多重估算缺缺数据指标 (Multiple imputation with missing data indicators)

Multiple imputation is a well-established general technique for analyzing data with missing values. A convenient way to implement multiple imputation is sequential regression multiple imputation (SRMI), also called chained equations multiple imputation. In this approach, we impute missing values using regression models for each variable, conditional on the other variables in the data. This approach, however, assumes that the missingness mechanism is missing at random, and it is not well-justified under not-at-random missingness without additional modification. In this paper, we describe how we can generalize the SRMI imputation procedure to handle not-at-random missingness (MNAR) in the setting where missingness may depend on other variables that are also missing. We provide algebraic justification for several generalizations of standard SRMI using Taylor series and other approximations of the target imputation distribution under MNAR. Resulting regression model approximations include indicators for missingness, interactions, or other functions of the MNAR missingness model and observed data. In a simulation study, we demonstrate that the proposed SRMI modifications result in reduced bias in the final analysis compared to standard SRMI, with an approximation strategy involving inclusion of an offset in the imputation model performing the best overall. The method is illustrated in a breast cancer study, where the goal is to estimate the prevalence of a specific genetic pathogenic variant.

翻译：多重估算是分析缺值数据的一种公认的通用方法。在本文中,我们描述一个方便的方法,即实施多重估算的方法是连续回归多重估算(SRMI),也称为链式等式多重估算。在这个方法中,我们使用每个变量的回归模型对缺值进行估算,但以数据中的其他变量为条件。但这一方法假定缺损机制是随机缺失的,在没有额外修改的情况下,在非随机缺失的情况下,这种机制是不合理的。在非随机缺失情况下,我们描述如何在缺损可能取决于同样缺失的其他变量的设置中,普遍推广SRMI估算程序,以处理非随机缺失(MI)问题。我们用泰勒系列和其他近似值来计算每个变量的缺损率。因此,回归模型的近似值包括MNAR缺失模型的缺失、互动或其他功能,以及观察到的数据。在模拟研究中,拟议对SRMI进行的非随机缺损(MNAR)计算程序(MNAR) (MNAR) (MNAR) (M) (MNAR) (MN) (MNAR) (M) (M) (M) (M) (M) (M) (MNAR) (M) (M) (M) (M) (M) (M) (M) (M) (M) (MI (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (的(M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M)