Chronic pain patients’ treatment preferences: a discrete-choice experiment

Mühlbacher, Axel; Junker, Uwe; Juhnke, Christin; Stemmler, Edgar; Kohlmann, Thomas; Leverkus, Friedhelm; Nübling, Matthias

doi:10.1007/s10198-014-0614-4

Cited by 30 publications

(31 citation statements)

References 73 publications

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“…Prior research has explored patient preferences for NSAIDs and opioids as treatments for chronic OA pain and CLBP 7e10 . These studies suggest that, in addition to efficacy, risks associated with NSAIDs 7,8 and opioids, including cardiovascular and gastrointestinal risks and risk of dependency 9 , are key drivers of preferences 7e9 . However, no prior studies have evaluated preferences for features that differentiate NSAIDs, opioids, and NGF inhibitors.…”

Section: Introductionmentioning

confidence: 99%

Patient preferences for osteoarthritis pain and chronic low back pain treatments in the United States: a discrete-choice experiment

Turk

Boeri²,

Abraham

et al. 2020

Osteoarthritis and Cartilage

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Objective: To quantify preferences for attributes of potential analgesic treatments for moderate-to-severe pain associated with osteoarthritis (OA) and/or chronic low back pain (CLBP) as relevant to injectable nerve growth factor (NGF)einhibitors, nonsteroidal anti-inflammatory drugs (NSAIDs), and opioids. Methods: We used a discrete-choice experiment (DCE) to elicit preferences for attributes of OA and CLBP pharmaceutical treatments, and a best-worst scaling (BWS) exercise to further characterize the relative importance of treatment-related side-effect risks. The survey was completed online by 602 US residents with self-reported chronic, moderate-to-severe OA pain and/or CLBP who had tried, had contraindications for, or were unwilling to take currently available pharmaceutical therapies. In the DCE, respondents repeatedly chose between two hypothetical treatments defined by six attributes (symptom control; treatment-related risks of (1) severe joint problems, (2) heart attack, and (3) physical dependence; mode/frequency of administration; and cost). In the BWS exercise, respondents evaluated ten side-effect risks. Random-parameters logit models were estimated; conditional relative attribute importance, maximum acceptable risks, and willingness to pay were calculated. Results: The most important DCE attributes were improving symptom control (scaled conditional relative importance, 10.00) and reducing risk of physical dependence (6.99). The three most important BWS attributes were, in rank order, risks of stroke, physical dependence, and heart attack. Respondents were willing to accept a > 4% treatment-related risk of severe joint problems for even modest symptom improvement. Conclusion: A pharmaceutical treatment with a risk of severe joint problems was viewed as an acceptable alternative to other treatments with comparable efficacy but risks associated with NSAIDs or opioids.

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Section: Introductionmentioning

confidence: 99%

Patient preferences for osteoarthritis pain and chronic low back pain treatments in the United States: a discrete-choice experiment

Turk

Boeri²,

Abraham

et al. 2020

Osteoarthritis and Cartilage

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“…This methodology provides researchers the opportunity to collect comparable data, to understand individual trade-offs and identify benefits from services characteristics and at the same time provide a consumers' utility function [20,21]. Some studies adopt this method to study innovative health technologies and non-market goods [19,20] but also to elicit chronic pain patients' treatment preferences [22], hospital preferences and insured preferences for health system attributes [23]. By using a DCE, services and policies are described by their attributes.…”

Section: Introductionmentioning

confidence: 99%

Potential users’ preferences towards cardiac telemedicine: A discrete choice experiment investigation in Sardinia

Deidda

Meleddu

Pulina

2018

Health Policy and Technology

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“…The 18 applied DCEs addressing understanding in the pretesting phase all used the concept of understanding as a way to modify or revise the DCE instrument before final roll out [44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61]. Most used pretesting to test or confirm understanding in relation to the terminology of attributes and medical concepts.…”

Section: Applied Dces Addressing Understanding In the Pretesting Phasementioning

confidence: 99%

“…Muhlbacher, 2015 [56] "Comprehensibility and practicality" To revise the DCE instrument "Two free text questions on content validity and comprehensibility (practicality) were included in the questionnaire"…”

Section: Not Testedmentioning

confidence: 99%

Respondent Understanding in Discrete Choice Experiments: A Scoping Review

Pearce

Harrison

Watson

et al. 2020

Patient

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Introduction Despite the recognised importance of participant understanding for valid and reliable discrete choice experiment (DCE) results, there has been limited assessment of whether, and how, people understand DCEs, and how 'understanding' is conceptualised in DCEs applied to a health context. Objectives Our aim was to identify how participant understanding is conceptualised in the DCE literature in a health context. Our research questions addressed how participant understanding is defined, measured, and used. Methods Searches were conducted (June 2019) in the MEDLINE, EMBASE, PsychINFO and Econlit databases, as well as hand searching. Search terms were based on previous DCE systematic reviews, with additional understanding keywords used in a proximity-based search strategy. Eligible studies were peer-reviewed journal articles in the field of health, related to DCE or best-worst scaling type 3 (BWS3) studies, and reporting some consideration or assessment of participant understanding. A descriptive analytical approach was used to chart relevant data from each study, including publication year, country, clinical area, subject group, sample size, study design, numbers of attributes, levels and choice sets, definition of understanding, how understanding was tested, results of the understanding tests, and how the information about understanding was used. Each study was categorised based on how understanding was conceptualised and used within the study. Results Of 306 potentially eligible articles identified, 31 were excluded based on titles and abstracts, and 200 were excluded on full-text review, resulting in 75 included studies. Three categories of study were identified: applied DCEs (n = 52), pretesting studies (n = 7) and studies of understanding (n = 16). Typically, understanding was defined in relation to either the choice context, such as attribute terminology, or the concept of choosing. Very few studies considered respondents' engagement as a component of understanding. Understanding was measured primarily through qualitative pretesting, rationality or validity tests included in the survey, and participant self-report, however reporting and use of the results of these methods was inconsistent. Conclusions Those conducting or using health DCEs should carefully select, justify, and report the measurement and potential impact of participant understanding in their specific choice context. There remains scope for research into the different components of participant understanding, particularly related to engagement, the impact of participant understanding on DCE validity and reliability, the best measures of understanding, and methods to maximise participant understanding.

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Chronic pain patients’ treatment preferences: a discrete-choice experiment

Cited by 30 publications

References 73 publications

Patient preferences for osteoarthritis pain and chronic low back pain treatments in the United States: a discrete-choice experiment

Patient preferences for osteoarthritis pain and chronic low back pain treatments in the United States: a discrete-choice experiment

Potential users’ preferences towards cardiac telemedicine: A discrete choice experiment investigation in Sardinia

Respondent Understanding in Discrete Choice Experiments: A Scoping Review

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