A non-iterative extension of the multivariate random effects meta-analysis.
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.
a Department of Biostatistics, Bioinformatics, and Biomathematics , Georgetown University , Washington , DC , USA.
Kepher H
KH
Makambi
Hyunuk
H
Seung
D015982:Bias; D014408:Biomarkers, Tumor / Q000032:analysis; D003198:Computer Simulation; D006801:Humans; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D009010:Monte Carlo Method; D015999:Multivariate Analysis; D011237:Predictive Value of Tests; D019098:Telomerase / Q000032:analysis; D001749:Urinary Bladder Neoplasms / Q000175:diagnosis / Q000201:enzymology
D014408:Biomarkers, Tumor; D019098:Telomerase
Bias; Bivariate outcome; Coverage probabilities; Mean square error; Univariate meta-analysis