Inference for correlated effect sizes using multiple univariate meta-analyses.

ABSTRACT: Multivariate meta-analysis, which involves jointly analyzing multiple and correlated outcomes from separate studies, has received a great deal of attention. One reason to prefer the multivariate approach is its ability to account for the dependence between multiple estimates from the same study. However, nearly all the existing methods for analyzing multivariate meta-analytic data require the knowledge of the within-study correlations, which are usually unavailable in practice. We propose a simple non-iterative method that can be used for the analysis of multivariate meta-analysis datasets, that has no convergence problems, and does not require the use of within-study correlations. Our approach uses standard univariate methods for the marginal effects but also provides valid joint inferenc

SUBMITTER: Chen Y 

PROVIDER: S-EPMC4821787 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature