Bayesian Mixed Treatment Comparisons Meta-Analysis for Correlated Outcomes Subject to Reporting Bias

Abstract

Many randomized controlled trials (RCTs) report more than one primary outcome. As a result, multivariate meta-analytic methods for the assimilation of treatment effects in systematic reviews of RCTs have received increasing attention in the literature. These methods show promise with respect to bias reduction and efficiency gain compared to univariate meta-analysis. However, most methods for multivariate meta-analysis have focused on pairwise treatment comparisons (i.e., when the number of treatments is two). Current methods for mixed treatment comparisons (MTC) meta-analysis (i.e., when the number of treatments is more than two) have focused on univariate or very recently, bivariate outcomes. To broaden their application, we propose a framework for MTC meta-analysis of multivariate (≥ 2) outcomes where the correlations among multivariate outcomes within- and between-studies are accounted for through copulas, and the joint modeling of multivariate random effects, respectively. We consider a Bayesian hierarchical model using Markov Chain Monte Carlo methods for estimation. An important feature of the proposed framework is that it allows for borrowing of information across correlated outcomes. We show via simulation that our approach reduces the impact of outcome reporting bias (ORB) in a variety of missing outcome scenarios. We apply the method to a systematic review of RCTs of pharmacological treatments for alcohol dependence, which tends to report multiple outcomes potentially subject to ORB.

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