Combining multiple imputation and inverse-probability weighting.
Combining multiple imputation and inverse-probability weighting.
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DOI：
10.1111/j.1541-0420.2011.01666.x
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发表时间：
2012-03
期刊：
影响因子：
1.9
通讯作者：
Li L
中科院分区：
文献类型：
--
作者：
Seaman SR;White IR;Copas AJ;Li L
处理缺失数据的两种常用方法是多重插补(MI)和逆概率加权(IPW)。IPW还用于调整不相等的抽样分数。MI通常比IPW更有效，但更复杂。虽然IPW只需要一个关于个人拥有完整数据(单变量结果)的概率的模型，而MI需要一个关于给定观察数据的缺失数据(多变量结果)的联合分布的模型。如果大量数据缺失，任何一种模型的不足都可能导致重要的偏差。第三种方法结合了MI和IPW，给出了一个双重稳健的估计器。第四种方法(IPW/MI)结合了MI和IPW，但与双稳健方法不同，该方法仅估算孤立的缺失值，并使用权重来考虑剩余的较大的未归因性缺失数据块，例如，在经历样本损耗的队列研究中和/或不相等的抽样分数。在本文中，我们检验了IPW/MI相对于MI和IPW单独的性能，并调查了Rubin的规则方差估计器是否对IPW/MI有效。我们证明了Rubin的规则方差估计对于具有估计结果的线性回归的IPW/MI是有效的，我们给出了在更一般的设置下支持这种方差估计的模拟，并且我们证明了IPW/MI比备选方案更有优势。IPW/MI适用于国家儿童发展研究的数据。
Two approaches commonly used to deal with missing data are multiple imputation (MI) and inverse-probability weighting (IPW). IPW is also used to adjust for unequal sampling fractions. MI is generally more efficient than IPW but more complex. Whereas IPW requires only a model for the probability that an individual has complete data (a univariate outcome), MI needs a model for the joint distribution of the missing data (a multivariate outcome) given the observed data. Inadequacies in either model may lead to important bias if large amounts of data are missing. A third approach combines MI and IPW to give a doubly robust estimator. A fourth approach (IPW/MI) combines MI and IPW but, unlike doubly robust methods, imputes only isolated missing values and uses weights to account for remaining larger blocks of unimputed missing data, such as would arise, e.g., in a cohort study subject to sample attrition, and/or unequal sampling fractions. In this article, we examine the performance, in terms of bias and efficiency, of IPW/MI relative to MI and IPW alone and investigate whether the Rubin’s rules variance estimator is valid for IPW/MI. We prove that the Rubin’s rules variance estimator is valid for IPW/MI for linear regression with an imputed outcome, we present simulations supporting the use of this variance estimator in more general settings, and we demonstrate that IPW/MI can have advantages over alternatives. IPW/MI is applied to data from the National Child Development Study.
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