• cost–effectiveness analysis;
  • covariate adjustment;
  • subgroups;
  • multicentre studies;
  • hierarchical models


Background: Overall assessments of cost–effectiveness are now commonplace in informing medical policy decision making. It is often important, however, also to investigate how cost–effectiveness varies between patient subgroups. Yet such analyses are rarely undertaken, because appropriate methods have not been sufficiently developed.

Methods: We propose a coherent set of Bayesian methods to extend cost–effectiveness analyses to adjust for baseline covariates, to investigate differences between subgroups, and to allow for differences between centres in a multicentre study using a hierarchical model. These methods consider costs and effects jointly, and allow for the typically skewed distribution of cost data. The results are presented as inferences on the cost–effectiveness plane, and as cost–effectiveness acceptability curves.

Results: In applying these methods to a randomised trial of case management of psychotic patients, we show that overall cost–effectiveness can be affected by ignoring the skewness of cost data, but that it may be difficult to gain substantial precision by adjusting for baseline covariates. While analyses of overall cost–effectiveness can mask important subgroup differences, crude differences between centres may provide an unrealistic indication of the true differences between them.

Conclusions: The methods developed allow a flexible choice for the distributions used for cost data, and have a wide range of applicability – to both randomised trials and observational studies. Experience needs to be gained in applying these methods in practice, and using their results in decision making. Copyright © 2005 John Wiley & Sons, Ltd.