A Bayesian approach to stochastic cost-effectiveness analysis. An illustration and application to blood pressure control in type 2 diabetes

Int J Technol Assess Health Care. Winter 2001;17(1):69-82. doi: 10.1017/s0266462301104071.

Abstract

The aim of this paper is to discuss the use of Bayesian methods in cost-effectiveness analysis (CEA) and the common ground between Bayesian and traditional frequentist approaches. A further aim is to explore the use of the net benefit statistic and its advantages over the incremental cost-effectiveness ratio (ICER) statistic. In particular, the use of cost-effectiveness acceptability curves is examined as a device for presenting the implications of uncertainty in a CEA to decision makers. Although it is argued that the interpretation of such curves as the probability that an intervention is cost-effective given the data requires a Bayesian approach, this should generate no misgivings for the frequentist. Furthermore, cost-effectiveness acceptability curves estimated using the net benefit statistic are exactly equivalent to those estimated from an appropriate analysis of ICERs on the cost-effectiveness plane. The principles examined in this paper are illustrated by application to the cost-effectiveness of blood pressure control in the U.K. Prospective Diabetes Study (UKPDS 40). Due to a lack of good-quality prior information on the cost and effectiveness of blood pressure control in diabetes, a Bayesian analysis assuming an uninformative prior is argued to be most appropriate. This generates exactly the same cost-effectiveness results as a standard frequentist analysis.

Publication types

  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Bayes Theorem*
  • Clinical Trials as Topic
  • Cost-Benefit Analysis / methods*
  • Decision Making*
  • Diabetes Mellitus, Type 2 / complications
  • Diabetes Mellitus, Type 2 / economics*
  • Humans
  • Hypertension / complications
  • Hypertension / economics*
  • Hypertension / prevention & control*
  • Stochastic Processes
  • Value of Life