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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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“4%) of the studies used blocking, where each respondent is randomised to only answer a subset of the choice tasks [54].”
BackgroundParticipatory Algorithmic Management
Lee, M. K., Nigam, I., Zhang, A., Afriyie, J., Qin, Z., & Gao, S. (2021) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3461702.3462628 · Cited by 41
“The features with low to high importance were then used to generate pairwise comparisons of alternatives based upon fractional factorial design principles [24].”
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“A full-profile DCE with fractional factorial design using triplets with a balanced overlap strategy and orthogonal design is used [13,33,40,41].”
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Self-citationFactors Associated with Patient Preferences for Disease-Modifying Therapies in Multiple Sclerosis
Hincapie, A. L., Penm, J. H.-D., & Burns, C. F. (2017) · Journal of Managed Care & Specialty Pharmacy · doi:10.18553/jmcp.2017.23.8.822 · Cited by 38
TO “VAPE” OR SMOKE? EXPERIMENTAL EVIDENCE ON ADULT SMOKERS
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“Thus, we are concerned that presenting exact numbers that many respondents likely knew are not based on evidence would lead to lower quality responses (Johnson et al. 2013).”
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Preference Stability and Choice Consistency in Discrete Choice Experiments
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“main effects of each attribute with no interaction effects) [70], is to balance the number of attributes modeled with the cognitive burden on respondents [71].”
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Enhancing Agent-Based Models with Discrete Choice Experiments
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“A comparison of this so ware with other DCE design approaches can be found in Johnson et al. (2013). The evaluation of the DCE leads to a partworth utility value for each attribute level and one for the “don’t sell”-option, the none-option.”
ResultColorectal Cancer Screening: Preferences, Past Behavior, and Future Intentions
Mansfield, C. A., Ekwueme, D. U., Tangka, F. K. L., Brown, D. S., Smith, J. L., Guy, G. P., Li, C., & Hauber, B. (2018) · Patient · doi:10.1007/s40271-018-0308-6 · Cited by 36
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Jiang, S., Ren, R., Gu, Y., Jeet, V., Liu, P., & Li, S. (2022) · PharmacoEconomics · doi:10.1007/s40273-022-01198-8 · Cited by 39
“The third explanation is the presence of potential biases that occurred in implementing the seven DCEs, such as the selection bias [16, 84].”
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“A partial profile design presented choices composed of a subset of the study’s attributes.(25) Sawtooth Software’s experimental design algorithm(25) composed choice tasks presenting three options described by the levels of three attributes (Figure 1).”
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“The study design was informed by the International Society for Pharmacoeconomics and Outcomes Research checklists (22,23).”
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“A foldover design was used to eliminate easy or straightforward choices from each scenario [33].”
MethodTorquati, B., Tempesta, T., Vecchiato, D., & Venanzi, S. (2018) · Sustainability · doi:10.3390/su10082795 · Cited by 34
“We used blocking given that it “promotes response efficiency by reducing the necessary cognitive effort for each respondent who completes the survey” ([88], p.”
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“In particular, different generators (set of numbers that are applied to the starting design to shift the levels on the attributes based on orthogonal arrays) have to be used depending on the type of choice experiment to be designed [32].”
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Zimba, R., Kulkarni, S., Berry, A., You, W., Mirzayi, C. A., Westmoreland, D. A., Parcesepe, A. M., Waldron, L., Rane, M. S., Kochhar, S., et al. (2020) · JMIR Public Health and Surveillance · doi:10.2196/25546 · Cited by 34
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“Discrete choice experiment The experiments were conducted according to research practices as described by Johnson et al.(19).”
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“All ages, y 125/229 (55) 33/144 (23) 158/373 (42) 29 95/158 (60) 27/95 (28) 122/253 (48) 30 30/71 (42) 6/49 (12) 36/120 (30) Prior dislocation, no surgery 34/72 (47) 8/42 (19) 42/114 (37)”
Lewis, M. A., Stine, A., Paquin, R. S., Mansfield, C. A., Wood, D., Rini, C. K., Roche, M. I., Powell, C. M., Berg, J. S., & Bailey, D. B. (2017) · Genetics in Medicine · doi:10.1038/gim.2017.93 · Cited by 32
“We also tested for differences in attribute-level relative preferences between White and Black respondents; adding these interaction terms to the main-effects model significantly improved model fit, Δχ2(15)=38.”
InfluentialOzawa, S., Grewal, S. K., & Bridges, J. F. P. (2016) · Applied Health Economics and Health Policy · doi:10.1007/s40258-016-0222-9 · Cited by 31
Self-citationMartinez, E., Solé, J. M. N., Rodríguez, M. V., Martín-Mola, E., Raya, E., Ibero, I., Nocea, G., Aragón, B., Lizán, L., & Prades, M. (2016) · Patient Preference and Adherence · doi:10.2147/ppa.s106311 · Cited by 31
“This orthogonal design, combining the levels of the attributes, resulted in eight scenarios, which described different alternatives of treatment with BAs for RA, AS, or PsA.”
