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

TitleBayesian inference on risk differences: an application to multivariate meta-analysis of adverse events in clinical trials.
Publication TypeJournal Article
Year of Publication2013
AuthorsChen, Yong, Sheng Luo, Haitao Chu, and Peng Wei
JournalStat Biopharm Res
Volume5
Issue2
Pagination142-155
Date Published2013 May 01
ISSN1946-6315
Abstract

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.

DOI10.1080/19466315.2013.791483
Alternate JournalStat Biopharm Res
Original PublicationBayesian inference on risk differences: an application to multivariate meta-analysis of adverse events in clinical trials.
PubMed ID23853700
PubMed Central IDPMC3706106
Grant ListU01 NS043127 / NS / NINDS NIH HHS / United States
P30 CA077598 / CA / NCI NIH HHS / United States
R03 HS020666 / HS / AHRQ HHS / United States
R01 HL095511 / HL / NHLBI NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
U01 NS043128 / NS / NINDS NIH HHS / United States
Project: