CINeMA: An approach for assessing confidence in the results of a network meta-analysis
CINeMA: An approach for assessing confidence in the results of a network meta-analysis
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DOI：
10.1371/journal.pmed.1003082
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发表时间：
2020-04-01
期刊：
影响因子：
15.8
通讯作者：
Salanti, Georgia
中科院分区：
文献类型：
--
作者：
Nikolakopoulou, Adriani;Higgins, Julian P. T.;Salanti, Georgia
研究背景对荟萃分析结果可信度的评价已成为证据合成过程的重要组成部分。我们提出了一个方法框架，以评估网络荟萃分析，网络荟萃分析（CINeMA）的结果的信心，当多个干预措施compared.MethodologyCINeMA考虑6个领域：（i）研究内的偏见，（ii）报告偏倚，（iii）间接性，（iv）不精确性，（v）异质性，（vi）不一致性。判断研究内偏倚和间接性的关键是百分比贡献矩阵，它显示了每项研究对网络荟萃分析结果的贡献。贡献矩阵可以使用免费的Web应用程序轻松计算。在评估的不精确性，异质性和不一致性，我们考虑这些组成部分的变化，形成临床decisions.ConclusionsVia 3例的影响，我们表明，CINeMA提高透明度，避免选择性使用的证据时，形成判断，从而限制主观性的过程中。CINeMA即使在大型复杂的网络中也易于应用。
BackgroundThe evaluation of the credibility of results from a meta-analysis has become an important part of the evidence synthesis process. We present a methodological framework to evaluate confidence in the results from network meta-analyses, Confidence in Network Meta-Analysis (CINeMA), when multiple interventions are compared.MethodologyCINeMA considers 6 domains: (i) within-study bias, (ii) reporting bias, (iii) indirectness, (iv) imprecision, (v) heterogeneity, and (vi) incoherence. Key to judgments about within-study bias and indirectness is the percentage contribution matrix, which shows how much information each study contributes to the results from network meta-analysis. The contribution matrix can easily be computed using a freely available web application. In evaluating imprecision, heterogeneity, and incoherence, we consider the impact of these components of variability in forming clinical decisions.ConclusionsVia 3 examples, we show that CINeMA improves transparency and avoids the selective use of evidence when forming judgments, thus limiting subjectivity in the process. CINeMA is easy to apply even in large and complicated networks.