title: A Bayesian approach to stochastic cost-effectiveness analysis.
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.
authors.affiliation: Health Economics Research Centre, University of Oxford, Headington, UK. andrew.briggs@ihs.ox.ac.uk
authors.forename: A H
authors.initials: AH
authors.lastname: Briggs
mesh_terms: D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000191:economics / Q000706:statistics & numerical data; D003362:Cost-Benefit Analysis / Q000379:methods* / Q000706:statistics & numerical data; D003661:Decision Support Techniques; D006302:Health Services Research / Q000191:economics / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D016013:Likelihood Functions