Statistical methods for the analysis of discrete choice experiments: a report of the ISPOR conjoint analysis good research practices task force
Open accessAbstract
Conjoint analysis is a stated-preference survey method that can be used to elicit responses that reveal preferences, priorities, and the relative importance of individual features associated with health care interventions or services. Conjoint analysis methods, particularly discrete choice experiments (DCEs), have been increasingly used to quantify preferences of patients, caregivers, physicians, and other stakeholders. Recent consensus-based guidance on good research practices, including two recent task force reports from the International Society for Pharmacoeconomics and Outcomes Research, has aided in improving the quality of conjoint analyses and DCEs in outcomes research. Nevertheless, uncertainty regarding good research practices for the statistical analysis of data from DCEs persists. There are multiple methods for analyzing DCE data. Understanding the characteristics and appropriate use of different analysis methods is critical to conducting a well-designed DCE study. This report will assist researchers in evaluating and selecting among alternative approaches to conducting statistical analysis of DCE data. We first present a simplistic DCE example and a simple method for using the resulting data. We then present a pedagogical example of a DCE and one of the most common approaches to analyzing data from such a question format-conditional logit. We then describe some common alternative methods for analyzing these data and the strengths and weaknesses of each alternative. We present the ESTIMATE checklist, which includes a list of questions to consider when justifying the choice of analysis method, describing the analysis, and interpreting the results.
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The DCE was designed and implemented following recommended guidelines.
“We followed recommended guidelines to design and implement the DCE. [7, 8, 11–16]”
From Assessing the American public's preferences for reforms to teen driving licensure systems: a discrete choice experiment (2026) · cites this paper as [13]
The Introduction says this report focuses exclusively on statistical analysis, one step in the broader DCE checklist. Its Good Research Principles for Statistical Analysis section provides guidance for that step, but the Introduction explicitly says its examples are not meant to imply good practices for survey design or development. It therefore supports the citation as analysis guidance, not as guidance for designing and implementing the DCE as a whole. (LLM, conf=0.86)
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- Authors
- Hauber, A. B., González, J. M., Groothuis-Oudshoorn, C. G. M., Prior, T., Marshall, D. A., Cunningham, C., IJzerman, M. J., & Bridges, J. F. P.
- Affiliations
- RTI Health Solutions (US)
- Johns Hopkins University (US)
- University of Calgary (CA)
- McMaster University (CA)
- University of Twente (NL)
- ORCID
- Venue
- Value in Health
- Publisher
- Elsevier BV
- Published
- 2016-06
- Volume
- 19
- Issue
- 4
- Pages
- 300-315
- ISSN
- 1098-3015 (ISSN-L)
- OpenAlex
- W2367707061
- Cited by
- 1,330
- References
- 64
- Citations by year
- 2026: 156 · 2025: 183 · 2024: 195 · 2023: 136 · 2022: 165 · 2021: 158 · 2020: 124 · 2019: 84 · 2018: 63 · 2017: 59 · 2016: 5
- FWCI
- 136.63
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Value in Health (publishedVersion)
- PubMed (publishedVersion)
- University of Twente Research Information (publishedVersion, other-oa)
- University of Twente Research Information (submittedVersion, other-oa)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economic and Environmental Valuation
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Health Systems, Economic Evaluations, Quality of Life
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Keywords
- discrete choice experiment, stated preference, conjoint analysis, good scientific practice, conditional logit model, preference elicitation, health care preferences, ISPOR
- MeSH terms
- Analgesics, Choice Behavior, Humans, Interprofessional Relations, Regression Analysis, Models, Statistical, Outcome Assessment, Health Care, Economics, Pharmaceutical, Advisory Committees, Checklist
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