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Biom J. 2017 May;59(3):496-510. doi: 10.1002/bimj.201600013. Epub 2017 Feb 14.

Multivariate meta-analysis with an increasing number of parameters.

Author information

Innovation Center for Biomedical Informatics, Georgetown University Medical Center, 2115 Wisconsin Avenue, Suite 110, Washington, DC 20007, USA.
Department of Oncology, Georgetown University Medical Center, 3970 Reservoir Road NW, Research Building, Suite E501, Washington, DC 20057, USA.
Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, 4000 Reservoir Road NW, Washington, DC 20057, USA.
Division of Cancer Epidemiology and Genetics, Biostatistics Branch, National Cancer Institute, 9609 Medical Center Drive, MSC 9776, Bethesda, MD 20892, USA.


Meta-analysis can average estimates of multiple parameters, such as a treatment's effect on multiple outcomes, across studies. Univariate meta-analysis (UVMA) considers each parameter individually, while multivariate meta-analysis (MVMA) considers the parameters jointly and accounts for the correlation between their estimates. The performance of MVMA and UVMA has been extensively compared in scenarios with two parameters. Our objective is to compare the performance of MVMA and UVMA as the number of parameters, p, increases. Specifically, we show that (i) for fixed-effect (FE) meta-analysis, the benefit from using MVMA can substantially increase as p increases; (ii) for random effects (RE) meta-analysis, the benefit from MVMA can increase as p increases, but the potential improvement is modest in the presence of high between-study variability and the actual improvement is further reduced by the need to estimate an increasingly large between study covariance matrix; and (iii) when there is little to no between-study variability, the loss of efficiency due to choosing RE MVMA over FE MVMA increases as p increases. We demonstrate these three features through theory, simulation, and a meta-analysis of risk factors for non-Hodgkin lymphoma.


Efficiency; Fixed-effect models; Multivariate meta-analysis; Random effects models

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