The DOI matches its Crossref record; 107 of the 107 references it cites were found in a registry or an index.
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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“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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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Metadata
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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