Extending DerSimonian and Laird's methodology to perform multivariate random effects meta-analyses.
Multivariate meta-analysis is increasingly used in medical statistics. In the univariate setting, the non-iterative method proposed by DerSimonian and Laird is a simple and now standard way of performing random effects meta-analyses. We propose a natural and easily implemented multivariate extension of this procedure which is accessible to applied researchers and provides a much less computationally intensive alternative to existing methods. In a simulation study, the proposed procedure performs similarly in almost all ways to the more established iterative restricted maximum likelihood approach. The method is applied to some real data sets and an extension to multivariate meta-regression is described.
Jackson
Dan
D
MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge CB2 0SR, UK. daniel.jackson@mrc-bsu.cam.ac.uk
White
Ian R
IR
Thompson
Simon G
SG
D014408:Biomarkers, Tumor / Q000032:analysis; D056808:Biostatistics; D016001:Confidence Intervals; D005340:Fibrinogen / Q000032:analysis; D006801:Humans; D016013:Likelihood Functions; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D015999:Multivariate Analysis; D019098:Telomerase / Q000032:analysis; D001749:Urinary Bladder Neoplasms / Q000175:diagnosis
D014408:Biomarkers, Tumor; D005340:Fibrinogen; D019098:Telomerase