Propensity score estimation with missing values using a multiple imputation missingness pattern (MIMP) approach
Propensity score estimation with missing values using a multiple imputation missingness pattern (MIMP) approach
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DOI：
10.1002/sim.3549
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发表时间：
2009-04-30
影响因子：
2
通讯作者：
Lipkovich, Ilya
中科院分区：
文献类型：
--
作者：
Qu, Yongming;Lipkovich, Ilya
在非随机研究中，倾向性评分被广泛用作减少偏倚的方法来评估治疗效果。由于在估计倾向性分数的模型中通常包括许多协变量，因此至少有一个协变量缺失的受试者的比例可能很大。虽然已经提出了许多在存在缺失协变量的情况下基于倾向得分的估计方法，但很少有人发表对这些方法的性能进行比较的文章。在本文中，我们提出了一种称为多重填补缺失模式(MIMP)的新方法，并在不同的缺失数据机制和协变量之间的相关程度下与朴素估计器(忽略倾向分数)和基于倾向分数估计中常用的三种缺失协变量处理方法(分别估计每个缺失数据模式内的倾向分数、多次填补和丢弃缺失数据)进行了比较。仿真结果表明，所有调整后的估计量都比朴素估计量的偏差小得多。在某些条件下，MIMP与现有的替代方案相比具有更小的偏差和均方误差。版权所有(C)2009 John Wiley&Sons，Ltd.
Propensity scores have been used widely as a bias reduction method to estimate the treatment effect in nonrandomized studies. Since many covariates are generally included in the model for estimating the propensity scores, the proportion of subjects with at least one missing covariate could be large. While many methods have been proposed for propensity score-based estimation in the presence of missing covariates, little has been published comparing the performance of these methods. In this article we propose a novel method called multiple imputation missingness pattern (MIMP) and compare it with the naive estimator (ignoring propensity score) and three commonly used methods of handling missing covariates in propensity score-based estimation (separate estimation of propensity scores within each pattern of missing data, multiple imputation and discarding missing data) under different mechanisms of missing data and degree of correlation among covariates. Simulation shows that all adjusted estimators are much less biased than the naive estimator. Under certain conditions MIMP provides benefits (smaller bias and mean-squared error) compared with existing alternatives. Copyright (C) 2009 John Wiley & Sons, Ltd.