Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies.
Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies.
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DOI：
10.1002/jrsm.1044
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发表时间：
2012-06
影响因子：
9.8
通讯作者：
White, I. R.
中科院分区：
文献类型：
--
作者：
Higgins, J. P. T.;Jackson, D.;Barrett, J. K.;Lu, G.;Ades, A. E.;White, I. R.
关键词：
同时比较多种治疗的荟萃分析（通常称为网络荟萃分析或混合治疗比较）正变得越来越普遍。网络荟萃分析的一个重要组成部分是评估不同证据来源在本质上和统计上的相容性程度。如果涉及一种感兴趣的治疗方法的研究与涉及另一种感兴趣的治疗方法的研究根本不同，则简单的间接比较可能会混淆。在这里，我们讨论了解决不同治疗的比较研究中证据不一致的方法。我们定义并回顾了异构和不一致的基本概念，并尝试引入“循环不一致”和“设计不一致”之间的区别。然后，我们提出按处理设计交互的概念为研究不一致性提供了一个有用的通用框架。特别是，使用治疗设计相互作用成功地解决了证据网络中存在多组试验引起的并发症。我们展示了Lu和Ades提出的不一致模型是我们完整的按处理设计交互模型的一个限制版本，并且对于任何特定的数据集可能有几个不同的Lu - Ades模型。我们引入了新的图形方法来描绘证据网络，清楚地描绘多组试验，并说明可能出现不一致的地方。我们将各种不一致模型应用于四种戒烟干预措施中不同比较的试验数据，并表明仅寻求解决循环不一致的模型可能会遇到问题。版权所有©2012 John Wiley & Sons， Ltd。
Meta-analyses that simultaneously compare multiple treatments (usually referred to as network meta-analyses or mixed treatment comparisons) are becoming increasingly common. An important component of a network meta-analysis is an assessment of the extent to which different sources of evidence are compatible, both substantively and statistically. A simple indirect comparison may be confounded if the studies involving one of the treatments of interest are fundamentally different from the studies involving the other treatment of interest. Here, we discuss methods for addressing inconsistency of evidence from comparative studies of different treatments. We define and review basic concepts of heterogeneity and inconsistency, and attempt to introduce a distinction between ‘loop inconsistency’ and ‘design inconsistency’. We then propose that the notion of design-by-treatment interaction provides a useful general framework for investigating inconsistency. In particular, using design-by-treatment interactions successfully addresses complications that arise from the presence of multi-arm trials in an evidence network. We show how the inconsistency model proposed by Lu and Ades is a restricted version of our full design-by-treatment interaction model and that there may be several distinct Lu–Ades models for any particular data set. We introduce novel graphical methods for depicting networks of evidence, clearly depicting multi-arm trials and illustrating where there is potential for inconsistency to arise. We apply various inconsistency models to data from trials of different comparisons among four smoking cessation interventions and show that models seeking to address loop inconsistency alone can run into problems. Copyright © 2012 John Wiley & Sons, Ltd.
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影响因子：
9.8
作者：
Lu, Guobing;Welton, Nicky J.;Ades, A. E.
通讯作者：
Ades, A. E.
影响因子：
2
作者：
Cooper, Nicola J.;Sutton, Alex J.;Welton, Nicky J.
通讯作者：
Welton, Nicky J.
影响因子：
168.9
作者：
Chan, AW;Altman, DG
通讯作者：
Altman, DG
影响因子：
2
作者：
Dias, S.;Welton, N. J.;Ades, A. E.
通讯作者：
Ades, A. E.
影响因子：
2.3
作者：
Salanti, Georgia;Higgins, Julian P. T.;Ioannidis, John P. A.
通讯作者：
Ioannidis, John P. A.