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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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“Choice-based techniques like those used in Cases 1, 2, and 4 are more information-rich compared to ranking and pointallocation, but program evaluators and researchers may be discouraged by their complex experimental designs [122].”
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“Measurement instruments Discrete choice experiment (DCE) The checklist developed by the ISPOR Good Research Practices for Conjoint Analysis Task Force was used to report the steps involved in conducting this DCE.16,17 Identification and selection of relevant attributes and levels The identification and selection proce…”
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“20 .”
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“[22] This design ensures the best orthogonal combination of attribute levels to minimize correlation between attributes and levels.”
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Mohamed, A. F., González, J. M., & Fairchild, A. O. (2015) · Journal of Thyroid Research · doi:10.1155/2015/438235 · Cited by 19
Sauthoff, S., Mußhoff, O., Danne, M., & Anastassiadis, F. (2016) · Biomass and Bioenergy · doi:10.1016/j.biombioe.2016.04.005 · Cited by 20
Determann, D., Lambooij, M., Gyrd‐Hansen, D., de Bekker-Grob, E. W., Steyerberg, E. W., Heldoorn, M., Pedersen, L. B., & de Wit, G. A. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw158 · Cited by 19
What is clearance worth? Patients’ stated risk tolerance for psoriasis treatments
Fairchild, A. O., Reed, S. D., Johnson, F. R., Anglin, G., Wolka, A. M., & Noel, R. A. (2017) · Journal of Dermatological Treatment · doi:10.1080/09546634.2017.1329499 · Cited by 20
“We followed established practice of dividing the full experimental design into several versions to reduce respondent burden [17].”
MethodCognitive reflection and the valuation of energy efficiency
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Seo, J., Heidenreich, S., Aldalooj, E., Poon, J. L., Spaepen, E., Eby, E. L., & Newson, R. S. (2023) · The Patient - Patient-Centered Outcomes Research · doi:10.1007/s40271-022-00610-x · Cited by 19
Xiong, H., Hu, W., Xu, M., & Zhan, J. (2024) · Food Policy · doi:10.1016/j.foodpol.2024.102690 · Cited by 19
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Self-citationPreferences of Iranian average risk population for colorectal cancer screening tests
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Makabayi‐Mugabe, R., Musaazi, J., Zawedde‐Muyanja, S., Kizito, E., Namwanje, H., Aleu, P., Charlet, D., Lopez, D. B. F., Brightman, H., Turyahabwe, S., et al. (2022) · BMC Health Services Research · doi:10.1186/s12913-021-07365-5 · Cited by 19
“To collect quantitative data, we conducted a discrete choice experiment (DCE) with three differ‑ ent attributes of community‑based care (DOT provider, location of care, and type of support) combined into eight choice sets, each with two options and an opt‑out.”
MethodInfluentialBarthold, D., Brah, A. T., Graham, S. M., Simoni, J. M., & Hauber, B. (2022) · Patient · doi:10.1007/s40271-022-00581-z · Cited by 19
Parental preference for influenza vaccine for children in China: a discrete choice experiment
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Zhang, M., He, X., Wu, J. H., Wang, X., Jiang, Q., & Xie, F. (2022) · Value in Health · doi:10.1016/j.jval.2022.05.003 · Cited by 19
Consumers’ Preference for the Consumption of the Fresh Black Slavonian Pig’s Meat
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“21,24 Details on the design are presented in supplemental Figure C1 (Appendix C).”
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“Therefore, we apply a D-efficient Bayesian design (Johnson et al. 2013 ) with prior parameters from the pilot study using the software ‘Ngene 1.1.2’.”
InfluentialCaregiver Preferences for the Treatment of Males with Fragile X Syndrome
Cross, J. D., Yang, J., Johnson, F. R., Quiroz, J. A., Dunn, J., Raspa, M., & Bailey, D. B. (2015) · Journal of Developmental & Behavioral Pediatrics · doi:10.1097/dbp.0000000000000234 · Cited by 19
Severity-Stratified Discrete Choice Experiment Designs for Health State Evaluations
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Designing Discrete Choice Experiments Using a Patient-Oriented Approach
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Self-citationiSAY (incentives for South African youth): Stated preferences of young people living with HIV
Galárraga, O., Kuo, C., Mtukushe, B., Maughan‐Brown, B., Harrison, A. D., & Hoare, J. (2020) · Social Science & Medicine · doi:10.1016/j.socscimed.2020.113333 · Cited by 19
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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