joint decision making and
preference formation, represents an important dimension of understanding behaviour. This has
resulted in attention within the choice modelling literature to examine the role that social interactions
play in terms of preference formation and how these interactions can be caught – and modelled 324,325.
Simply aggregating preferences of individual members of a group into a “group”-preference is unlikely
to result in a valid or efficient choice as a decision made by more than one individual is subject of
negotiation and interaction and differences in strength of preference an individual has for the
alternatives in question. The latter being some of what we try to capture on certainty scales.
Given that many choices are a function of group decisions, understanding the dynamic nature of group
preference formation is essential in gaining accurate valuations for attributes and alternatives. The
universal nature of group decision making means that choices under such circumstances are of
particular interest to social scientists in any discipline.
Three different methodologies for exploring shared or joint decision making has been proposed. One is
the interactive agency choice experiments (IACE), another the group negotiated choice experiments
(GNCE) and a third the minimum information group inference (MIGI).
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GCNE is designed to represent the true group decision process with members of a group discussing
alternatives and agreeing on one common choice. IACE tries to make that process more real by
examining changes in preference structures resulting from group interaction step for step. This method
does not require the actual participants to meet as the method uses a series of choice tasks for each
member of a group to complete, while receiving continuous feedback of other members’ choices until
the group reaches consensus. It is reasonable to assume that each individual has a varying amount of
knowledge of the others’ preferences and a view on the influence of each agent and the completion of
iterative rounds of interactive review, choice, review, revision, etc. progress the level of knowledge and
influence both of which may change for each individual. The MIGI approach is parallel to IACE, but
makes group members rank the various alternatives and state their acceptable outcomes, enabling the
researcher to project what the group’s outcome might be.
Attempting to reproduce decisions in groups in an SP-experimental setting is potentially difficult and
costly, but researchers have explored the methods and proposed ways. Within the IACE framework Rose
and Hensher326 made respondents interact at various stages in the decision making process and bring
different perspectives and influences to the formation of preferences and in the final group choice. It
has even been shown that estimates derived from this process does not suffer from hypothetical bias as
they are equivalent to RP data 327. The modelling techniques that Rose and Hensher326 employ for IACE
data are also transferable to the MIGI and GNCE framework.
To overcome the potential costs of performing IACE, MIGI was introduced by Hensher and others in
2007 328,329. Here, the researcher establishes the trade-offs that each agent is willing to make. This is
accomplished by presenting interdependent respondents with the fundamental elements of interactive
choice settings, coordinating their stated preference rankings into projected joint choice outcomes and
then examining these outcomes in reference to the prevailing characteristics of the relationship
between the decision makers.
Literature proposes a number of different methods to model the influence of peers on choices. All of
which could proof beneficial in health economic SP-research. Future research should use these novel
techniques to investigate the influence of friends and family on a patient’s preferences and maybe even
more interestingly, the joint decision making taking place between HCPs and patients. However, before
individual utilities can be compared and meaningful implications about influences be made, the impact
of scaling effects need to be acknowledged as presented in section 2.3.3.1 and further discussed in
section 7.4.2
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7.4 Methodological issues
A range of methodological issues were raised during the research undertaken in this thesis, most of
which has been already addressed. Two issues, however, came to stand out, one - heterogeneity, has
already been touched upon, another - the feasibility of assuming utility maximizing behaviour has yet to
be discussed. The following sections will discuss alternatives to utility maximisation and further
elucidate the topic of preference as well as scale heterogeneity, the latter picking up from the discussion
on group decisions in section 7.3.1.
7.4.1 Are we utility-maximising?
In the qualitative work, it became apparent that assuming utility maximizing behaviour might not be in
accordance with decision rules applied in real life. This notion was not pursued as standard SP-work
suggested otherwise and knowledge on alternatives to RUM had yet to be gained. However, research
fields outside health economics have increasingly looked into alternatives, focusing especially on
random regret minimization (RRM) 330. This alternative behavioural paradigm emerged in the travel
choice literature. RRM assumes that choices are motivated by the desire to avoid scenarios made up
from unwanted attribute levels. So the chosen alternative is outperformed by the non-chosen one. Or
simply put, regret is what you experience when a foregone alternative performs better than the chosen
one.