Katz, E. G., Hauber, B., Gopal, S., Fairchild, A., Pugh, A., Weinstein, R. B., & Levitan, B. S. (2016) · Patient Preference and Adherence · doi:10.2147/ppa.s114172 · Cited by 31
Adolescent values for immunisation programs in Australia: A discrete choice experiment
Wang, B., Chen, G., Ratcliffe, J., Afzali, H. H. A., Giles, L. C., & Marshall, H. S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0181073 · Cited by 31
Chudner, I., Goldfracht, M., Goldblatt, H., Drach‐Zahavy, A., & Karkabi, K. (2018) · Patient · doi:10.1007/s40271-018-0318-4 · Cited by 31
Does information change German consumers’ attitudes about genetically modified food?
Wuepper, D., Wree, P., & Ardali, G. (2018) · European Review of Agricultural Economics · doi:10.1093/erae/jby018 · Cited by 34
de Freitas, H. M., Ito, T., Hadi, M., Al-Jassar, G., Henry-Szatkowski, M., Nafees, B., & Lloyd, A. J. (2019) · Advances in Therapy · doi:10.1007/s12325-018-0861-3 · Cited by 31
Maize farmer preferences for intercropping systems to reduce Striga in Malawi
Silberg, T. R., Richardson, R. B., & López, M. C. (2020) · Food Security · doi:10.1007/s12571-020-01013-2 · Cited by 32
“The opt-out option allows participants to select neither of the two alternatives, inferring they reject both alternatives (Johnson et al. 2013).”
BackgroundKrauth, C., Oedingen, C., Bartling, T., Dreier, M., Spura, A., Bock, F. D., von Rüden, U., Betsch, C., Korn, L., & Robra, B. (2021) · International Journal of Public Health · doi:10.3389/ijph.2021.591027 · Cited by 32
Dommaraju, S., Hagey, J. M., Odeny, T., Okaka, S., Kadima, J., Bukusi, E. A., Cohen, C. R., Kwena, Z., Eshun‐Wilson, I., & Geng, E. H. (2021) · PLoS ONE · doi:10.1371/journal.pone.0255650 · Cited by 31
“where n is the number of patients, t is the number of choice tasks per questionnaire (t = 10 in our study), a is the number of options per choice task (a = 3), and c is the number of cells (c = 4) [22, 23].”
BackgroundKezhong, A., Lu, X., Wang, J., Hu, L., Li, B., & Lu, Y. (2021) · Vaccines · doi:10.3390/vaccines9101088 · Cited by 33
Seghieri, C., Mengoni, A., & Nuti, S. (2013) · The European Journal of Health Economics · doi:10.1007/s10198-013-0542-8 · Cited by 31
Involving Patients in Weighting Benefits and Harms of Treatment in Parkinson's Disease
Weernink, M. G. M., van Til, J. A., van Vugt, J. P. P., Movig, K. L. L., Groothuis‐Oudshoorn, C. G. M., & IJzerman, M. J. (2016) · PLoS ONE · doi:10.1371/journal.pone.0160771 · Cited by 31
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Understanding patient preferences and willingness to pay for hemophilia therapies
Chaugule, S., Hay, J. W., & Young, G. A. (2015) · Patient Preference and Adherence · doi:10.2147/ppa.s92985 · Cited by 30
González, J. M., Johnson, F. R., McAteer, H., Posner, J., & Mughal, F. (2016) · British Journal of Dermatology · doi:10.1111/bjd.14798 · Cited by 30
Feldman, S. R., Moeller, A. H., & Erntoft, S. (2016) · Journal of health economics and outcomes research · doi:10.36469/9817 · Cited by 30
González, J. M., Doan, J. F., Gebben, D., Boeri, M., & Fishman, M. (2018) · PharmacoEconomics · doi:10.1007/s40273-018-0640-7 · Cited by 30
Bolt, T., Kobayashi, H., & Mahlich, J. (2018) · PharmacoEconomics - Open · doi:10.1007/s41669-018-0104-1 · Cited by 29
Sydenham, R. V., Jarbøl, D. E., Hansen, M. P., Justesen, U. S., Watson, V., & Pedersen, L. B. (2022) · Social Science & Medicine · doi:10.1016/j.socscimed.2022.115033 · Cited by 30
Dong, D., Özdemir, S., Bee, Y. M., Toh, S.-A. E. S., Bilger, M., & Finkelstein, E. A. (2016) · Value in Health · doi:10.1016/j.jval.2016.03.1837 · Cited by 30
An introduction to “discrete choice experiments” for behavior analysts
Friedel, J. E., Foreman, A. M., & Wirth, O. (2022) · Behavioural Processes · doi:10.1016/j.beproc.2022.104628 · Cited by 29
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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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