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.

journal_name

Health Econ

journal_title

Health economics

authors

Briggs AH

doi

10.1002/(sici)1099-1050(199905)8:3<257::aid-hec427

keywords:

subject

Has Abstract

pub_date

1999-05-01 00:00:00

pages

257-61

issue

3

eissn

1057-9230

issn

1099-1050

pii

10.1002/(SICI)1099-1050(199905)8:3<257::AID-HEC427

journal_volume

8

pub_type

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