Choice theories and choice models build on regret based choice, hypothesize that individuals try to
minimize regret, and not maximize utility, when choosing. Thus RRM models assume that as
alternatives are characterized in terms of multiple attributes, which implies that trade-off to be made by
the respondent, there will be regret in the sense that there will generally be at least one non-chosen
alternative that outperforms a chosen one in terms of one or more attributes 330–332.
The RRM approach has only just been introduced in health economics 333 and has yet to be tested and
applied in a series of studies, investigating whether it outperforms the RUM framework. RUM still have
the advantage of being standard in most software and in choice situations in which only two alternatives
are present, RUM and RRM measures are the same and even if respondents qualitatively did talk of
regret as a basis for choosing, using RUM as the decision framework for the studies in this PhD was
perfectly adequate. But when designing SP-studies one needs to recognise the impact of RRM and how
different attributes are perceived differently and chosen between differently 331.
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Taking the notion of alternative decision rules even further, some authors argue that a single generic
framework is never appropriate no matter its premises. Respondents might use a range of decision
rules to answer different choice tasks. Recent studies have highlighted the importance of allowing for
heterogeneity in terms of decision-making strategies 334,335. In the context of RRM, this has led to
modelling approaches which allow for different segments of the population to choose in a way more in
line with RUM premises, while others may choose in a way more in line with RRM premises 336,337.
Regardless of what decision framework is chosen in probably will never adequately be able to explain
the process by which choices are made in real live. However, notwithstanding the obvious value of these
approaches being able to capture heterogeneity, they only capture one or two dimensions of
heterogeneity (across decision makers and/or decision contexts). Testing which decision rule or whether
a mix of rules performs better is somewhat just another way of looking into possible explanations for
responses. Investigating and capturing heterogeneity in decision rules and in tastes and scale continues
to be at the forefront of choice research. The next section further discusses the issue of taste and scale
heterogeneity.
7.4.2 Heterogeneity
From the studies presented in sections 5 and 6 it is apparent that heterogeneity alters results of choice
data. In the review by De Bekker Grob et al. 17 it is concluded that main-effects models continue to
dominate , and despite increasing popularity, models taken account of some heterogeneity like nested
logit, mixed logit and latent class models are still not as commonly used as simple MNL models. It is
further concluded that heterogeneity is the single most salient feature in micro level consumer demand.
Also the LC, MXL, NL models acquire assumptions about e.g. what parameters to be random with what
distribution or number of classes etc. which needs to be done with care and in the most informed way
possible.
More advanced models, linking taste heterogeneity to underlying values and attitudes have only just
begun to emerge into health economics, with the paper presented in section 6 as a first attempt.
As stated, taste heterogeneity - or decision rule heterogeneity – is not the only sorts of heterogeneity
one need to look out for. Researchers have increasingly also acknowledged that scale heterogeneity is
an issue too. This has been tried to have been solved by the so-called Generalised Multinomial Logit
model (GMNL) 338, which however does not separate taste and scale heterogeneity as intended as
demonstrated by Hess and Rose in 2012 339.
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Scale heterogeneity embodies differences in behaviour of each respondent over repeated choice
observations. Differences in scale can be influenced by the mere variation in respondents, i.e. different
levels of experience, knowledge or cognitive ability or even be feeling one way or another while
completing the survey. Scale is inversely linked to the error variances. Hence, large scale effects are
linked to small error variances and vice versa. As stated, attempts have been made to try to model scale
separately from taste heterogeneity, but to date, models can only allow for differences in scale between
subsets of respondents i.e. in the nested logit or error component models. Accounting for scale
heterogeneity between respondents has been yet to be studied further in the literature clever ways
around the difficulties in estimation still needs to be found. Hence, the models used to evaluate the data
gathered from patients and HCPs, accounting for taste heterogeneity and providing explanations for the
observed differences is not just state-of-the-practice but state-of-the-art.
7.5 Concluding remarks
From dealing with the field of SP and LBP a number of important lessons have been learned. Firstly in
regards to typology of both fields, which is reflected in the papers that changes their wording
concurrently with my reading into literature on back pain and especially literature from other fields of
research on choice modelling. It quickly became apparent that the wide use of SP methods makes it
necessary to read into fields of literature way outside health economics to avoid overlap and to be able
to contribute significantly to health economics. This task is often overlooked by researchers and
mistakes are repeated.
