The choice of prior distribution for a covariance matrix in multivariate meta-analysis: a simulation study.

ABSTRACT: Bayesian meta-analysis is an increasingly important component of clinical research, with multivariate meta-analysis a promising tool for studies with multiple endpoints. Model assumptions, including the choice of priors, are crucial aspects of multivariate Bayesian meta-analysis (MBMA) models. In a given model, two different prior distributions can lead to different inferences about a particular parameter. A simulation study was performed in which the impact of families of prior distributions for the covariance matrix of a multivariate normal random effects MBMA model was analyzed. Inferences about effect sizes were not particularly sensitive to prior choice, but the related covariance estimates were. A few families of prior distributions with small relative biases, tight mean squared erro

SUBMITTER: Hurtado Rua SM 

PROVIDER: S-EPMC4715690 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature