Jackson, D; Bujkiewicz, S; Law, M; Riley, RD; White, IR; (2017) A matrix-based method of moments for fitting multivariate network meta-analysis models with multiple outcomes and random inconsistency effects. Biometrics 10.1111/biom.12762. (In press).

Preview	Text (Published article) Jackson_Matrix based_method_moments_VoR.pdf - Published Version Download (183kB) | Preview
Preview	Text (Supplementary file 1) Jackson_Matrix based_method_moments_S1.pdf Download (227kB) | Preview
Preview	Text (Supplementary file 2) Jackson_Matrix based_method_moments_S2.pdf Download (347kB) | Preview

Abstract

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 meta-analysis 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 (). We investigate the use of our proposed methods in the context of both a simulation study and a real example.

Download activity - last month

Export as XMLCSVJSON

Download activity - last 12 months

Export as JSONCSVXML

Downloads by country - last 12 months

Export as JSONCSVXML

Archive Staff Only

View Item