Linear inference for mixed treatment comparison meta-analysis: A two-stage approach
Linear inference for mixed treatment comparison meta-analysis: A two-stage approach
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DOI：
10.1002/jrsm.34
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发表时间：
2011-03-01
影响因子：
9.8
通讯作者：
Ades, A. E.
中科院分区：
文献类型：
--
作者：
Lu, Guobing;Welton, Nicky J.;Ades, A. E.
混合治疗比较（MTC）荟萃分析综合了一个研究领域中可用的随机对照试验（RCT）集合中关于多种治疗或其他干预措施的比较证据，同时仍然尊重RCT中的随机化结构。本文旨在研究MTC估计的属性，并阐明MTC网络中直接和间接证据之间的一致性概念。我们将MTC合成分解为两个阶段。在第一阶段，在具有相同治疗对照的每组试验中进行普通荟萃分析，这提供了相对效应参数的“直接”估计。在第二阶段，最小化直接估计值和一致性超平面之间的距离的最佳一致性估计值可以被推导为具有表示一致性条件的特定设计矩阵的线性回归模型的加权最小二乘解。一致的MTC估计，然后可以明确表示为直接估计的线性组合，并在正态性假设下的整体证据的一致性，可以测试与似然比统计。这个两阶段的框架进一步允许我们使用杠杆统计来诊断第一阶段证据的影响，并使用回归残差来评估局部不一致性。该方法说明了两个例子，从医学研究。版权所有（C）2011约翰威利父子有限公司
Mixed treatment comparisons (MTC) meta-analysis synthesises comparative evidence on multiple treatments or other interventions from a collection of randomised controlled trials (RCT) available in a research area, while still respecting the randomisation structure in RCTs. This paper sets out to examine the properties of MTC estimates and elucidate the concept of consistency between direct and indirect evidence in MTC networks. We decompose MTC synthesis into two stages. At the first stage, ordinary meta-analysis is performed in each group of trials that have the same treatment comparators-this provides the 'direct' estimates of relative effect parameters. At the second stage, the optimal consistent estimates that minimise the distance between the direct estimates and the consistency hyper-plane can be deduced as the weighted least squares solution to a linear regression model with a specific design matrix that represents the consistency conditions. The consistent MTC estimates can then be represented explicitly as linear combinations of direct estimates, and under normality assumptions the overall evidence consistency can be tested with a likelihood-ratio statistic. This two-stage framework further allows us to use the leverage statistics to diagnose influence of the first-stage evidence and use the regression residuals to assess local inconsistency. The method is illustrated with two examples from medical research. Copyright (C) 2011 John Wiley & Sons, Ltd.