• Bayesian analysis;
  • cost-effectiveness;
  • Gibbs sampling;
  • Markov chain Monte Carlo (MCMC) methods;
  • uncertainty


As part of their practice, policymakers have to make economic evaluations using clinical trial data. Recent interest has been expressed in determining how cost-effectiveness analysis can be undertaken in a regression framework. In this respect, published research basically provides a general method for prognostic factor adjustment in the presence of imbalance, emphasizing sub-group analysis. In this paper, we present an alternative method from a Bayesian approach. We propose the use of covariates in Bayesian health technology assessment in order to reduce uncertainty about the effect of treatments. We show its advantages by comparison with another published method that do not adjust for covariates using simulated data. Copyright © 2004 John Wiley & Sons, Ltd.