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A Bayesian approach to stochastic cost‐effectiveness analysis
Author(s) -
Briggs Andrew H.
Publication year - 1999
Publication title -
health economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.55
H-Index - 109
eISSN - 1099-1050
pISSN - 1057-9230
DOI - 10.1002/(sici)1099-1050(199905)8:3<257::aid-hec427>3.0.co;2-e
Subject(s) - frequentist inference , bayesian probability , pooling , frequentist probability , bayes' theorem , bayesian statistics , bayes factor , computer science , bayesian econometrics , econometrics , prior probability , machine learning , bayesian inference , artificial intelligence , mathematics
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. Copyright © 1999 John Wiley & Sons, Ltd.