Discrete Choice Experiments in Health Economics: Past, Present and Future
Discrete Choice Experiments (DCEs) are a method used to understand what people prefer in healthcare by asking them to choose between different options. These choices help researchers figure out which features of a treatment or service matter most to patients or the public.
At a glance
Use when
You need to understand and quantify preferences for healthcare options, especially when trade-offs between features (e.g., efficacy, side effects, cost, delivery mode) are involved, and when input from patients or the public is required for decision-making.
Avoid when
The decision context is highly clinical with no patient involvement needed, when preferences are already well-established, or when resources or expertise for rigorous DCE design and analysis are lacking.
Inputs
Attributes and levels of healthcare options, experimental design (e.g., choice sets), respondent population, survey instrument, qualitative data for attribute development
Outputs
Quantified preference weights (utilities), marginal willingness to pay, relative importance scores for attributes, subgroup preference analyses
How it works
Discrete Choice Experiments (DCEs) are quantitative stated-preference methods used to estimate the relative importance of different attributes in health care decisions. Respondents choose between hypothetical scenarios defined by varying attribute levels. Data are analyzed using econometric models (e.g., conditional logit, mixed logit) to estimate utility parameters. DCEs are increasingly implemented using efficient experimental designs (e.g., D-efficient) generated by software such as Ngene, and often informed by qualitative research to select attributes and levels.
- HTA domains
- Clinical Effectiveness, Costs & Economic Evaluation, Patient and Social Aspects
- Categories
- AppraisalContext & ImplementationPROMs
- Assumptions
- Respondents make consistent, utility-maximizing choices; attributes are independent; the hypothetical choices reflect real-world behavior; the design adequately represents the decision space
- Strengths
- Enables quantification of patient and public preferences,Supports trade-off analysis between multiple attributes,Can be integrated into economic models and HTA submissions,Flexible in application across diseases, populations, and interventions,Informed by qualitative methods for content validity
- Limitations
- Hypothetical bias: responses may not reflect real behavior,Complexity in design and analysis requires expertise,Frequent lack of reporting detail limits reproducibility and quality assessment,Risk of cognitive burden on respondents with too many attributes or choice sets,May not capture all relevant factors influencing real-world decisions
- Also known as
- DCE, Discrete Choice Experiment, Stated Preference Method
Questions this answers
- › Which attributes of a healthcare intervention do patients or the public value most?
- › How do individuals trade off risks, benefits, and costs in health decisions?
- › What design features of health services influence patient uptake or satisfaction?
- › How can patient preferences be quantified for use in economic evaluations?
- › What is the willingness to pay for specific health outcomes or service improvements?
- › How do preferences vary across different population subgroups?
References & sources
Similar by meaning
- ISPOR Statistical Methods for the Analysis of Discrete-Choice Experiments
- DIRECT Checklist (Discrete Choice Experiment Reporting Checklist)
- ISPOR Conjoint Analysis Experimental Design Good Research Practices
- Distributional Cost-Effectiveness Analysis (DCEA)
- The Use of a Discrete Choice Experiment Including Both Duration and Dead for the Development of an EQ-5D-5L Value Set for Australia
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