Bayesian inference on risk differences: an application to multivariate meta-analysis of adverse events in clinical trials.


Journal Article

Multivariate meta-analysis is useful in combining evidence from independent studies which involve several comparisons among groups based on a single outcome. For binary outcomes, the commonly used statistical models for multivariate meta-analysis are multivariate generalized linear mixed effects models which assume risks, after some transformation, follow a multivariate normal distribution with possible correlations. In this article, we consider an alternative model for multivariate meta-analysis where the risks are modeled by the multivariate beta distribution proposed by Sarmanov (1966). This model have several attractive features compared to the conventional multivariate generalized linear mixed effects models, including simplicity of likelihood function, no need to specify a link function, and has a closed-form expression of distribution functions for study-specific risk differences. We investigate the finite sample performance of this model by simulation studies and illustrate its use with an application to multivariate meta-analysis of adverse events of tricyclic antidepressants treatment in clinical trials.

Full Text

Duke Authors

Cited Authors

  • Chen, Y; Luo, S; Chu, H; Wei, P

Published Date

  • May 1, 2013

Published In

Volume / Issue

  • 5 / 2

Start / End Page

  • 142 - 155

PubMed ID

  • 23853700

Pubmed Central ID

  • 23853700

International Standard Serial Number (ISSN)

  • 1946-6315

Digital Object Identifier (DOI)

  • 10.1080/19466315.2013.791483


  • eng

Conference Location

  • United States