Hurtado Rúa Sandra M, Mazumdar Madhu, Strawderman Robert L
Department of Mathematics, Cleveland State University, 2121 Euclid Avenue, RT 1515, Cleveland, 44115-2214, OH, U.S.A.
Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, Room L2-70L, New York, 10029, NY.
Stat Med. 2015 Dec 30;34(30):4083-104. doi: 10.1002/sim.6631. Epub 2015 Aug 24.
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.
贝叶斯荟萃分析是临床研究中日益重要的组成部分，多变量荟萃分析是用于多终点研究的一种很有前景的工具。模型假设，包括先验的选择，是多变量贝叶斯荟萃分析（MBMA）模型的关键方面。在给定模型中，两种不同的先验分布可能会对特定参数得出不同的推断。进行了一项模拟研究，分析了多变量正态随机效应MBMA模型协方差矩阵的先验分布族的影响。对效应量的推断对先验选择不是特别敏感，但相关的协方差估计则不然。确定了一些相对偏差小、均方误差紧密且效应量估计接近名义覆盖率的先验分布族。我们的结果表明需要进行敏感性分析，并为这类问题中先验分布的选择提出了一些指导原则。这里提出的MBMA模型在牙周领域一个小型荟萃分析示例和中风研究的一个中型荟萃分析中进行了说明。版权所有© 2015约翰威立父子有限公司。
