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Probabilistic sensitivity analysis in health economics.
Gianluca Baio1, A Philip Dawid2
1Department of Statistical Science, University College London, London, UK. Department of Statistics, University of Milano Bicocca, Milan, Italy. gianluca@stats.ucl.ac.uk.
Statistical Methods in Medical Research
|September 21, 2011
Summary
Health economic evaluations increasingly use Bayesian methods to account for uncertainty. This review focuses on probabilistic sensitivity analysis within Bayesian decision theory for health economics.
Area of Science:
- Health economics
- Biostatistics
- Decision theory
Background:
- Health economic evaluations are integral to clinical and medical research.
- Advanced statistical decision-theoretic foundations underpin these evaluations.
- Bayesian methods are increasingly mandated to address parameter and variable uncertainty.
Purpose of the Study:
- To review health economic assessment through the lens of Bayesian statistical decision theory.
- To examine the underlying philosophy of sensitivity analysis procedures.
- To highlight the predominant role of probabilistic sensitivity analysis.
Main Methods:
- Review of Bayesian statistical decision theory principles.
- Analysis of probabilistic sensitivity analysis methodologies.
- Exploration of philosophical underpinnings in health economic assessment.
Main Results:
- Bayesian methods provide a robust framework for handling uncertainty in health economic evaluations.
- Probabilistic sensitivity analysis is a key tool for decision-making under uncertainty.
- Understanding the philosophy behind these methods enhances their application.
Conclusions:
- Bayesian decision theory offers a rigorous approach to health economic assessment.
- Probabilistic sensitivity analysis is crucial for reliable economic evaluations.
- Further attention to the philosophical basis of methods is warranted.
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