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A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression

Authors
Journal
Biometrical Journal
0323-3847
Publisher
Wiley Blackwell (John Wiley & Sons)
Publication Date
Volume
55
Issue
2
Identifiers
DOI: 10.1002/bimj.201200152
Keywords
  • Random Effects And Meta-Analysis
Disciplines
  • Mathematics

Abstract

Multivariate meta-analysis is becoming more commonly used. Methods for fitting the multivariate random effects model include maximum likelihood, restricted maximum likelihood, Bayesian estimation and multivariate generalisations of the standard univariate method of moments. Here, we provide a new multivariate method of moments for estimating the between-study covariance matrix with the properties that (1) it allows for either complete or incomplete outcomes and (2) it allows for covariates through meta-regression. Further, for complete data, it is invariant to linear transformations. Our method reduces to the usual univariate method of moments, proposed by DerSimonian and Laird, in a single dimension. We illustrate our method and compare it with some of the alternatives using a simulation study and a real example.

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