The Choice of Prior Distribution for A Covariance Matrix in Multivariate Meta-Analysis: A Simulation Study
Statistics in Medicine
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 errors, and close to nominal coverage for the effect size estimates were identified. Our results demonstrate the need for sensitivity analysis and suggest some guidelines for choosing prior distributions in this class of problems. The MBMA models proposed here are illustrated in a small meta-analysis example from the periodontal field and a medium meta-analysis from the study of stroke.
Hurtado Rua, Sandra M.; Mazumdar, Madhu; and Strawderman, Robert L., "The Choice of Prior Distribution for A Covariance Matrix in Multivariate Meta-Analysis: A Simulation Study" (2015). Mathematics Faculty Publications. 240.
This is the accepted version of the following article: A. L. Perez et al. "The Choice of Prior Distribution for A Covariance Matrix in Multivariate Meta-Analysis: A Simulation Study," , Statistics in Medicine, vol. 34, no.30, pp. 4083-4104, 2015, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/sim.6631
They were partially funded by NIH CTSA funding to Weill Medical College of Cornell University (2UL1 TR000457-06).