Cited by 1,799Economic and Environmental ValuationHealth Systems, Economic Evaluations, Quality of LifeOptimal Experimental Design Methods

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Constructing Experimental Designs for Discrete-Choice Experiments: Report of the ISPOR Conjoint Analysis Experimental Design Good Research Practices Task Force

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Reed Johnson, F., Lancsar, Emily, Marshall, Deborah, Kilambi, Vikram, Mühlbacher, Axel, Regier, Dean A., Bresnahan, Brian W., Kanninen, Barbara, Bridges, John F.P.·2013·Value in Health

DOI 10.1016/j.jval.2012.08.2223

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Abstract

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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“An experimental design is used to combine the attributes and levels to create hypothetical alternatives and the optimal placement of such alternatives into choice-sets to ensure estimation of utility functions of interest and adequate statistical power of the analysis [41].”
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“Such algorithms maximise the D-efficiency criterion [23]; here we used Fortran programming language for computations.”
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“Efficiency is a measure of the level of precision in which the parameter estimates on the attributes are measured (Johnson et al., 2013).”
Background
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“While the creation of an efficient experimental design objectively is a complicated task, a wide literature exists on how to handle it (see, e.g., Johnson et al., 2013; Rose & Bliemer, 2009).”
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Self-citation
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“The cognitive complexity of BCE and MCE, for example, is significantly affected by an increase in the number of attributes [69].”
Background
Organic food labels bias food healthiness perceptions: Estimating healthiness equivalence using a Discrete Choice Experiment
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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: Lancsar, Emily
Marshall, Deborah
Kilambi, Vikram
Mühlbacher, Axel
Regier, Dean A.
Bridges, John F.P.
Venue: Value in Health
Publisher: Elsevier BV
Published: 2013-01
Volume: 16
Issue: 1
Pages: 3-13
ISSN: 1098-3015 (ISSN-L)
DOI: 10.1016/j.jval.2012.08.2223
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: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 365 d)
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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Constructing Experimental Designs for Discrete-Choice Experiments: Report of the ISPOR Conjoint Analysis Experimental Design Good Research Practices Task Force

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