Another thing that became apparent was the dilemma of perfecting models while maintaining or
increasing use of SP results. Accounting for heterogeneity has made modelling choice data a highly
complex task. Even with “simpler” models allowing for only taste heterogeneity like latent class and
mixed logits, require a number of decisions to be made by researchers, i.e. number of classes, draws and
random/fixed parameters and their distributions. Incorporating latent variables, multiple decision
makers or group choices or accounting for scale heterogeneity, makes models very complicated, harder
to estimate and analyse and understand. The latter being especially important in regards to making
policy recommendations on the basis of such models. So while there’s a definite room to grow and
advance in choice modelling in health economics, this move might counteract another movement,
namely the challenge of incorporating SP in economic evaluations and political decision making.
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8 Conclusion
This thesis presented a collection of journal manuscripts and an independent overview adding to the
clarification and elaboration of the issues.
The objective of the PhD study has been two-fold. Firstly, it was the aim to explore the preferences of
patients with back pain when facing a choice of trade-offs between treatment options, outcomes and
risk. Secondly, it was an objective to add to the methodological work in the field of preference elicitation
by aiming at contributing with added focus on qualitative work prior to gathering quantitative data on
preferences and by focusing on the complex formation of preferences for patients including
perceptions’ and attitudes’ influence on choices.
The objectives of the PhD study were met in different ways.
First, an introduction presented not only the field of interest, but also a thorough description of the
challenges of LBP and SP-studies. This knowledge served as a background for the presentation of a
thorough and systematic description of existing knowledge of preferences in relation to LBP and thus
helps to answer the research question of eliciting preferences for treatment of low back pain, based on
existing knowledge. Also an introduction to stated-preference-elicitation method of choice modelling
was given, presenting a step-wise process of generating SP-studies and including an introduction to
qualitative methods used in that process. The latter was highlighted numerous times both by thoroughly
describing all design measures taken in the empirical work from the formulation of a research question
to statistical analysis and by the first empirical paper contribution The paper presented the design of the
stated preference or discrete choice experiment and demonstrated how qualitative work can
significantly impact and guide a design, making it clear that a less thorough qualitative process would
have resulted in a less useable and valid design.
The examination of patients’ preferences for treatment of LBP was also done empirically by eliciting
patients’ preferences in a clinical setting. As expected, results show that respondents assign positive
utilities to positive treatment outcomes and disutility to higher risks and longer wait for effects of
treatment. Respondents also showed a disutility towards surgical intervention and were on average
willing to wait two years for effect of treatment to avoid surgery, while they were willing to wait almost
three years for effect of treatment, if the treatment made them free of pain. The mixed logit model
captured significant heterogeneity within the sample for outcomes regarding pain reduction and ability
to pursue activities of daily living and in relation to treatment modality. The sub-group analysis showed
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differences in willingness to wait, especially in regards to treatment modality and particularly in groups
of high/low pain at time of data collection and respondents with an ex-ante preference for surgery/non-
surgery. The results indicated that pain relief is the most important factor in choice of treatment,
followed by treatment modality and ability to perform activities of daily living while risk of relapse was
the least important factor. This is not only valuable information for practitioners in their communication
with patients and everyday decision-making, but also important information for policy makers who seek
to ensure a utility-maximizing mix of treatment options for patients. The section empirically answers the
first research question by investigating patients’ preferences for treatment of low back pain.
The investigation of the influence of attitudes and perceptions in patients’ treatment choices for lower
back pain and testing the benefits of a hybrid choice model approach, was also done empirically. The
results show that while the hybrid structures provide some further insights into the formation of
attitudes, and some gains in efficiency, the overall results remain largely unaffected. Interestingly, the
commonly used socio-demographic variables did not prove to be of significant influence, while
recommendations of surgery from GPs or friends and family had a positive impact in simpler models,
while recommendations from friends and family are of even greater importance when looking at the
hybrid model. Results thus suggest that patients are significantly influenced by peers and less so by
professionals.
All results were discussed in relation to existing knowledge. And further discussions of limitations and of
the validity of the stated choice method was performed. Also, discussions on how results can be put into
practice and on the relationship between patients and HCPs were presented, by introducing novel
approaches to thinking about choices, derived from literature outside of health economics.
Conclusively this thesis makes several contributions to the field of modelling health care choices in
general and preferences in regard to treatment for low back pain in particular.
