Design techniques for stated preference methods in health economics.
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Summary
The designs are evaluated according to their ability to predict the true marginal willingness to pay under different specifications of the utility function in Monte Carlo simulations and suggest that the designs produce unbiased estimations, but orthogonal designs result in larger mean square error in comparison to D-optimal designs.
- Type
- article
- Published
- 2003-04-01
- Cited by
- 446
- References
- 24
- Access
- Open access
- OpenAlex
- https://openalex.org/W2042644029
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:2412828
Keywords
Optimal design, Prior probability, Preference, Mathematical optimization, Design of experiments
References
- The Oil Pollution Act of 1990
- Do Hypothetical and Actual Marginal Willingness to Pay Differ in Choice Experiments?: Application to the Valuation of the Environment
- Conditional logit analysis of qualitative choice behavior
- The Disutility of Time Spent on the United Kingdom's National Health Service Waiting Lists
- Evaluating Health Risks: An Economic Approach
- Optimal Experimental Design for Double-Bounded Dichotomous Choice Contingent Valuation
- Establishing Patient Preferences for Blood Transfusion Support: An Application of Conjoint Analysis
- Optimal Designs for Discrete Choice Contingent Valuation Surveys: Single-Bound, Double-Bound, and Bivariate Models
- Willingness to pay for improved respiratory and cardiovascular health: a multiple-format, stated-preference approach.
- The Importance of Utility Balance in Efficient Choice Designs
- Estimating time preferences for health using discrete choice experiments.
- Analyzing Decision Making: Metric Conjoint Analysis
- The feasibility of additive conjoint measurement in measuring utilities in breast cancer patients
- Design of Sequential Experiments for Contingent Valuation Studies
- Optimal Design for Multinomial Choice Experiments
- Agency in health care. Examining patients' preferences for attributes of the doctor-patient relationship.
- Using conjoint analysis to assess women's preferences for miscarriage management.
- Efficient Experimental Design with Marketing Research Applications
- Efficient Experimental Design with Marketing Research Applications
- A General Method for Constructing Efficient Choice Designs
Cited by
- Environmental valuation, ecosystem services and aquatic species
- Justice distributive : opinions, jugements et choix individuels.
- Validity and feasibility of best worst scaling using multiple treatment outcomes of Parkinson's disease
- Why Junior Doctors Don’t Want to Become General Practitioners: A Discrete Choice Experiment from the MABEL Longitudinal Study of Doctors
- Demand for Pharmaceutical Drugs: A Choice Modelling Experiment
- Estimating the willingness to pay for a reliable electricity supply in the Turkish Republic of Northern Cyprus
- Efficient Designs for Alternative Specific Choice Experiments
- Constructing efficient choice experiments
- Evaluating the Costs and Benefits of Tidal Range Energy Generation
- Donor selection for patients undergoing allogeneic hematopoietic stem cell transplantation: Assessment of the priorities of Canadian hematopoietic stem cell transplant physicians
- Development of a time-dependent hurricane evacuation model for the New Orleans area : research project capsule.
- The implications of respondent information processing rules on preference revelation in stated choice experiments
- Selective developments in choice analysis and a reminder about the dimensionality of behavioural analysis
- Revealing Differences in Willingness to Pay due to the Dimensionality of Stated Choice Designs: An Initial Assessment
- Optimal design of stated preference experiments when using mixed logit models
- Managed Lane Travelers—Do They Pay for Travel As They Claimed They Would?
- The implications on willingness to pay of respondents ignoring specific attributes
- HOW DO RESPONDENTS HANDLE STATED CHOICE EXPERIMENTS? INFORMATION PROCESSING STRATEGIES UNDER VARYING INFORMATION LOAD
- A comparison of algorithms for generating efficient choice experiments.
- How good is good? Exploring frontiers of experimental design efficiency
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