Using covariates to reduce uncertainty in the economic evaluation of clinical trial data
Article first published online: 20 OCT 2004
Copyright © 2004 John Wiley & Sons, Ltd.
Volume 14, Issue 6, pages 545–557, June 2005
How to Cite
Vázquez-Polo, F. J., Negrín Hernández, M. A. and López-Valcárcel, B. G. (2005), Using covariates to reduce uncertainty in the economic evaluation of clinical trial data. Health Econ., 14: 545–557. doi: 10.1002/hec.947
- Issue published online: 9 MAY 2005
- Article first published online: 20 OCT 2004
- Manuscript Accepted: 1 JUN 2004
- Manuscript Received: 1 MAR 2002
- Bayesian analysis;
- Gibbs sampling;
- Markov chain Monte Carlo (MCMC) methods;
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.