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9 Summary
This thesis is divided into 10 sections providing empirical, theoretical and methodological insight into the
field of preferences elicitation in health economics focusing on treatment of low back pain.
This first section presented the background for this thesis. It introduced the field of back pain, providing
insight into the challenges in decision making in regards to conflicting evidence and the presence of
other factors influencing decisions and outcomes. Literature dealing with back pain was presented and
in summary this pointed to a widely prevalent disorder with severe consequences for sufferers and
society. Persons suffering from back pain often belong to lower social groups and have an extensive use
of health care services. Back pain pathogenesis is multifaceted including both hereditary factors and
lifestyle and treatments are as multifaceted including pain reducing efforts as well as non-surgical cross-
disciplinary management and surgical management. The latter being subject to much discussion
including in a Danish context in which recent changes to guidelines have put focus on finding the right
candidates for surgery.
The research questions of both empirical and methodological matter were presented as a two-fold
effort investigating patients’ preferences for treatment of low back pain and contributing to the field of
preference elicitation in health care in two distinct ways; by focusing on the much overlooked
qualitative work prior to designing stated preference and by exploring the importance of and the
process by which attitudes and perceptions are influencing the choices made by patients.
The introduction also highlighted how the work in this thesis relates to current and historical trends in
investigating hypothetical choices in health care and it presented the contribution to the field while
highlighting the interdependency of the three empirical papers.
The second section provided an introduction to the methods used and the existing knowledge on
patients’ preferences in relation to LBP and how preferences affect outcomes. Observing choices, tell us
something about what people find important. By choosing something over something else it can be
hypothesized that the preferred option gives more utility. It is assume that individuals choose based on
rational thought processes and by trading the positives/negatives of each option available. Also, it is
assumed that some of the factors leading to a choice are observable why some are hidden from a
researcher. Making stated preferences studies enables researchers to include otherwise unobservable,
but key characteristics into a hypothetical, yet plausible choice scenario.
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Some research has already been done in relation to preferences for LBP and conclusively literature point
to a field acknowledging the fact that somehow patients’ preferences, expectations and beliefs matter
and can affect treatment outcomes. However, only one existing study uses traditional preference
elicitation methods while the majority simply asks patients to state their preference.
When choosing treatments patients are influenced by previous experience and the level of information
can shape preferences. Patients seem to prefer skilled and experienced HCPs who take them seriously
and communicate clearly. If HCPs exhibit uncertainty this can make patients lose faith and find
information elsewhere.
Getting people to state preferences by observing hypothetical choices is a valid and well-established
way of obtaining useful knowledge about peoples’ motivations and wishes and the method is widely
used within a range of research fields. Designing and analysing stated preference studies calls for a
number of methodological choices to be made by the researcher. As the choice of design and model for
analysis affects results these choices are crucial and should be based on insight into literature as well as
thorough qualitative work looking into the field of interest. Firstly choosing the right attributes and
levels to include in the choice sets is a core task but researcher also need to focus on the presentation of
the choice sets and how these are framed. Further an experimental design must be made combining the
attribute levels into hypothetical scenarios. This can be done in a range of different ways, each with
different advantages and disadvantages. Previously, orthogonal designs were accepted as the best
design, but experimental design has evolved into focusing on maximizing the information on
preferences obtained from each choice made by a respondent over all observations. Decisions on
whether the design should include an option to opt out of choosing and how this should be formulated
should rely on mimicking the real life choice as perfectly as possible. Also researchers should decide
upon whether the choices should be labelled or not depending on whether a label appropriately mimics
the market and does not make respondents disregard attributes and choose based on labels only.
Modelling results from SP-studies used to rely on simple models excluding preference heterogeneity,
but much research has put into modelling taste heterogeneity in a number of ways. Primarily by simply
allowing utility estimates to have random distributions but also by linking choice response to latent
attitudes and perceptions in hybrid structures. In all cases one needs to be observant of potential issues
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of scale influencing observed differences, which is often stated as a reason for looking into marginal
rates of substitution, not influenced by scale.
The third section presented the design and data collection for the choice experiment used to generate
data for the empirical contributions. The section introduces empirical details on each step of the
process, highlighting the use of qualitative work that went into the process, and thoroughly describing
all design measures taken in the empirical work from the formulation of a research question to
statistical analysis.
