Quantifying indirect evidence in network meta-analysis.
Quantifying indirect evidence in network meta-analysis.
复制标题
量化网络荟萃分析中的间接证据。
DOI：
10.1002/sim.7187
复制
发表时间：
2017
影响因子：
2
通讯作者：
T. A.
中科院分区：
文献类型：
--
作者：
Noma;H.;Tanaka;S.;Matsui;S.;Cipriani;A.;Furukawa;T. A.
网络荟萃分析能够全面综合有关多种治疗的证据以及基于直接和间接证据的同时比较。网络荟萃分析的一个基本先决条件是从不同来源获得的证据的一致性，特别是直接和间接证据是否相互一致，以及它们如何影响总体估计。我们开发了一种有效的方法来量化间接证据，以及使用 Lindsay 的复合似然法来评估其不一致性的测试程序。我们还表明该估计量具有间接证据的完整信息。使用这种方法，我们可以评估直接证据和间接证据之间的一致性程度及其对总体估计的贡献率。也可以用这种方法进行敏感性分析，以评估潜在不一致的治疗对比对总体结果的影响。这些方法可以为总体比较结果提供有用的信息，这些结果可能因特定的不一致的治疗对比而产生偏差。我们还提供了一些关于多臂试验一致性限制的方法的有效推断的基本要求。此外，基于模拟研究证明了所开发方法的效率。介绍了 12 种新一代抗抑郁药的网络荟萃分析的应用。版权所有 © 2016 约翰·威利父子有限公司
Network meta‐analysis enables comprehensive synthesis of evidence concerning multiple treatments and their simultaneous comparisons based on both direct and indirect evidence. A fundamental pre‐requisite of network meta‐analysis is the consistency of evidence that is obtained from different sources, particularly whether direct and indirect evidence are in accordance with each other or not, and how they may influence the overall estimates. We have developed an efficient method to quantify indirect evidence, as well as a testing procedure to evaluate their inconsistency using Lindsay's composite likelihood method. We also show that this estimator has complete information for the indirect evidence. Using this method, we can assess the degree of consistency between direct and indirect evidence and their contribution rates to the overall estimate. Sensitivity analyses can be also conducted with this method to assess the influences of potentially inconsistent treatment contrasts on the overall results. These methods can provide useful information for overall comparative results that might be biased from specific inconsistent treatment contrasts. We also provide some fundamental requirements for valid inference on these methods concerning consistency restrictions on multi‐arm trials. In addition, the efficiency of the developed method is demonstrated based on simulation studies. Applications to a network meta‐analysis of 12 new‐generation antidepressants are presented. Copyright © 2016 John Wiley & Sons, Ltd.