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Measures of importance for economic analysis based on decision modeling
Douglas Coyle1, Martin J Buxton, Bernie J O'Brien
1Clinical Epidemiology Program, Ottawa Health Research Institute, 1053 Carling Avenue, Ottawa, ON K1Y 4E9, Canada. dcoyle@ohri.ca
Journal of Clinical Epidemiology
|October 22, 2003
Summary
This study compares various importance measures for probabilistic economic analysis. It identifies key input parameters influencing decisions, using a Parkinson
Area of Science:
- Decision Analysis
- Economic Modeling
- Uncertainty Quantification
Background:
- Probabilistic economic analysis quantifies input parameter uncertainty to assess decision uncertainty.
- Importance analysis identifies key parameters driving uncertainty in complex simulations across disciplines.
Purpose of the Study:
- To compare various importance measures within the context of economic analysis.
- To evaluate different techniques for their effectiveness in identifying influential parameters.
Main Methods:
- Classified importance techniques into categories: variance/correlation, information, probability, entropy, and elasticity-based measures.
- Applied selected common measures to an economic model for Parkinson's disease treatment.
Main Results:
- Evaluated techniques based on variable ranking, complexity, and interpretability.
- Demonstrated practical application of importance measures in a specific economic model.
Conclusions:
- Different importance measures offer varying insights into parameter influence.
- The choice of measure impacts the understanding of uncertainty in economic models.
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