Advanced methods in meta-analysis: multivariate approach and meta-regression
Advanced methods in meta-analysis: multivariate approach and meta-regression
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DOI：
10.1002/sim.1040
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发表时间：
2002-02-28
影响因子：
2
通讯作者：
Stijnen, T
中科院分区：
文献类型：
--
作者：
van Houwelingen, HC;Arends, LR;Stijnen, T
这篇关于荟萃分析的高级统计方法的教程可以被看作是诺曼德最近发表的关于荟萃分析的生物统计学文章的续集，该文章专注于基本方法。在一般线性混合模型的框架内使用近似似然。我们讨论的方法来分析单变量以及双变量的治疗效果的荟萃分析以及荟萃回归方法。几个扩展的模型进行了讨论，如精确似然，非正态混合和多个端点。最后，我们讨论了贝叶斯方法在荟萃分析中的应用。所有的方法都说明了一个关于卡介苗对结核病的疗效荟萃分析。所有使用近似可能性的分析都可以通过标准软件进行。我们演示了如何使用SAS Proc Mixed对模型进行过滤。版权所有（C）2002约翰威利父子有限公司
This tutorial on advanced statistical methods for meta-analysis can be seen as a sequel to the recent Tutorial in Biostatistics on meta-analysis by Normand, which focused on elementary methods. Within the framework of the general linear mixed model using approximate likelihood. we discuss methods to analyse univariate as well as bivariate treatment effects in meta-analyses as well as meta-regression methods. Several extensions of the models are discussed, like exact likelihood, non-normal mixtures and multiple endpoints. We end with a discussion about the use of Bayesian methods in meta-analysis. All methods are illustrated by a meta-analysis concerning the efficacy of BCG vaccine against tuberculosis. All analyses that use approximate likelihood can be carried out by standard software. We demonstrate how the models can be flitted using SAS Proc Mixed. Copyright (C) 2002 John Wiley Sons, Ltd.