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在成分网络荟萃分析中对证据结构进行图形评估。

Graphical evaluation of evidence structure within a component network meta-analysis.

作者信息

Li Hua, Shih Ming-Chieh, Tu Yu-Kang

机构信息

Institute of Epidemiology & Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.

Department of Applied Mathematics, A330 Science and Engineering Building I, National Dong Hwa University, Hualien, Taiwan.

出版信息

Res Synth Methods. 2023 Jul;14(4):596-607. doi: 10.1002/jrsm.1623. Epub 2023 Feb 7.

Abstract

Component network meta-analysis (CNMA) compares treatments comprising multiple components and estimates the effects of individual components. For network meta-analysis, a standard network plot with nodes for treatments and edges for direct comparisons between treatments is drawn to visualize the evidence structure and the connections between treatments. However, the standard network plot does not effectively illustrate the connections between components for a CNMA. For example, the comparison between linear combinations of components within a trial is not shown directly in a standard network plot, and whether all components are identifiable cannot be deduced directly from the plot. Therefore, we need a new approach to visualizing the evidence structure of a CNMA. In this article, we proposed a new graph, a modified signal-flow graph representing a system of equations, to evaluate the evidence structure for CNMA. In our new graph, each node represents a component, and arrows are used to show linear relationships between components. We used two examples to demonstrate how to draw and interpret the graph and how to use it to identify components that require more evidence.

摘要

成分网络荟萃分析(CNMA)比较包含多个成分的治疗方法,并估计各个成分的效果。对于网络荟萃分析,绘制一个标准的网络图,其中节点表示治疗方法,边表示治疗方法之间的直接比较,以可视化证据结构和治疗方法之间的联系。然而,标准网络图并不能有效地说明CNMA中各成分之间的联系。例如,试验中成分的线性组合之间的比较在标准网络图中没有直接显示,而且不能直接从图中推断出所有成分是否可识别。因此,我们需要一种新的方法来可视化CNMA的证据结构。在本文中,我们提出了一种新的图形,即一种表示方程组的修正信号流图,以评估CNMA的证据结构。在我们的新图形中,每个节点代表一个成分,箭头用于表示成分之间的线性关系。我们用两个例子来说明如何绘制和解释该图形,以及如何用它来识别需要更多证据的成分。

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