Zeng Linan, Hultcrantz Monica, Tovey David, Santesso Nancy, Dahm Philipp, Brignardello-Petersen Romina, Mustafa Reem A, Murad M Hassan, Izcovich Ariel, de Beer Hans, Ragusa Martin Alberto, Johnston Bradley, Zhang Lingli, Iorio Alfonso, Guyatt Gordon
Pharmacy Department/Evidence-based Pharmacy Centre/Children's Medicine Key Laboratory of Sichuan Province, West China Second University Hospital, Sichuan University; Sichuan University and Key Laboratory of Birth Defects and Related Disease of Women and Children, Ministry of Education, West China Second University Hospital, Chengdu, People's Republic of China.
West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
BMJ Evid Based Med. 2025 May 20;30(3):202-207. doi: 10.1136/bmjebm-2024-113077.
When one initially targets the null effect and the point estimate falls close to the null, two challenges exist in rating certainty of evidence. First, when the point estimate is near the null and the data, therefore, suggests little or no effect, rating certainty in a benefit or harm is misleading. Second, since in general the narrower the confidence interval (CI) the more precise the estimate, if the CI is narrow, rating down for imprecision due simply to crossing the null is inappropriate. This paper addresses these issues and provides a solution: to revise the target of certainty rating from a non-zero effect to a little or no effect. This solution requires estimating a range in which the minimal important difference (MID) for benefit and an MID for harm might lie, and thus establishing a range that represents little or no effect. If GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) users are confident that the point estimate represents an effect less than the smallest plausible MID, they will revise their target and rate certainty to a little or no effect. If the entire CI falls within the range of little or no effect, they will not rate down for imprecision. Otherwise (if the CI includes an important effect), they will rate down. Using the solution provided in this paper GRADE users can make an optimal choice of the target of certainty rating.
当最初将无效效应作为目标且点估计值接近无效值时,在评估证据的确定性方面存在两个挑战。首先,当点估计值接近无效值,因此数据表明几乎没有或没有效应时,对益处或危害的确定性进行评级会产生误导。其次,一般来说,置信区间(CI)越窄,估计就越精确,如果CI很窄,仅仅因为跨越无效值就因不精确而降低评级是不合适的。本文探讨了这些问题并提供了一个解决方案:将确定性评级的目标从非零效应修订为几乎没有或没有效应。该解决方案需要估计益处的最小重要差异(MID)和危害的MID可能所在的范围,从而确定一个代表几乎没有或没有效应的范围。如果GRADE(推荐分级、评估、制定和评价)用户确信点估计值代表的效应小于最小的合理MID,他们将修订目标并将确定性评级为几乎没有或没有效应。如果整个CI落在几乎没有或没有效应的范围内,他们将不会因不精确而降低评级。否则(如果CI包含一个重要效应),他们将降低评级。使用本文提供的解决方案,GRADE用户可以对确定性评级的目标做出最佳选择。