Conducting Discrete Choice Experiments to Inform Healthcare Decision Making: A User??s Guide
Open accessAbstract
Discrete choice experiments (DCEs) are regularly used in health economics to elicit preferences for healthcare products and programmes. There is growing recognition that DCEs can provide more than information on preferences and, in particular, they have the potential to contribute more directly to outcome measurement for use in economic evaluation. Almost uniquely, DCEs could potentially contribute to outcome measurement for use in both cost-benefit and cost-utility analysis. Within this expanding remit, our intention is to provide a resource for current practitioners as well as those considering undertaking a DCE, using DCE results in a policy/commercial context, or reviewing a DCE. We present the fundamental principles and theory underlying DCEs. To aid in undertaking and assessing the quality of DCEs, we discuss the process of carrying out a choice study and have developed a checklist covering conceptualizing the choice process, selecting attributes and levels, experimental design, questionnaire design, pilot testing, sampling and sample size, data collection, coding of data, econometric analysis, validity, interpretation and welfare and policy analysis. In this fast-moving area, a number of issues remain on the research frontier. We therefore outline potentially fruitful areas for future research associated both with DCEs in general, and with health applications specifically, paying attention to how the results of DCEs can be used in economic evaluation. We also discuss emerging research trends. We conclude that if appropriately designed, implemented, analysed and interpreted, DCEs offer several advantages in the health sector, the most important of which is that they provide rich data sources for economic evaluation and decision making, allowing investigation of many types of questions, some of which otherwise would be intractable analytically. Thus, they offer viable alternatives and complements to existing methods of valuation and preference elicitation.
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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 [8]
An opt-out alternative has the potential to make choices more realistic by simulating a real-world context where individuals can exercise their right not to take up an app.
“The opt-out option has the potential to make the choices more realistic [34] by simulating a real-world context where individuals can exercise their right not to take up an app, given the apps on offer [20].”
From Understanding Uptake of Digital Health Products: Methodology Tutorial for a Discrete Choice Experiment Using the Bayesian Efficient Design (2021) · cites this paper as [20]
A full-factorial design is often considered impractical due to the high number of choice tasks required.
“However, this type of design is often considered impractical due to the high number of choice tasks required [20].”
From Understanding Uptake of Digital Health Products: Methodology Tutorial for a Discrete Choice Experiment Using the Bayesian Efficient Design (2021) · cites this paper as [20]
The higher the number of attributes, alternatives, and choice tasks, the higher the task complexity.
“The higher the number of attributes, alternatives, and choice tasks, the higher the task complexity [20].”
From Understanding Uptake of Digital Health Products: Methodology Tutorial for a Discrete Choice Experiment Using the Bayesian Efficient Design (2021) · cites this paper as [20]
When a range is not wide enough, there is a risk that participants could ignore the attributes because they judge the difference between levels to be insignificant.
“When a range is not wide enough, there is a risk that participants could ignore the attributes because they judge the difference between levels to be insignificant [20].”
From Understanding Uptake of Digital Health Products: Methodology Tutorial for a Discrete Choice Experiment Using the Bayesian Efficient Design (2021) · cites this paper as [20]
When perfect orthogonality and balance cannot be achieved or are not desirable, an efficient design can be applied.
“When perfect orthogonality and balance cannot be achieved or are not desirable, an efficient design can be applied [20].”
From Understanding Uptake of Digital Health Products: Methodology Tutorial for a Discrete Choice Experiment Using the Bayesian Efficient Design (2021) · cites this paper as [20]
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Muthulingam, D., Bia, J., Madden, L. M., Farnum, S. O., Barry, D. T., & Altice, F. L. (2019) · Journal of Substance Abuse Treatment · doi:10.1016/j.jsat.2019.01.019 · Cited by 79 · see citing works
Financial Toxicity in Advanced and Metastatic Cancer: Overburdened and Underprepared
Rotter, J., Spencer, J. C., & Wheeler, S. B. (2019) · Journal of Oncology Practice · doi:10.1200/jop.18.00518 · Cited by 79 · see citing works
- Authors
- Lancsar, E., & Louviere, J.
- Affiliations
- Newcastle University (GB)
- University of Technology Sydney (AU)
- Corresponding author
- Lancsar, Emily
- Venue
- PharmacoEconomics
- Published
- 2008
- Volume
- 26
- Issue
- 8
- Pages
- 661-677
- ISSN
- 1170-7690 (ISSN-L)
- OpenAlex
- W2122707212
- Cited by
- 1,707
- References
- 107
- Citations by year
- 2026: 159 · 2025: 163 · 2024: 156 · 2023: 155 · 2022: 142 · 2021: 160 · 2020: 135 · 2019: 100 · 2018: 92 · 2017: 78 · 2016: 64 · 2015: 63 · 2014: 78 · 2013: 48 · 2012: 39
- FWCI
- 55.24
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- PharmacoEconomics (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- UTS ePRESS (University of Technology Sydney) (submittedVersion)
- Newcastle University ePrints (Newcastle Univesity)
- 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 › General Decision Sciences › Decision-Making and Behavioral Economics
- Keywords
- discrete choice experiment, healthcare decision-making, outcome measurement, economic evaluation, cost-benefit analysis, cost-utility analysis, preference elicitation, questionnaire design, validity, welfare analysis, policy analysis
- MeSH terms
- Attitude to Health, Choice Behavior, Consumer Behavior, Costs and Cost Analysis, Health Services Research, Humans, Policy Making, Models, Econometric, Patient Satisfaction
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