Schünemann Holger J, Neumann Ignacio, Hultcrantz Monica, Brignardello-Petersen Romina, Zeng Linan, Murad M Hassan, Izcovich Ariel, Morgano Gian Paolo, Baldeh Tejan, Santesso Nancy, Cuello Carlos Garcia, Mbuagbaw Lawrence, Guyatt Gordon, Wiercioch Wojtek, Piggott Thomas, De Beer Hans, Vinceti Marco, Mathioudakis Alexander G, Mayer Martin G, Mustafa Reem, Filippini Tommaso, Iorio Alfonso, Nieuwlaat Robby, Marcucci Maura, Coello Pablo Alonso, Bonovas Stefanos, Piovani Daniele, Tomlinson George, Akl Elie A
World Health Organization Collaborating Center for Infectious Diseases, Research Methods and Recommendations, Michael G. DeGroote Cochrane Canada & McMaster GRADE Centres; McMaster University, Hamilton, Ontario, Canada; Department of Health Research Methods, Evidence and Impact, McMaster University, 1280 Main Street West, Hamilton, L8S 4L8, Ontario, Canada; Department of Medicine, McMaster University, 1280 Main Street West, Hamilton, L8S 4L8, Ontario, Canada; Department of Biomedical Sciences, Humanitas University, 20072 Pieve Emanuele, Milan, Italy.
Department of Health Research Methods, Evidence and Impact, McMaster University, 1280 Main Street West, Hamilton, L8S 4L8, Ontario, Canada; Escuela de Medicina, Facultad de Medicina y Ciencia, Universidad San Sebastián, Sede, Santiago, Santiago, Chile.
J Clin Epidemiol. 2022 Oct;150:225-242. doi: 10.1016/j.jclinepi.2022.07.015. Epub 2022 Aug 5.
Grading of Recommendations Assessment, Development and Evaluation (GRADE) guidance to rate the certainty domain of imprecision is presently not fully operationalized for rating down by two levels and when different baseline risk or uncertainty in these risks are considered. In addition, there are scenarios in which lowering the certainty of evidence by three levels for imprecision is more appropriate than lowering it by two levels. In this article, we conceptualize and operationalize rating down for imprecision by one, two and three levels for imprecision using the contextualized GRADE approaches and making decisions.
Through iterative discussions and refinement in online meetings and through email communication, we developed draft guidance to rating the certainty of evidence down by up to three levels based on examples. The lead authors revised the approach according to the feedback and the comments received during these meetings and developed GRADE guidance for how to apply it. We presented a summary of the results to all attendees of the GRADE Working Group meeting for feedback in October 2021 (approximately 80 people) where the approach was formally approved.
This guidance provides GRADE's novel approach for the considerations about rating down for imprecision by one, two and three levels based on serious, very serious and extremely serious concerns. The approach includes identifying or defining thresholds for health outcomes that correspond to trivial or none, small, moderate or large effects and using them to rate imprecision. It facilitates the use of evidence to decision frameworks and also provides guidance for how to address imprecision about implausible large effects and trivial or no effects using the concept of the 'review information size' and for varying baseline risks. The approach is illustrated using practical examples, an online calculator and graphical displays and can be applied to dichotomous and continuous outcomes.
In this GRADE guidance article, we provide updated guidance for how to rate imprecision using the partially and fully contextualized GRADE approaches for making recommendations or decisions, considering alternate baseline risks and for both dichotomous and continuous outcomes.
推荐评估、制定与评价（GRADE）中关于对不精确性这一确定性领域进行评级的指南，目前在向下降低两个等级进行评级以及考虑不同基线风险或这些风险中的不确定性时，尚未完全实施。此外，在某些情况下，因不精确性将证据确定性降低三个等级比降低两个等级更为合适。在本文中，我们使用情境化的GRADE方法并做出决策，对因不精确性而向下降低一、二、三个等级的评级进行概念化和操作化。
通过在线会议中的反复讨论和完善以及电子邮件沟通，我们基于实例制定了将证据确定性向下降低多达三个等级的指南草案。主要作者根据在这些会议期间收到的反馈和意见修订了该方法，并制定了关于如何应用它的GRADE指南。我们于2021年10月向GRADE工作组会议的所有与会者（约80人）展示了结果摘要以获取反馈，该方法在此正式获得批准。
本指南提供了GRADE的新颖方法，用于基于严重、非常严重和极其严重的担忧，考虑因不精确性而向下降低一、二、三个等级的评级。该方法包括识别或定义与微不足道或无影响、小影响、中等影响或大影响相对应的健康结果阈值，并使用它们来对不精确性进行评级。它有助于将证据用于决策框架，还提供了如何使用“综述信息规模”的概念以及针对不同基线风险来处理关于难以置信的大影响和微不足道或无影响的不精确性的指南。该方法通过实际示例、在线计算器和图形展示进行说明，并且可应用于二分法和连续性结果。
在这篇GRADE指南文章中，我们提供了更新后的指南，用于在做出推荐或决策时，考虑替代基线风险以及针对二分法和连续性结果，使用部分情境化和完全情境化的GRADE方法如何对不精确性进行评级。
