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J Biopharm Stat. 2015;25(1):109-23. doi: 10.1080/10543406.2014.919930.

A non-iterative extension of the multivariate random effects meta-analysis.

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a Department of Biostatistics, Bioinformatics, and Biomathematics , Georgetown University , Washington , DC , USA.


Multivariate methods in meta-analysis are becoming popular and more accepted in biomedical research despite computational issues in some of the techniques. A number of approaches, both iterative and non-iterative, have been proposed including the multivariate DerSimonian and Laird method by Jackson et al. (2010), which is non-iterative. In this study, we propose an extension of the method by Hartung and Makambi (2002) and Makambi (2001) to multivariate situations. A comparison of the bias and mean square error from a simulation study indicates that, in some circumstances, the proposed approach perform better than the multivariate DerSimonian-Laird approach. An example is presented to demonstrate the application of the proposed approach.


Bias; Bivariate outcome; Coverage probabilities; Mean square error; Univariate meta-analysis

[Indexed for MEDLINE]

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