Visualizing the flow of evidence in network meta-analysis and characterizing mixed treatment comparisons
Visualizing the flow of evidence in network meta-analysis and characterizing mixed treatment comparisons
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DOI：
10.1002/sim.6001
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发表时间：
2013-12-30
影响因子：
2
通讯作者：
Binder, Harald
中科院分区：
文献类型：
--
作者：
Koenig, Jochem;Krahn, Ulrike;Binder, Harald
网络荟萃分析技术允许汇集来自不同研究的证据，仅部分重叠设计，以获得更广泛的决策支持基础。结果是基于网络的效果估计，考虑了所有治疗对的间接证据。结果很大程度上取决于同质性和一致性假设，而这些假设有时很难调查。为了支持这种评估，我们建议展示证据流，并引入表征混合治疗比较结构的新措施。具体来说，考虑用于网络荟萃分析的线性固定效应模型，其中两种治疗的网络估计是比较这些或其他治疗的直接效应估计的线性组合。线性系数可以看作是经典荟萃分析中已知的权重的概括。我们总结了这些系数的属性，并将它们显示为加权有向无环图，代表证据流。此外，还引入了量化直接证据比例、平均路径长度和混合治疗比较的最小并行性的措施。两个已发表的网络荟萃分析的图形显示和测量进行了说明。在这些应用中，所提出的方法被认为使混合处理比较中的数据池过程变得透明。预计它们对于指导和促进网络荟萃分析中的有效性评估更加有用。版权所有 (c) 2013 John Wiley & Sons, Ltd.
Network meta-analysis techniques allow for pooling evidence from different studies with only partially overlapping designs for getting a broader basis for decision support. The results are network-based effect estimates that take indirect evidence into account for all pairs of treatments. The results critically depend on homogeneity and consistency assumptions, which are sometimes difficult to investigate. To support such evaluation, we propose a display of the flow of evidence and introduce new measures that characterize the structure of a mixed treatment comparison. Specifically, a linear fixed effects model for network meta-analysis is considered, where the network estimates for two treatments are linear combinations of direct effect estimates comparing these or other treatments. The linear coefficients can be seen as the generalization of weights known from classical meta-analysis. We summarize properties of these coefficients and display them as a weighted directed acyclic graph, representing the flow of evidence. Furthermore, measures are introduced that quantify the direct evidence proportion, the mean path length, and the minimal parallelism of mixed treatment comparisons. The graphical display and the measures are illustrated for two published network meta-analyses. In these applications, the proposed methods are seen to render transparent the process of data pooling in mixed treatment comparisons. They can be expected to be more generally useful for guiding and facilitating the validity assessment in network meta-analysis. Copyright (c) 2013 John Wiley & Sons, Ltd.