A Note on Listwise Deletion versus Multiple Imputation
A Note on Listwise Deletion versus Multiple Imputation
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DOI：
10.1017/pan.2018.18
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发表时间：
2018-10-01
影响因子：
5.4
通讯作者：
Pepinsky, Thomas B.
中科院分区：
文献类型：
--
作者：
Pepinsky, Thomas B.
这封信使用模拟方法比较了多重插补和列表删除的性能。重点是“非随机缺失”（MNAR）的数据，在这种情况下，多重插补和列表删除都是有偏倚的。在这些模拟中，当数据是MNAR时，多重插补产生的结果通常比列表删除更有偏差，效率更低，覆盖率更差。即使完全观测变量和缺失值变量之间存在非常强的相关性，数据也几乎是“随机缺失”。“当真实的数据生成过程未知时，这些结果建议在比较多重插补和列表删除的结果时要谨慎。
This letter compares the performance of multiple imputation and listwise deletion using a simulation approach. The focus is on data that are "missing not at random" (MNAR), in which case both multiple imputation and listwise deletion are known to be biased. In these simulations, multiple imputation yields results that are frequently more biased, less efficient, and with worse coverage than listwise deletion when data are MNAR. This is the case even with very strong correlations between fully observed variables and variables with missing values, such that the data are very nearly "missing at random." These results recommend caution when comparing the results from multiple imputation and listwise deletion, when the true data generating process is unknown.