The fourth section presented the first empirical and methodological contribution in a journal paper
format. The section provides an important insight into the essential qualitative work needed to design a
SP-experiment involving the developing, testing and optimizing of the survey. This process is important
for the success of the experiment and the validity of the results, but it is often not reported thoroughly.
The paper demonstrates how qualitative work can significantly impact and guide a design, making it
clear that a less thorough qualitative process would have resulted in a less useable and valid design. A
range of methods were used to gain knowledge prior to making an experimental design. These included
a literature review, fieldwork in clinical departments in Danish hospitals and was supplemented by
qualitative interviews with patients and doctors. The process help select the attributes included in the
experiment and the appropriate levels and also allowed for testing the framing and presentation of
choice sets. It helped determine not to label the experiment as the labels guided choices too much and
made respondents disregard attributes and it helped explore the inclusion of an opt out option which
was kept to mimic real life although disregard by respondents in the think aloud exercises.
Each step of the qualitative process helped guide the final design and it is useful for researchers to
perform similar research prior to designing SP-experiments as validity is increased.
The fifth section presented the empirical investigation of patients’ preferences for treatment of back
pain based on the design developed in the qualitative process. The journal paper argues that since
treatment decisions are distorted by conflicting evidence the inclusion of patient preferences in
decision- and policy making should be emphasized. It builds on results from the SP experiment
conducted in patients referred to a regional spine centre. Analysis was performed using Mixed Logit
models and a sub-group analysis further explored the willingness to wait by dividing respondents into
socio-demographic and disease related categories.
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As expected, results show that respondents assign positive utilities to positive treatment outcomes and
disutility to higher risks and longer wait for effects of treatment. Respondents also showed a disutility
towards surgical intervention and were on average willing to wait two years for effect of treatment to
avoid surgery, while they were willing to wait almost three years for effect of treatment, if the
treatment made them free of pain. The mixed logit model captured significant heterogeneity within the
sample for outcomes regarding pain reduction and ability to pursue activities of daily living and in
relation to treatment modality. The sub-group analysis showed differences in willingness to wait,
especially in regards to treatment modality and particularly in groups of high/low pain at time of data
collection and respondents with an ex-ante preference for surgery/non-surgery. The results indicated
that pain relief is the most important factor in choice of treatment, followed by treatment modality and
ability to perform activities of daily living while risk of relapse was the least important factor. This is not
only valuable information for practitioners in their communication with patients and everyday decision-
making, but also important information for policy makers who seek to ensure a utility-maximizing mix of
treatment options for patients.
The sixth section investigated the role of patients’ perceptions in treatment choice. The section served
as the second methodological contribution of introducing an integration of choice and a latent variable
in a hybrid choice model approach.
The paper argues that, possibly particularly in scenarios where clinical evidence is limited or not clear
cut, perceptions that patient forms, either through past experience or through discussion with other
patients and/or medical experts, will play a major role in shaping decisions. The paper thus investigates
the potential benefits in capturing the role that perceptions and attitudes may have in explaining
treatment choices made by patients by including a latent attitude in a joint model of the formation of
perceptions and of the choices. The findings from this model are then contrasted to structures allowing
for simple random heterogeneity.
The results show that while the hybrid structures provide some further insights into the formation of
attitudes, and some gains in efficiency, the overall results remain largely unaffected. Interestingly, the
commonly used socio-demographic variables did not prove to be of significant influence, while
recommendations of surgery from GPs or friends and family had a positive impact in simpler models,
while recommendations from friends and family are of even greater importance when looking at the
hybrid model. Results thus suggest that patients are significantly influenced by peers and less so by
professionals.
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The seventh section presented a discussion on how the empirical knowledge links to literature. One
important result is the link between perceptions and attitudes as a driving force for treatment choices
which is observed in numerous studies and confirmed in the study presented in section 6. It was also
shown by that same study that patients were less influenced by recommendations from HCPs and GPs
than what could’ve been expected, but interestingly patients were influenced by advice from peers. The
fact that many choices are a function of group decisions has given rise to an effort to understand the
dynamic nature of group preference formation. Different methodologies for exploring shared or joint
decision making has been propose and are discussed.
