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证据综合决策 4:基于随机对照试验的证据网络中的不一致性。

Evidence synthesis for decision making 4: inconsistency in networks of evidence based on randomized controlled trials.

机构信息

School of Social and Community Medicine, University of Bristol, Bristol, UK (SD, NJW, DMC, GL, AEA)

Department of Health Sciences, University of Leicester, Leicester, UK (AJS)

出版信息

Med Decis Making. 2013 Jul;33(5):641-56. doi: 10.1177/0272989X12455847.

Abstract

Inconsistency can be thought of as a conflict between "direct" evidence on a comparison between treatments B and C and "indirect" evidence gained from AC and AB trials. Like heterogeneity, inconsistency is caused by effect modifiers and specifically by an imbalance in the distribution of effect modifiers in the direct and indirect evidence. Defining inconsistency as a property of loops of evidence, the relation between inconsistency and heterogeneity and the difficulties created by multiarm trials are described. We set out an approach to assessing consistency in 3-treatment triangular networks and in larger circuit structures, its extension to certain special structures in which independent tests for inconsistencies can be created, and describe methods suitable for more complex networks. Sample WinBUGS code is given in an appendix. Steps that can be taken to minimize the risk of drawing incorrect conclusions from indirect comparisons and network meta-analysis are the same steps that will minimize heterogeneity in pairwise meta-analysis. Empirical indicators that can provide reassurance and the question of how to respond to inconsistency are also discussed.

摘要

可以将不一致性视为治疗 B 和 C 之间的“直接”比较的证据与 AC 和 AB 试验获得的“间接”证据之间的冲突。与异质性一样,不一致性是由效应修饰剂引起的,特别是由直接和间接证据中效应修饰剂分布的不平衡引起的。将不一致性定义为证据循环的属性,描述了不一致性与异质性之间的关系以及多臂试验带来的困难。我们提出了一种评估三治疗三角网络和更大电路结构中一致性的方法,将其扩展到可以创建独立的不一致性检验的某些特殊结构中,并描述了适用于更复杂网络的方法。附录中提供了样本 WinBUGS 代码。可以采取相同的步骤来最小化从间接比较和网络荟萃分析中得出错误结论的风险,这些步骤也将最小化成对荟萃分析中的异质性。还讨论了可以提供保证的经验指标以及如何应对不一致性的问题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/903b/3704208/dac523c10648/10.1177_0272989X12455847-fig1.jpg

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