A matrix-based method of moments for fitting multivariate network meta-analysis models with multiple outcomes and random inconsistency effects.
A matrix-based method of moments for fitting multivariate network meta-analysis models with multiple outcomes and random inconsistency effects.
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DOI：
10.1111/biom.12762
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发表时间：
2018-06
期刊：
影响因子：
1.9
通讯作者：
White IR
中科院分区：
文献类型：
--
作者：
Jackson D;Bujkiewicz S;Law M;Riley RD;White IR
随机效应荟萃分析在医学统计学中非常常用。最近的方法学发展包括多变量（多个结果）和网络（多个治疗）荟萃分析。在这里，我们提供了一个新的模型和相应的估计程序的多变量网络荟萃分析，使多个结果和治疗可以包括在一个单一的分析。我们新的多变量模型是最近提出的网络元分析单变量模型的直接扩展。在我们的模型中，我们允许两种类型的未知方差参数，这代表了研究间的异质性和不一致性。当不同形式的直接和间接证据不一致时，即使考虑到研究间的异质性，也会出现不一致。然而，一致性假设在实践中经常被假设，因此我们也解释了如何拟合做出这种假设的简化模型。我们的估计方法扩展了其他几种常用的荟萃分析方法，包括提出的方法。我们调查使用我们提出的方法在模拟研究和真实的例子的背景下。
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. We investigate the use of our proposed methods in the context of both a simulation study and a real example.
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影响因子：
2
作者：
Bujkiewicz S;Thompson JR;Riley RD;Abrams KR
通讯作者：
Abrams KR
影响因子：
2
作者：
Jackson, Daniel;Riley, Richard D.
通讯作者：
Riley, Richard D.
影响因子：
1.9
作者：
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通讯作者：
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2
作者：
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通讯作者：
Salanti, Georgia
影响因子：
2
作者：
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通讯作者：
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