Guyatt Gordon H, Ebrahim Shanil, Alonso-Coello Pablo, Johnston Bradley C, Mathioudakis Alexander G, Briel Matthias, Mustafa Reem A, Sun Xin, Walter Stephen D, Heels-Ansdell Diane, Neumann Ignacio, Kahale Lara A, Iorio Alfonso, Meerpohl Joerg, Schünemann Holger J, Akl Elie A
Department of Health Research Methods, Evidence and Impact, McMaster University, 1200 Main St. West, Hamilton L8S 4K1, Canada; Department of Medicine, McMaster University, 1200 Main St. West, Hamilton L8S 4K1, Canada.
Department of Health Research Methods, Evidence and Impact, McMaster University, 1200 Main St. West, Hamilton L8S 4K1, Canada; Systematic Overviews through Advancing Research Technology (SORT), Child Health Evaluative Sciences, The Hospital for Sick Children Research Institute, 555 University Ave, Toronto, ON M5G 1X8, Canada.
J Clin Epidemiol. 2017 Jul;87:14-22. doi: 10.1016/j.jclinepi.2017.05.005. Epub 2017 May 18.
To provide GRADE guidance for assessing risk of bias across an entire body of evidence consequent on missing data for systematic reviews of both binary and continuous outcomes.
Systematic survey of published methodological research, iterative discussions, testing in systematic reviews, and feedback from the GRADE Working Group.
Approaches begin with a primary meta-analysis using a complete case analysis followed by sensitivity meta-analyses imputing, in each study, data for those with missing data, and then pooling across studies. For binary outcomes, we suggest use of "plausible worst case" in which review authors assume that those with missing data in treatment arms have proportionally higher event rates than those followed successfully. For continuous outcomes, imputed mean values come from other studies within the systematic review and the standard deviation (SD) from the median SDs of the control arms of all studies.
If the results of the primary meta-analysis are robust to the most extreme assumptions viewed as plausible, one does not rate down certainty in the evidence for risk of bias due to missing participant outcome data. If the results prove not robust to plausible assumptions, one would rate down certainty in the evidence for risk of bias.
为评估因二元和连续型结局的系统评价中存在数据缺失而导致的整个证据体的偏倚风险提供GRADE指南。
对已发表的方法学研究进行系统调查、反复讨论、在系统评价中进行测试以及收集GRADE工作组的反馈。
方法首先是使用完全病例分析进行初步的荟萃分析，然后进行敏感性荟萃分析，在每项研究中对缺失数据的个体进行数据推算，随后在各项研究间进行合并。对于二元结局，我们建议使用“似然最差情况”，即综述作者假设治疗组中缺失数据的个体的事件发生率按比例高于成功随访个体的事件发生率。对于连续型结局，推算的均值来自系统评价中的其他研究，标准差（SD）来自所有研究对照组中位数标准差。
如果初步荟萃分析的结果对于视为似然的最极端假设具有稳健性，则不会因参与者结局数据缺失导致的偏倚风险而降低证据的确定性等级。如果结果对似然假设不具有稳健性，则会降低证据中偏倚风险的确定性等级。
