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Design techniques for stated preference methods in health economics
Fredrik Carlsson1, Peter Martinsson
1Department of Economics, Göteborg University, Gothenburg, Sweden. frederik.carlsson@economics.gu.se
Health Economics
|March 26, 2003
Summary
D-optimal designs improve precision in health economic choice experiments. While orthogonal designs yield higher mean square error, D-optimal designs with accurate priors offer superior estimation for willingness to pay.
Area of Science:
- Health Economics
- Experimental Design
- Econometrics
Background:
- Stated preference surveys are crucial for health economic evaluations.
- Choice experiments are widely used to elicit preferences for health interventions.
- Effective survey design is essential for precise estimation of economic values.
Purpose of the Study:
- To compare different design techniques for choice experiments in health economics.
- To evaluate the impact of design choices on the precision of parameter estimates and welfare measures.
- To investigate the role of prior information and sequential design in improving estimation accuracy.
Main Methods:
- Comparison of orthogonal, cyclical, and D-optimal designs.
- Inclusion of prior parameter information in D-optimal design construction.
- Monte Carlo simulations to evaluate design performance.
- Assessment of marginal willingness to pay prediction accuracy.
Main Results:
- All designs produced unbiased estimations.
- D-optimal designs resulted in lower mean square error compared to orthogonal designs, especially with correct priors.
- Welfare measures showed limited sensitivity to biased priors in D-optimal designs.
Conclusions:
- D-optimal designs are recommended for health economic choice experiments due to superior precision.
- Incorporating accurate prior information enhances the efficiency of D-optimal designs.
- Sequential design procedures can further optimize estimation precision.
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