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Constructing Experimental Designs for Discrete-Choice Experiments: Report of the ISPOR Conjoint Analysis Experimental Design Good Research Practices Task Force
Open accessAbstract
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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“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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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“However, based upon the observation that there is limited improvement in estimate precision when a sample size of n = 300 is exceeded, a minimum of 300 participants is generally recommended if there are no planned subgroup analyses, or at least 200 participants per subgroup when subgroup analyses are planned [12,16,17…”
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“The experimental design considers desirable design criteria: Orthogonality (attribute levels appear in choice sets with equal frequency with each level of each other attribute), level balance (levels of each attribute appear equally often), minimum overlap (minimize the overlap of levels for each attribute in each cho…”
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“Bayesian efficient designs maximize the information that can be obtained from the choice data and the accuracy of estimate choice model parameters (13, 14).”
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“We also always presented choice sets where the alternative with the most expensive control fee included at least one high-value of an ecologically desirable attribute (e.g., high native predator density) to ensure that the scenarios were realistic (Reed Johnson et al., 2013).”
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“Using Sawtooth Software’s experimental design module, we created a unique combination of attribute levels for each participant (Johnson et al. 2013).”
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“Three critical steps are followed when designing a choice experiment, namely, the establishment of attributes of interest, assigning levels and, finally, the design of the choice sets (Mangham et al., 2009; Johnson et al., 2013).”
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“Besides allowing us to determine trade-offs that patients are prepared to make, DCEs also allow us to objectively assess the relative importance patients place on the attributes of the scenarios (Reed Johnson et al. , 2013).”
Metadata
- 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
- 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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