黎巴嫩贝鲁特美国大学医学中心Elie A Akl团队研究了系统评价中缺失结局数据对治疗效果的潜在影响。2020年8月26日，《英国医学杂志》发表了该成果。
为了评估在系统评价中缺失结局数据相关的偏倚风险，研究组进行了一项插补研究。筛选包括患者重要对立疗效结局有统计学显著效果的组水平荟萃分析的系统评价。
当应用以下每种假设时，即四种普遍讨论但不合理的假设：最佳情况、没有事件、都有事件和最坏情况；四种缺失数据的合理假设，即基于信息缺失优势比（IMOR）方法：IMOR 1.5（最不严格）、IMOR 2、IMOR 3、IMOR 5（最严格），观察每种方法超过无效结果阈值的荟萃分析的百分比，以及每种方法定性改变效果方向的荟萃分析的百分比。基于处理缺失数据的八种不同方法进行敏感性分析。
研究组共分析了100项系统评价，涉及653项随机对照试验。当应用不合理但普遍讨论的假设时，相对效果估计值的中位数变化范围为从0％到30.4％。超过无效效果阈值的荟萃分析的百分比从1％（最佳情况）到60％（最坏情况）不等，向最坏情况方向改变了26％。
在应用合理假设时，相对效果估计值的中位数百分比变化范围为从1.4％至7.0％。超过无效效果阈值的荟萃分析的百分比变化范围从6％（IMOR1.5）到22％（IMOR 5）不等，向最严格（IMOR 5）方向改变了2％。
研究结果表明，即使将合理假设应用于具有明确缺失数据的参与者的结果中，汇总相对效果估计的平均变化也是实质性的，并且几乎四分之一（22％）的荟萃分析超过了无效效果阈值。系统评价作者应提出缺失结果数据对其效果评估的潜在影响，并以此来告知其总体评级的偏倚风险及其对结果的解释。
附：英文原文
Title: Potential impact of missing outcome data on treatment effects in systematic reviews: imputation study
Author: Lara A Kahale, Assem M Khamis, Batoul Diab, Yaping Chang, Luciane Cruz Lopes, Arnav Agarwal, Ling Li, Reem A Mustafa, Serge Koujanian, Reem Waziry, Jason W Busse, Abeer Dakik, Holger J Schünemann, Lotty Hooft, Rob JPM Scholten, Gordon H Guyatt, Elie A Akl
Issue&Volume: 2020/08/26
Abstract: Objective To assess the risk of bias associated with missing outcome data in systematic reviews.
Design Imputation study.
Setting Systematic reviews.
Population 100 systematic reviews that included a group level meta-analysis with a statistically significant effect on a patient important dichotomous efficacy outcome.
Main outcome measures Median percentage change in the relative effect estimate when applying each of the following assumption (four commonly discussed but implausible assumptions (best case scenario, none had the event, all had the event, and worst case scenario) and four plausible assumptions for missing data based on the informative missingness odds ratio (IMOR) approach (IMOR 1.5 (least stringent), IMOR 2, IMOR 3, IMOR 5 (most stringent)); percentage of meta-analyses that crossed the threshold of the null effect for each method; and percentage of meta-analyses that qualitatively changed direction of effect for each method. Sensitivity analyses based on the eight different methods of handling missing data were conducted.
Results 100 systematic reviews with 653 randomised controlled trials were included. When applying the implausible but commonly discussed assumptions, the median change in the relative effect estimate varied from 0% to 30.4%. The percentage of meta-analyses crossing the threshold of the null effect varied from 1% (best case scenario) to 60% (worst case scenario), and 26% changed direction with the worst case scenario. When applying the plausible assumptions, the median percentage change in relative effect estimate varied from 1.4% to 7.0%. The percentage of meta-analyses crossing the threshold of the null effect varied from 6% (IMOR 1.5) to 22% (IMOR 5) of meta-analyses, and 2% changed direction with the most stringent (IMOR 5).
Conclusion Even when applying plausible assumptions to the outcomes of participants with definite missing data, the average change in pooled relative effect estimate is substantive, and almost a quarter (22%) of meta-analyses crossed the threshold of the null effect. Systematic review authors should present the potential impact of missing outcome data on their effect estimates and use this to inform their overall GRADE (grading of recommendations assessment, development, and evaluation) ratings of risk of bias and their interpretation of the results.
DOI: 10.1136/bmj.m2898
Source: https://www.bmj.com/content/370/bmj.m2898
BMJ-British Medical Journal:《英国医学杂志》，创刊于1840年。隶属于BMJ出版集团，最新IF：93.333
官方网址：http://www.bmj.com/
投稿链接：https://mc.manuscriptcentral.com/bmj
本期文章：《英国医学杂志》：Online/在线发表
