title: 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 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. Copyright © 2015 John Wiley & Sons, Ltd.
authors: Department of Mathematics, Cleveland State University, 2121 Euclid Avenue, RT 1515, Cleveland, 44115-2214, OH, U.S.A.
authors: Sandra M
authors: SM
authors: Hurtado Rúa
authors: Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, Room L2-70L, New York, 10029, NY.
authors: Madhu
authors: M
authors: Mazumdar
authors: Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Box 630, Rochester, 14642, NY.
authors: Robert L
authors: RL
authors: Strawderman
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D056808:Biostatistics; D002317:Cardiovascular Agents / Q000627:therapeutic use; D003198:Computer Simulation; D006801:Humans; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D015999:Multivariate Analysis; D010510:Periodontal Diseases / Q000628:therapy; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D020521:Stroke / Q000188:drug therapy
chemical_list: D002317:Cardiovascular Agents
keywords: Bayesian methods; Markov chain Monte Carlo; meta-analysis; multiple correlated outcomes; multivariate random-effects model; prior distributions; simulation study