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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Citations
Cites
Prospect Theory: An Analysis of Decision under Risk
Kahneman, D., & Tversky, A. (1979) · Econometrica · doi:10.2307/1914185 · Cited by 48,524
Prospect theory: An analysis of decision under risk
Kahneman, D. J., & Tversky, A. (1988) · Cambridge University Press eBooks · doi:10.1017/cbo9780511609220.014 · Cited by 32,720
Conditional logit analysis of qualitative choice behavior
McFadden, D. L. (1972) · eScholarship (California Digital Library) · Cited by 14,203
Methods for the Economic Evaluation of Health Care Programmes
Drummond, M. E., Sculpher, M. J., Torrance, G. W., O’Brien, B. J., & Stoddart, G. L. (2005) · doi:10.1093/oso/9780198529446.001.0001 · Cited by 13,648
Mail and internet surveys : the tailored design method
Dillman, D. A. (2007) · Cited by 9,587
A New Approach to Consumer Theory
Lancaster, K. J. (1966) · Journal of Political Economy · doi:10.1086/259131 · Cited by 8,471
Discrete Choice Methods with Simulation
Train, K. E. (2001) · doi:10.1017/cbo9780511805271 · Cited by 7,784
Louviere, J. J., Hensher, D. A., Swait́, J., & Adamowicz, W. (2000) · Cambridge University Press eBooks · doi:10.1017/cbo9780511753831 · Cited by 7,008
Self-citationA New Approach to Consumer Theory
Lancaster, K. J. (1976) · Lecture notes in economics and mathematical systems · doi:10.1007/978-3-642-51565-1_34 · Cited by 6,946
Addelman, S., Box, G. E. P., Hunter, W. G., & Hunter, J. S. (1979) · Technometrics · doi:10.2307/1267766 · Cited by 6,753
Research works printed but not linked: 5
- Hensher, 1983, Transport Res, 890, 11
- Ryan, 2003, Applied Health Econ Health Policy, 2, 55
- Chakraborty, 1994, J Health Care Mark, 14, 21
- Swait, 1993, JMark Res, 30, 305
- Longo, 2006, Br J Gen Practice, 56, 35
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Drabo, E. F., Michael, J. P., & Ehsani, J. P. (2026) · Injury Prevention · doi:10.1136/ip-2023-045221 · Cited by 1
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
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