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A Bayesian approach to stochastic cost-effectiveness analysis.

A H Briggs
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Abstract

The aim of this paper is to briefly outline a Bayesian approach to cost-effectiveness analysis (CEA). Historically, frequentists have been cautious of Bayesian methodology, which is often held as synonymous with a subjective approach to statistical analysis. In this paper, the potential overlap between Bayesian and frequentist approaches to CEA is explored--the focus being on the empirical and uninformative prior-based approaches to Bayesian methods rather than the use of subjective beliefs. This approach emphasizes the advantage of a Bayesian interpretation for decision-making while retaining the robustness of the frequentist approach. In particular the use of cost-effectiveness acceptability curves is examined. A traditional frequentist approach is equivalent to a Bayesian approach assuming no prior information, while where there is pre-existing information available from which to construct a prior distribution, an empirical Bayes approach is equivalent to a frequentist approach based on pooling the available data. Cost-effectiveness acceptability curves directly address the decision-making problem in CEA. Although it is argued that their interpretation as the probability that an intervention is cost-effective given the data requires a Bayesian interpretation, this should generate no misgivings for the frequentist.

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Bayes TheoremClinical Trials as TopicCost-Benefit AnalysisDecision Support TechniquesHealth Services ResearchHumansLikelihood FunctionsJournal ArticleResearch Support, Non-U.S. Gov't

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A H Briggs. 1999. A Bayesian approach to stochastic cost-effectiveness analysis.. https://doi.org/10.1002/(sici)1099-1050(199905)8%3A3%3C257%3A%3Aaid-hec427%3E3.0.co%3B2-e

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