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Data · dataset · 2015

A Non-Iterative Extension of the Multivariate Random Effects Meta-Analysis

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Multivariate methods in meta-analysis are becoming popular and more accepted in biomedical research despite computational issues in some of the techniques.

Description

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.

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Simulation 75%

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Provenance · 2 source records, 19 field assertions
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DataCite10.6084/m9.figshare.102935412 d agoJSON v1
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