A Bayesian approach to stochastic cost-effectiveness analysis

Health Econ. 1999 May;8(3):257-61. doi: 10.1002/(sici)1099-1050(199905)8:3<257::aid-hec427>3.0.co;2-e.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bayes Theorem*
  • Clinical Trials as Topic / economics
  • Clinical Trials as Topic / statistics & numerical data
  • Cost-Benefit Analysis / methods*
  • Cost-Benefit Analysis / statistics & numerical data
  • Decision Support Techniques
  • Health Services Research / economics
  • Health Services Research / methods*
  • Health Services Research / statistics & numerical data
  • Humans
  • Likelihood Functions