Multivariate meta-analysis
Multivariate meta-analysis
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DOI：
10.1002/sim.1410
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发表时间：
2003-07-30
影响因子：
2
通讯作者：
Garthwaite, P
中科院分区：
文献类型：
--
作者：
Nam, IS;Mengersen, K;Garthwaite, P
荟萃分析现在是一种标准的统计工具，用于在多个独立研究的基础上评估一种关系的总体强度和有趣特征。然而，最近人们认识到，在许多应用程序中，响应很少是唯一确定的。因此，重点已经从单一的反应转变为对多种结果的分析。本文提出并评价了三种贝叶斯多元元分析模型：两种传统单变量随机效应模型的多元类比模型，它们对研究和估计之间的关系做出了不同的假设；另一种多变量随机效应模型是混合模型方法的贝叶斯适应模型。然后，通过对父母吸烟和儿童两种健康结果（哮喘和下呼吸道疾病）的新数据集的分析，说明了我们首选的方法。版权所有：John Wiley Sons， Ltd。
Meta-analysis is now a standard statistical tool for assessing the overall strength and interesting features of a relationship, on the basis of multiple independent studies. There is, however, recent acknowledgement of the fact that in many applications responses are rarely uniquely determined. Hence there has been some change of focus from a single response to the analysis of multiple outcomes. In this paper we propose and evaluate three Bayesian multivariate meta-analysis models: two multivariate analogues of the traditional univariate random effects models which make different assumptions about the relationships between studies and estimates, and a multivariate random effects model which is a Bayesian adaptation of the mixed model approach. Our preferred method is then illustrated through an analysis of a new data set on parental smoking and two health outcomes (asthma and lower respiratory disease) in children. Copyright (C) 2003 John Wiley Sons, Ltd.