Meta-analyses of clinical trials are increasingly seeking to go beyond estimating the effect of a treatment and may also aim to investigate the effect of other covariates and how they alter treatment effectiveness. This requires the estimation of treatment-covariate interactions. Meta-regression can be used to estimate such interactions using published data, but it is known to lack statistical power, and is prone to bias.The use of individual patient data can improve estimation of such interactions, among other benefits, but it can be difficult and time-consuming to collect and analyse. This paper derives, under certain conditions, the power of meta-regression and IPD methods to detect treatment-covariate interactions. These power formulae are shown to depend on heterogeneity in the covariate distributions across studies. This allows the derivation of simple tests, based on heterogeneity statistics, for comparing the statistical power of the analysis methods.
Copyright 2006 John Wiley & Sons, Ltd.