A Bayesian model averaging approach for cost-effectiveness analyses
Article first published online: 12 SEP 2008
Copyright © 2008 John Wiley & Sons, Ltd.
Volume 18, Issue 7, pages 807–821, July 2009
How to Cite
Conigliani, C. and Tancredi, A. (2009), A Bayesian model averaging approach for cost-effectiveness analyses. Health Econ., 18: 807–821. doi: 10.1002/hec.1404
- Issue published online: 10 JUN 2009
- Article first published online: 12 SEP 2008
- Manuscript Accepted: 2 JUL 2008
- Manuscript Revised: 9 MAY 2008
- Manuscript Received: 23 JUL 2007
- Bayesian model averaging;
- cost data;
- health economics;
- sensitivity analysis
We consider the problem of assessing new and existing technologies for their cost-effectiveness in the case where data on both costs and effects are available from a clinical trial, and we address it by means of the cost-effectiveness acceptability curve. The main difficulty in these analyses is that cost data usually exhibit highly skew and heavy-tailed distributions so that it can be extremely difficult to produce realistic probabilistic models for the underlying population distribution, and in particular to model accurately the tail of the distribution, which is highly influential in estimating the population mean. Here, in order to integrate the uncertainty about the model into the analysis of cost data and into cost-effectiveness analyses, we consider an approach based on Bayesian model averaging: instead of choosing a single parametric model, we specify a set of plausible models for costs and estimate the mean cost with a weighted mean of its posterior expectations under each model, with weights given by the posterior model probabilities. The results are compared with those obtained with a semi-parametric approach that does not require any assumption about the distribution of costs. Copyright © 2008 John Wiley & Sons, Ltd.