Checking consistency in mixed treatment comparison meta-analysis
Checking consistency in mixed treatment comparison meta-analysis
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DOI：
10.1002/sim.3767
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发表时间：
2010-03-01
影响因子：
2
通讯作者：
Ades, A. E.
中科院分区：
文献类型：
--
作者：
Dias, S.;Welton, N. J.;Ades, A. E.
汇集来自随机试验的直接和间接证据，称为混合治疗比较（MTC），在临床文献中变得越来越普遍。 MTC 可以对几种治疗方法中哪一种最有效做出一致的判断，并生成每种治疗方法与网络中其他治疗方法相比的相对效果的估计。我们引入了两种检查直接和间接证据一致性的方法。第一种方法（反算）从直接证据和 MTC 分析的输出推断间接证据的贡献，并且当唯一可用数据由成对对比的汇总摘要组成时非常有用。第二种更通用但计算密集的方法基于“节点分裂”，它将特定比较（节点）的证据分为“直接”和“间接”，并且可以应用于可获得试验级数据的网络。方法用文献中的例子进行说明。我们采用分层贝叶斯方法来使用 WinBUGS 和 R 实施 MTC。我们表明，这两种方法都可用于识别不同类型网络中潜在的不一致，并且它们说明了直接和间接证据如何结合起来产生相对治疗效果的后 MTC 估计。这使用户能够了解 MTC 综合如何汇集数据，以及什么在“驱动”最终估计。最后，我们对正在做出的建模假设、将反算方法扩展到试验级数据的问题进行了一些考虑，并在现有文献的背景下讨论了我们的方法。版权所有 (C) 2010 约翰·威利父子有限公司
Pooling of direct and indirect evidence from randomized trials, known as mixed treatment comparisons (MTC), is becoming increasingly common in the clinical literature. MTC allows coherent judgements on which of the several treatments is the most effective and produces estimates of the relative effects of each treatment compared with every other treatment in a network.We introduce two methods for checking consistency of direct and indirect evidence. The first method (back-calculation) infers the contribution of indirect evidence from the direct evidence and the output of an MTC analysis and is useful when the only available data consist of pooled summaries of the pairwise contrasts. The second more general, but computationally intensive, method is based on 'node-splitting' which separates evidence on a particular comparison (node) into 'direct' and 'indirect' and can be applied to networks where trial-level data are available. Methods are illustrated with examples from the literature. We take a hierarchical Bayesian approach to MTC implemented using WinBUGS and R.We show that both methods are useful in identifying potential inconsistencies in different types of network and that they illustrate how the direct and indirect evidence combine to produce the posterior MTC estimates of relative treatment effects. This allows users to understand how MTC synthesis is pooling the data, and what is 'driving' the final estimates.We end with some considerations on the modelling assumptions being made, the problems with the extension of the back-calculation method to trial-level data and discuss our methods in the context of the existing literature. Copyright (C) 2010 John Wiley & Sons, Ltd.