Abstract：
BACKGROUND: Missing outcome data is common in trials, and robust methods to address this are needed. Most trial reports currently use methods applicable under a missing completely at random assumption (MCAR), although this strong assumption can often be inappropriate.
OBJECTIVE: To identify and summarise current literature on the analytical methods for handling missing outcome data in randomised controlled trials (RCTs), emphasising methods appropriate for data missing at random (MAR) or missing not at random (MNAR).
METHODS: We conducted a methodological scoping review and identified papers through searching four databases (MEDLINE, Embase, CENTRAL, and CINAHL) from January 2015 to March 2023. We also performed forward and backward citation searching. Eligible papers discussed methods or frameworks for handling missing outcome data in RCTs or simulation studies with an RCT design.
RESULTS: From 1878 records screened, our search identified 101 eligible papers. 90 (89%) papers described specific methods for addressing missing outcome data and 11 (11%) described frameworks for overall methodological approach. Of the 90 methods papers, 30 (33%) described methods under the MAR assumption, 48 (53%) explored methods under the MNAR assumption and 11 (12%) discussed methods under a hybrid of MAR and MNAR assumptions. Control-based methods under the MNAR assumption were the most common method explored, followed by multiple imputation under the MAR assumption.
CONCLUSIONS: This review provides guidance on available analytic approaches for handling missing outcome data, particularly under the MNAR assumption. These findings may support trialists in using appropriate methods to address missing outcome data.
OBJECTIVE: To identify and summarise current literature on the analytical methods for handling missing outcome data in randomised controlled trials (RCTs), emphasising methods appropriate for data missing at random (MAR) or missing not at random (MNAR).
METHODS: We conducted a methodological scoping review and identified papers through searching four databases (MEDLINE, Embase, CENTRAL, and CINAHL) from January 2015 to March 2023. We also performed forward and backward citation searching. Eligible papers discussed methods or frameworks for handling missing outcome data in RCTs or simulation studies with an RCT design.
RESULTS: From 1878 records screened, our search identified 101 eligible papers. 90 (89%) papers described specific methods for addressing missing outcome data and 11 (11%) described frameworks for overall methodological approach. Of the 90 methods papers, 30 (33%) described methods under the MAR assumption, 48 (53%) explored methods under the MNAR assumption and 11 (12%) discussed methods under a hybrid of MAR and MNAR assumptions. Control-based methods under the MNAR assumption were the most common method explored, followed by multiple imputation under the MAR assumption.
CONCLUSIONS: This review provides guidance on available analytic approaches for handling missing outcome data, particularly under the MNAR assumption. These findings may support trialists in using appropriate methods to address missing outcome data.
摘要：
背景：缺少结果数据在试验中很常见，和强大的方法来解决这个问题是需要的。大多数试验报告目前使用适用于完全随机缺失假设(MCAR)的方法，尽管这种强烈的假设往往是不恰当的。
目的：确定和总结目前关于处理随机对照试验（RCT）中缺失结果数据的分析方法的文献，强调适合随机缺失(MAR)或非随机缺失(MNAR)数据的方法。
方法：我们进行了方法学范围审查，并通过搜索四个数据库（MEDLINE，Embase,中部,和CINAHL)从2015年1月到2023年3月。我们还进行了向前和向后引文搜索。符合条件的论文讨论了在RCT或RCT设计的模拟研究中处理缺失结果数据的方法或框架。
结果：从筛选的1878条记录中，我们的搜索确定了101份符合条件的论文.90篇(89%)论文描述了解决缺失结果数据的具体方法，11篇(11%)描述了总体方法学方法的框架。在90篇方法论文中，30(33%)描述了MAR假设下的方法，48（53％）在MNAR假设下探索了方法，11（12％）在MAR和MNAR假设的混合下讨论了方法。MNAR假设下的基于控制的方法是最常用的方法，其次是MAR假设下的多重插补。
结论：本综述为处理缺失结果数据的可用分析方法提供了指导，特别是在MNAR假设下。这些发现可能支持试验人员使用适当的方法来解决缺失的结果数据。
目的：确定和总结目前关于处理随机对照试验（RCT）中缺失结果数据的分析方法的文献，强调适合随机缺失(MAR)或非随机缺失(MNAR)数据的方法。
方法：我们进行了方法学范围审查，并通过搜索四个数据库（MEDLINE，Embase,中部,和CINAHL)从2015年1月到2023年3月。我们还进行了向前和向后引文搜索。符合条件的论文讨论了在RCT或RCT设计的模拟研究中处理缺失结果数据的方法或框架。
结果：从筛选的1878条记录中，我们的搜索确定了101份符合条件的论文.90篇(89%)论文描述了解决缺失结果数据的具体方法，11篇(11%)描述了总体方法学方法的框架。在90篇方法论文中，30(33%)描述了MAR假设下的方法，48（53％）在MNAR假设下探索了方法，11（12％）在MAR和MNAR假设的混合下讨论了方法。MNAR假设下的基于控制的方法是最常用的方法，其次是MAR假设下的多重插补。
结论：本综述为处理缺失结果数据的可用分析方法提供了指导，特别是在MNAR假设下。这些发现可能支持试验人员使用适当的方法来解决缺失的结果数据。
