Reference-Based Multiple Imputation-What is the Right Variance and How to Estimate It
Reference-Based Multiple Imputation-What is the Right Variance and How to Estimate It
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DOI：
10.1080/19466315.2021.1983455
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发表时间：
2021-11-12
影响因子：
1.8
通讯作者：
Bartlett, Jonathan W.
中科院分区：
文献类型：
--
作者：
Bartlett, Jonathan W.
基于参考的多重插补方法已成为处理随机临床试验中缺失数据的流行方法。众所周知，鲁宾方差估计器与基于参考的插补估计器的真实重复采样（频率）方差相比存在偏差。令人惊讶的是，考虑到这些方法越来越受欢迎，文献中关于鲁宾方差估计器或针对重复采样方差的替代（较小）方差估计器是否更合适的争论相对较少。我们回顾了这场辩论双方的论点，并认为重复抽样方差更合适。我们回顾了估计频率方差的不同方法，并提出了最近提出的将引导法与多重插补相结合作为广泛适用的通用解决方案的建议。与此同时，鉴于基于参考的假设对频率方差的影响，我们认为有必要对这些方法进行进一步审查，以确定其假设的强度是否普遍合理。
Reference-based multiple imputation methods have become popular for handling missing data in randomized clinical trials. Rubin's variance estimator is well known to be biased compared to the reference-based imputation estimator's true repeated sampling (frequentist) variance. Somewhat surprisingly given the increasing popularity of these methods, there has been relatively little debate in the literature as to whether Rubin's variance estimator or alternative (smaller) variance estimators targeting the repeated sampling variance are more appropriate. We review the arguments made on both sides of this debate, and argue that the repeated sampling variance is more appropriate. We review different approaches for estimating the frequentist variance, and suggest a recent proposal for combining bootstrapping with multiple imputation as a widely applicable general solution. At the same time, in light of the consequences of reference-based assumptions for frequentist variance, we believe further scrutiny of these methods is warranted to determine whether the strength of their assumptions is generally justifiable.