Also, a discussion on limitations was given, focusing not only on limitations in the empirical data
collection, but also on a more general level, looking into the criticism of SP-surveys being hypothetical
and not reflecting real live decision making. Testing whether SP results does suffer from hypothetical
bias, by comparing to revealed preference data is rarely done in health economics.
As was stated in the first section, incorporating patients’ preferences into clinical guidelines has been
giving raising attention, but suffers from preferences differing between people, and by preferences
being a moving target. Another way of using knowledge on preferences is thus to elicit theses just prior
to consultations as an onset of discussions between patients and HCPs, thus incorporating individual
measures into decision making in a decision tool.
Finally the section provided discussions on novel methodological approaches to SP-studies. The arrival of
random regret minimisation theory, has led to a hype of activity across different topic areas. In practice,
the benefits of the model are often not clear to analysts, nor are its limitations. But it is acknowledges
that the commonly used random utility maximisation model does not necessarily mimic real decision
making. A mix of decision rules based upon the nature of study and attributes is thus potentially more
accurate.
Looking into heterogeneity in decision rules, in scale and in taste has led to the development of more
and more complex models, requiring more insights and more decisions on model specifications by
researchers. The movement has dramatically changed to flexibility and accuracy of models and the
possibility of providing explanatory factors for observed choices, but has maybe also led to moving
preferences even further away from everyday policy-making and –formation. This is a dilemma choice
modellers need to think about in the future.
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Finally, the last section provided a conclusion on the thesis. The two-fold objective of exploring patients’
preferences with back pain and achieving methodological contributions by focusing on qualitative
methods and by focusing on how perceptions influence choices, were met in different ways. A stated
preference study was performed investigating patients’ preferences and prior to designing the study a
range of qualitative methods were used to ensure validity of the design. Lastly preferences of patients
were explained by the inclusion of a latent variable in the modelling of choices.
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10 Dansk Sammenfatning
Denne afhandling er inddelt i 10 afsnit, der tilsammen udgør et teoretisk, metodisk og empirisk bidrag til
præferenceafdækning indenfor sundhedsøkonomi med fokus på behandling af lænderygsmerter.
Det første afsnit præsenterede afhandlingens baggrund. Afsnittet introducerede litteratur om
lænderygsmerter og gav indblik i udfordringerne omkring behandlingsvalg, grundet manglende eller
modsatrettet evidens og tilstedeværelsen af andre faktorer, der influerer beslutningstagning og effekter
af behandling. Litteraturen viser at lænderygsmerter har en høj prævalens og har store konsekvenser for
de lidende og for samfundet. Mennesker, der lider af lænderygsmerter tilhører ofte lavere sociale
grupper og har et øget forbrug af sundhedsydelser. Patogenesen for lænderygsmerter er multifaktoriel
og inkluderer både arvelige og livsstils elementer og behandling for lænderygsmerter inkluderer både
ikke-kirurgisk behandling og kirurgi. Sidstnævnte er genstand for stor debat, inklusiv i Danmark, hvor de
seneste kliniske retningslinjer, har fokus på at nedbringe andelen af operationer og udvælge de rette
kandidater til operation.
Forskningsspørgsmålene som forsøgtes besvaret i afhandlingen, var af bade empirisk og metodisk
karakter og blev præsenteret som en todelt indsats, der dels undersøgte patienters præferencer for
behandling af lænderygsmerter og dels gav et metodisk bidrag ved at fokusere på den oversete
kvalitative indsats, der er nødvendig for at designe et præference-studie og ved at undersøge processen
hvormed holdninger og opfattelser influerer patienters behandlingsvalg.
Det første afsnit introducerede også et indblik i afhandlingens indplacering i eksisterende og historiske
trends i udviklingen af præferenceafdækning med fokus på sundhedsrelaterede emner og viste de
empiriske bidrags sammenhæng, såvel som afhandlingens samlede bidrag til feltet.
Det andet afsnit introducerede den anvendte metode og den eksisterende viden om patienternes
præferencer vedrørende lænderygsmerter og hvordan disse påvirkes af opfattelser af sygdommen og
holdninger og erfaringer.
