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Meta‐analysis methods and models with applications in evaluation of cholesterol‐lowering drugs

In this paper, we propose a class of multivariate random effects models allowing for the inclusion of study‐level covariates to carry out meta‐analyses. As existing algorithms for computing maximum likelihood estimates often converge poorly or may not converge at all when the random effects are multi‐dimensional, we develop an efficient expectation–maximization algorithm for fitting multi‐dimensional random effects regression models. In addition, we also develop a new methodology for carrying out variable selection with study‐level covariates. We examine the performance of the proposed methodology via a simulation study. We apply the proposed methodology to analyze metadata from 26 studies involving statins as a monotherapy and in combination with ezetimibe. In particular, we compare the low‐density lipoprotein cholesterol‐lowering efficacy of monotherapy and combination therapy on two patient populations (naïve and non‐naïve patients to statin monotherapy at baseline), controlling for aggregate covariates. The proposed methodology is quite general and can be applied in any meta‐analysis setting for a wide range of scientific applications and therefore offers new analytic methods of clinical importance. Copyright © 2012 John Wiley & Sons, Ltd.