背景:缺失的数据在观察性研究中很常见,并且经常发生在估计因果效应时所需要的几个变量中,即曝光,用于控制混杂的结果和/或变量。涉及多个不完整变量的分析不如具有单个不完整变量的分析那么简单。例如,在多变量错误的背景下,标准缺失数据假设(“完全随机缺失”,\“随机丢失\”[MAR],“不随机遗漏”)难以解释和评估。尚不清楚在实践中如何解决由于多变量错误而引起的复杂性。这项研究的目的是回顾如何在使用多重归因(MI)进行因果效应估计的观察性研究中管理和报告缺失的数据。特别关注缺失的数据摘要,缺失数据假设,主要和敏感性分析,和MI实施。
方法:我们搜索了5种顶级一般流行病学期刊的观察性研究,旨在回答因果研究问题,并使用MI,在2019年1月至2021年12月之间发布。系统进行了文章筛选和数据提取。
结果:本综述纳入的130项研究中,108(83%)通过排除特定变量中数据缺失的个体来得出分析样本(例如,结果)和114(88%)在分析样本中有多变量错误。44项(34%)研究提供了关于缺失数据假设的声明,其中35陈述了MAR假设,但只有11/44(25%)的研究为这些假设提供了理由。估算的数量,MI方法和MI软件报告普遍良好(71%,75%和88%的研究,分别),而在超过一半的研究中,归因模型规范的各个方面都不清楚。在69/130(53%)研究中进行了使用不同方法处理缺失数据的二次分析。在这69项研究中,68(99%)缺乏对二次分析的明确理由。
结论:需要努力澄清MI的基本原理,并改进MI的报告,以便从观察数据中估计因果效应。我们鼓励在制定和报告与缺失数据相关的分析决策时提高透明度。
BACKGROUND: Missing data are common in observational studies and often occur in several of the variables required when estimating a causal effect, i.e. the exposure, outcome and/or variables used to control for confounding. Analyses involving multiple incomplete variables are not as straightforward as analyses with a single incomplete variable. For example, in the context of multivariable missingness, the standard missing data assumptions (\"missing completely at random\", \"missing at random\" [MAR], \"missing not at random\") are difficult to interpret and assess. It is not clear how the complexities that arise due to multivariable missingness are being addressed in practice. The aim of this study was to review how missing data are managed and reported in observational studies that use multiple imputation (MI) for causal effect estimation, with a particular focus on missing data summaries, missing data assumptions, primary and sensitivity analyses, and MI implementation.
METHODS: We searched five top general epidemiology journals for observational studies that aimed to answer a causal research question and used MI, published between January 2019 and December 2021. Article screening and data extraction were performed systematically.
RESULTS: Of the 130 studies included in this review, 108 (83%) derived an analysis sample by excluding individuals with missing data in specific variables (e.g., outcome) and 114 (88%) had multivariable missingness within the analysis sample. Forty-four (34%) studies provided a statement about missing data assumptions, 35 of which stated the MAR assumption, but only 11/44 (25%) studies provided a justification for these assumptions. The number of imputations, MI method and MI software were generally well-reported (71%, 75% and 88% of studies, respectively), while aspects of the imputation model specification were not clear for more than half of the studies. A secondary analysis that used a different approach to handle the missing data was conducted in 69/130 (53%) studies. Of these 69 studies, 68 (99%) lacked a clear justification for the secondary analysis.
CONCLUSIONS: Effort is needed to clarify the rationale for and improve the reporting of MI for estimation of causal effects from observational data. We encourage greater transparency in making and reporting analytical decisions related to missing data.