Observation af valg, afslører, hvad folk synes er vigtigt. Ved at vælge noget fremfor noget andet, kan det
antages, at den foretrukne løsning giver større nytte. Det antages, at individer vælger baseret på
rationelle tankeprocesser og ved at afveje fordele og ulemper ved hver mulighed. Det er også antaget,
at nogle af de faktorer, der fører til et valg er observerbare og nogle er skjult for en observatør. At
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anvende præferencestudier, gør det muligt for en forsker at inkludere observerbare, men centrale
elementer i et hypotetisk, men plausibelt valg-scenarie. Der findes allerede en række studier vedrørende
præferencer og lænderygsmerter og litteraturen peger på en bred accept af at patienter præferencer
har indflydelse på udfaldet af behandling. Der eksisterer dog kun et studie, der benytter traditionelle
metoder til at afsløre præferencer. Litteraturen viser også, at patienter er influeret af tidligere erfaring
og deres informationsniveau i formationen af præferencer og at patienter foretrækker erfarne
behandlere, der tager patienten alvorligt og udviser stor troværdighed.
Behandling af lænderygsmerter kræver individualiserede behandlingsmuligheder og - beslutninger, der
bygger på patientens individuelle præferencer.
Præferencer baseret på hypotetiske valg er en veletableret måde at opnå brugbar viden om folks
motivationer og ønsker, og metoden er meget udbredt inden for en række forskningsområder. Design
og analyse af præferenceundersøgelser kræver en række metodiske valg, der skal foretages af
forskeren. Valg af design og model til analyse påvirker resultater og da disse valg er afgørende, bør de
være baseret på indsigt i litteraturen samt grundigt kvalitativ arbejde. Valg af de karakteristika, der skal
indgå i præference-afdækningen er helt central, men der skal også fokuseres på hvordan
beslutningsscenarierne beskrives og vises. Ydermere skal et eksperimentelt design, der kombinerer
karakteristika i valgsæt, vælges. Tidligere var disse design baseret på ortogonalitet, men har siden
udviklet sig i retning af at forsøge at maksimere oplysninger om præferencer pr. foretaget valg. Det skal
endvidere besluttes om præference-afdækningseksperimentet skal indeholde en mulighed for at vælge
ikke at vælge, og dette skal forsøge at afspejle det rigtige marked så vidt muligt.
Der findes en række modeller til analyse af præferencestudier. Disse har udviklet i sig retning mod at
kunne håndtere præferenceheterogenitet i en befolkning. Primært ved at tillade fordelinger af
præferencer, men også ved at integrere latente variable med valg i hybrid-strukturer.
Det tredje afsnit præsenterede baggrunden for de empiriske bidrag. Her blev dataindsamling detaljeret
beskrevet. Indsamlingen inkluderede både observationsstudier og interviews, så vel som kvantitative
pilottests forud for det endelig design.
Det fjerde afsnit præsenterede det første empiriske og metodiske bidrag i et tidsskriftsartikelformat.
Afsnittet giver et vigtigt indblik i det væsentlige kvalitative arbejdet forud for at designe et præference-
eksperiment, som omfatter udvikling, test og optimering af undersøgelsen. Denne proces er vigtig for
eksperimentets validitet og gyldigheden af resultaterne, men er ofte ikke rapporteret. Artiklen viser
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hvordan kvalitativt arbejde i væsentlig grad kan påvirke og vejlede et design, og gør det klart, at en
mindre grundig kvalitativ proces ville have resulteret i et mindre brugbart design. En række metoder
blev brugt til at få viden om feltet. Disse omfattede en litteraturgennemgang, feltarbejde i kliniske
afdelinger på danske sygehuse og blev suppleret med kvalitative interviews med patienter og læger.
Hvert trin i den kvalitative processen vejledte det endelige design, og det er nyttigt for forskere at
udføre lignende kvalitative tiltag i forbindelse med at designe præference-studier.
Den femte sektion præsenterede den empiriske undersøgelse af patienters præferencer til behandling
af rygsmerter baseret på designet udviklet i den kvalitative proces. Artiklen beskriver at
beslutningsprocessen vedrørende behandling af lænderygsmerter, influereres af den modstridende
evidens og at inddragelse af patienters præferencer er centralt i både klinisk og politisk optik. Artiklen
bygger på data indsamlet på Middelfart Rygcenter og data blev analyseret ved brug af en mixed-logit
model, suppleret af en subgruppeanalyse, der inddelte patienter i socioøkonomiske grupper. Som
forventet viste resultaterne at patienterne tildelte positiv nytte til positive behandlingsresultater og
negativ nytte til længere ventetid, større risici og kirurgi. Patienterne var i gennemsnit villige til at vente
to år på effekt af behandling for at undgå kirurgi, mens de var villige til at vente næsten tre år for effekt
af behandlingen, hvis behandlingen gjorde dem fri for smerter. Modellen fangede betydelig
heterogenitet i forhold til smertereduktion og evnen til at udføre dagligdagsaktiviteter samt
behandlingstype. Resultaterne viste endvidere at smertelindring var den mest centrale faktor i valg af
behandling. Dette er værdigfuld information for behandlere i deres kommunikation med patienter.
