GRADE approach to drawing conclusions from a network meta-analysis using a partially contextualised framework
GRADE approach to drawing conclusions from a network meta-analysis using a partially contextualised framework
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DOI：
10.1136/bmj.m3907
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发表时间：
2020-11-10
影响因子：
105.7
通讯作者：
Schunemann, Holger J.
中科院分区：
文献类型：
--
作者：
Brignardello-Petersen, Romina;Izcovich, Ariel;Schunemann, Holger J.
本文描述了GRADE（分级推荐评估，发展和评价）指导如何从干预措施的网络荟萃分析中得出结论，包括每次一个结果的个体随机对照试验。该指南基于一种部分情境化的方法，在这种方法中，综述作者必须确定影响的大小范围，这些范围代表了微不足道到没有影响、小但重要的影响、中等影响和大影响。指导这一框架的原则是：干预措施应根据效果的大小进行分类；而且，将干预措施归入此类的判断应该考虑对效果的估计、证据的确定性和排名。我们用一个例子来描述和说明这个框架的四个步骤。
This article describes GRADE (grading of recommendations assessment, development and evaluation) guidance on how to make conclusions from a network meta-analysis of interventions that includes individual randomised controlled trials for one outcome at a time. The guidance is based on a partially contextualised approach in which review authors must establish ranges of magnitudes of effect that represent a trivial to no effect, small but important effect, moderate effect, and large effect. The principles guiding this framework are that interventions should be grouped in categories, based on the magnitude of the effect; and that the judgments that place interventions in such categories should consider the estimates of effect, the certainty of the evidence, and the rankings. We describe and illustrate the four steps of this framework using an example.