Nam In-Sun, Mengersen Kerrie, Garthwaite Paul
Queensland University of Technology, Australia.
Stat Med. 2003 Jul 30;22(14):2309-33. doi: 10.1002/sim.1410.
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.
荟萃分析如今是一种标准的统计工具，用于基于多项独立研究评估某种关系的总体强度和有趣特征。然而，最近人们认识到，在许多应用中，反应很少是唯一确定的。因此，关注点已从单一反应有所转变，转向对多个结果的分析。在本文中，我们提出并评估了三种贝叶斯多元荟萃分析模型：传统单变量随机效应模型的两种多元类似模型，它们对研究与估计之间的关系做出了不同假设；以及一种多元随机效应模型，它是混合模型方法的贝叶斯改编版。然后，我们通过分析一个关于父母吸烟与儿童两种健康结果（哮喘和下呼吸道疾病）的新数据集，来说明我们首选的方法。