Det sjette afsnit præsenterede et studie omhandlende hvordan patienters præferencer for behandling
er influeret af en række faktorer. Afsnittet fungerer samtidigt som det andet metodiske bidrag ved at
introducere hybridmodeller.
Artiklen argumenter at især når klinisk evidens mangler eller er uklar, er der et rum for, at præferencer
kan have en stor rolle i beslutningstagning. Artiklen undersøger fordelene ved at integrere holdninger og
erfaring i en samlet model, der undersøger både latente variable og hypotetiske valg samtidigt.
Resultaterne blev dernæst diskuteret i forhold til modeller, der håndterer forskelle i respondenters valg
på mere udbredt vis.
Resultaterne viser, at mens hybridmodellen giver et større indblik i hvordan holdninger og erfaring
påvirker adfærd og at modellen giver mere præcision. Dog er de overordnede resultater de samme som
i mere simple modeller. Mest interessant er det, at sociodemografiske variable og lægers anbefalinger
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ikke synes at have indflydelse på patienternes valg, mens venners og families anbefalinger har stor
indflydelse på valgene.
Det syvende afsnit var en diskussion af hvorledes de empiriske bidrag både bekræfter og tilfører nyt til
den eksisterende viden. Et vigtigt resultat er sammenhængen mellem holdninger som drivkraft for valg
af behandlingsmuligheder, der både er observeret i adskillige undersøgelser og bekræftet i
undersøgelsen præsenteresti afsnit 6. Det blev også vist ved den samme undersøgelse, at patienterne
var mindre påvirket af anbefalinger fra praktiserende læger og andre sundhedsprofessionelle end hvad
der kunne have været forventet, men patienter blev påvirket af rådgivning fra ligemænd. Det faktum, at
mange valg er en funktion af en gruppes beslutninger, har givet anledning til en indsats for at forstå
dynamikken bag gruppes præferencedannelse. Forskellige metoder til at udforske delt eller fælles
beslutningstagning bliver foreslået og diskuteres. Derudover diskuteres studiernes begrænsninger og
metodiske begrænsninger, hvor kritikken om den hypotetiske natur i præferencestudier debatteres. Det
foreslås at flere sundhedsøkonomer fokuserer på at teste om de hypotetiske valg afspejler virkelige valg.
Som anført i det første afsnit, har indarbejdelse af patienters præferencer i kliniske retningslinjer været
givet opmærksomhed, men lider under heterogenitet og præferencers ikke statiske anlæg. En anden
måde at inddrage præferencer er således at undersøge disse i forbindelse med mødet mellem behandler
og patient. Endelig diskuteres nye metodiske tiltag og strømninger indenfor præferencelitteraturen,
men fokus på minimering af ubehag, som beslutningsregel og håndtering af heterogenitet i form af
skalaforskelle, præferencer og beslutningsregler. Udviklingen af modeller, har betydet en dramatisk
forbedring i håndteringen af heterogenitet, men har også betydet en væsentlig øget kompleksitet, der
måske er med til at bringe præferencestudier endnu længere væk fra klinisk og især politiske
beslutningstagning.
Endelig giver det sidste afsnit en konklusion på afhandlingen. Det dobbelte formål med at udforske
patienternes præferencer for behandling af rygsmerter og opnå metodiske bidrag ved at fokusere på
kvalitative metoder og ved at fokusere på, hvordan opfattelser påvirker valg, blev opfyldt på forskellige
måder. Patienters præferencer blev studeret og beskrevet og forud derfor blev grundigt kvalitativt
arbejde udført. Endelig blev patienters beslutninger undersøgt i forhold til disses påvirkning af latente
variable og i forhold til mere almindelige modeller.
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