Journal Article

A Matrix-Based Method of Moments for Fitting Multivariate Network Meta-Analysis Models with Multiple Outcomes and Random Inconsistency Effects

Riley, Richard D., Jackson, Dan, Law, Martin, White, Ian R., Bujkiewicz, Sylwia

2018

Overview

Random-effects meta-analyses are very commonly used in medical statistics. Recent methodological developments include multivariate (multiple outcomes) and network (multiple treatments) meta-analysis. Here, we provide a new model and corresponding estimation procedure for multivariate network meta-analysis, so that multiple outcomes and treatments can be included in a single analysis. Our new multivariate model is a direct extension of a univariate model for network metaanalysis that has recently been proposed. We allow two types of unknown variance parameters in our model, which represent between-study heterogeneity and inconsistency. Inconsistency arises when different forms of direct and indirect evidence are not in agreement, even having taken between-study heterogeneity into account. However, the consistency assumption is often assumed in practice and so we also explain how to fit a reduced model which makes this assumption. Our estimation method extends several other commonly used methods for meta-analysis, including the method proposed by DerSimonian and Laird (1986). We investigate the use of our proposed methods in the context of both a simulation study and a real example.

• Publisher: Wiley-Blackwell,Blackwell Publishing Ltd
• Subject: BIOMETRIC METHODOLOGY: DISCUSSION PAPER / biometry / Computer Simulation / Heterogeneity / Incoherence / Meta-analysis / Method of moments / Mixed treatment comparisons / Models, Statistical / Multiple treatments meta‐analysis / Multivariate Analysis / Network Meta-Analysis as Topic / Random‐effects models / Statistical analysis / variance