The proportion of missing data should not be used to guide decisions on multiple imputation
The proportion of missing data should not be used to guide decisions on multiple imputation
复制标题
DOI：
10.1016/j.jclinepi.2019.02.016
复制
发表时间：
2019-06-01
影响因子：
7.2
通讯作者：
Heron, Jon
中科院分区：
文献类型：
--
作者：
Madley-Dowd, Paul;Hughes, Rachael;Heron, Jon
目的：研究人员担心，当大部分数据缺失时，是否应该使用多重插补（MI）或完整的病例分析。我们的目的是提供指导，从数据中得出的结论与大比例的missingness.Study设计和设置：通过模拟，我们研究了如何缺失数据的比例，缺失信息的分数（FMI），和辅助变量的可用性影响MI性能。结果数据完全随机缺失或随机缺失（MAR）。结果：提供足够的辅助信息; MI在偏倚方面是有益的，在效率方面从未有害。具有相似FMI值但缺失数据比例不同的模型对效应估计值也具有相似的精度。在没有偏见的情况下，FMI是一个更好的指导使用MI比缺失data.Conclusion的比例的效率收益：我们提供的证据表明，MAR数据，有效MI减少偏见，即使当比例的缺失是大的。我们建议研究人员使用FMI来指导插补分析中辅助变量的选择以获得效率增益，并且如果完整病例数较少，则可能需要包括不同插补模型的敏感性分析。(C)2019年，任作家。爱思唯尔公司出版
Objectives: Researchers are concerned whether multiple imputation (MI) or complete case analysis should be used when a large proportion of data are missing. We aimed to provide guidance for drawing conclusions from data with a large proportion of missingness.Study Design and Setting: Via simulations, we investigated how the proportion of missing data, the fraction of missing information (FMI), and availability of auxiliary variables affected MI performance. Outcome data were missing completely at random or missing at random (MAR).Results: Provided sufficient auxiliary information was available; MI was beneficial in terms of bias and never detrimental in terms of efficiency. Models with similar FMI values, but differing proportions of missing data, also had similar precision for effect estimates. In the absence of bias, the FMI was a better guide to the efficiency gains using MI than the proportion of missing data.Conclusion: We provide evidence that for MAR data, valid MI reduces bias even when the proportion of missingness is large. We advise researchers to use FMI to guide choice of auxiliary variables for efficiency gain in imputation analyses, and that sensitivity analyses including different imputation models may be needed if the number of complete cases is small. (C) 2019 The Authors. Published by Elsevier Inc.