Constructing Experimental Designs for Discrete-Choice Experiments: Report of the ISPOR Conjoint Analysis Experimental Design Good Research Practices Task Force
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Stated-preference methods are a class of evaluation techniques for studying the preferences of patients and other stakeholders. While these methods span a variety of techniques, conjoint-analysis methods-and particularly discrete-choice experiments (DCEs)-have become the most frequently applied approach in health care in recent years. Experimental design is an important stage in the development of such methods, but establishing a consensus on standards is hampered by lack of understanding of available techniques and software. This report builds on the previous ISPOR Conjoint Analysis Task Force Report: Conjoint Analysis Applications in Health-A Checklist: A Report of the ISPOR Good Research Practices for Conjoint Analysis Task Force. This report aims to assist researchers specifically in evaluating alternative approaches to experimental design, a difficult and important element of successful DCEs. While this report does not endorse any specific approach, it does provide a guide for choosing an approach that is appropriate for a particular study. In particular, it provides an overview of the role of experimental designs for the successful implementation of the DCE approach in health care studies, and it provides researchers with an introduction to constructing experimental designs on the basis of study objectives and the statistical model researchers have selected for the study. The report outlines the theoretical requirements for designs that identify choice-model preference parameters and summarizes and compares a number of available approaches for constructing experimental designs. The task-force leadership group met via bimonthly teleconferences and in person at ISPOR meetings in the United States and Europe. An international group of experimental-design experts was consulted during this process to discuss existing approaches for experimental design and to review the task force's draft reports. In addition, ISPOR members contributed to developing a consensus report by submitting written comments during the review process and oral comments during two forum presentations at the ISPOR 16th and 17th Annual International Meetings held in Baltimore (2011) and Washington, DC (2012).
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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 [12]
The study used a D-efficient design to reduce the number of choice cards while maintaining the precision of parameter estimation.
“To improve experimental efficiency and avoid redundancy, this study adopted a D-efficient design, which reduces the number of choice cards while maintaining the precision of parameter estimation, thereby enabling participants to provide more efficient data contributions within a limited number of tasks (59, 60).”
From Designing age-friendly interfaces and wearable–environment integrations to enhance medication adherence: a discrete choice experiment among older adults (2026) · cites this paper as [59]
An efficient fractional factorial design was used to generate 7 choice scenarios, maximizing D-efficiency with zero priors on the coefficients.
“we used an efficient fractional factorial design to generate 7 choice scenarios, maximizing D-efficiency with zero priors on the coefficients. [12, 15]”
From Assessing the American public's preferences for reforms to teen driving licensure systems: a discrete choice experiment (2026) · cites this paper as [12]
The most widely used type of stated preference method is the discrete choice experiment (DCE).
“The most widely used type of stated preference method is the discrete choice experiment (DCE) [7].”
From Understanding Uptake of Digital Health Products: Methodology Tutorial for a Discrete Choice Experiment Using the Bayesian Efficient Design (2021) · cites this paper as [7]
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- Authors
- Reed Johnson, F., Lancsar, E., Marshall, D., Kilambi, V., Mühlbacher, A., Regier, D. A., Bresnahan, B. W., Kanninen, B., & Bridges, J. F. P.
- Affiliations
- Monash University (AU)
- University of Calgary (CA)
- RTI Health Solutions (US)
- Duke University (US)
- BC Cancer Agency (CA)
- University of Washington (US)
- Johns Hopkins University (US)
- ORCID
- Venue
- Value in Health
- Publisher
- Elsevier BV
- Published
- 2013-01
- Volume
- 16
- Issue
- 1
- Pages
- 3-13
- ISSN
- 1098-3015 (ISSN-L)
- OpenAlex
- W2159936838
- Cited by
- 1,799
- References
- 51
- Citations by year
- 2026: 158 · 2025: 181 · 2024: 213 · 2023: 162 · 2022: 190 · 2021: 186 · 2020: 150 · 2019: 117 · 2018: 109 · 2017: 109 · 2016: 99 · 2015: 56 · 2014: 50 · 2013: 15 · 2012: 1
- FWCI
- 171.60
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Value in Health (publishedVersion)
- PubMed (publishedVersion)
- 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 › Management Science and Operations Research › Optimal Experimental Design Methods
- Keywords
- discrete choice experiment, experimental design, stated preference methods, conjoint analysis, health care preferences, choice models, good scientific practice
- MeSH terms
- Choice Behavior, Delivery of Health Care, Humans, International Cooperation, Research Design, Models, Statistical, Patient Preference
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