Jackson Dan, Bujkiewicz Sylwia, Law Martin, Riley Richard D, White Ian R
MRC Biostatistics Unit, Cambridge, U.K.
Biostatistics Research Group, Department of Health Sciences, University of Leicester, U.K.
Biometrics. 2018 Jun;74(2):548-556. doi: 10.1111/biom.12762. Epub 2017 Aug 14.
Random-effects meta-analyses are very commonly used in medical statistics. Recent methodological developments include multivariate (multiple outcomes) and network (multiple treatments) meta-analysis. Here, we provide a new model and corresponding estimation procedure for multivariate network meta-analysis, so that multiple outcomes and treatments can be included in a single analysis. Our new multivariate model is a direct extension of a univariate model for network meta-analysis that has recently been proposed. We allow two types of unknown variance parameters in our model, which represent between-study heterogeneity and inconsistency. Inconsistency arises when different forms of direct and indirect evidence are not in agreement, even having taken between-study heterogeneity into account. However, the consistency assumption is often assumed in practice and so we also explain how to fit a reduced model which makes this assumption. Our estimation method extends several other commonly used methods for meta-analysis, including the method proposed by DerSimonian and Laird (). We investigate the use of our proposed methods in the context of both a simulation study and a real example.
随机效应荟萃分析在医学统计学中非常常用。最近的方法学进展包括多变量（多个结局）和网状（多种治疗）荟萃分析。在此，我们为多变量网状荟萃分析提供了一种新模型及相应的估计程序，以便在单一分析中纳入多个结局和治疗。我们的新多变量模型是最近提出的用于网状荟萃分析的单变量模型的直接扩展。我们在模型中允许两种类型的未知方差参数，它们分别代表研究间异质性和不一致性。当不同形式的直接和间接证据不一致时，即使考虑了研究间异质性，也会出现不一致性。然而，在实际中常常假定一致性假设成立，因此我们还解释了如何拟合一个做出此假设的简化模型。我们的估计方法扩展了其他几种常用的荟萃分析方法，包括DerSimonian和Laird提出的方法。我们在模拟研究和实际例子的背景下研究了我们所提出方法的应用。
