A comparison of multiple imputation and doubly robust estimation for analyses with missing data
A comparison of multiple imputation and doubly robust estimation for analyses with missing data
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DOI：
10.1111/j.1467-985x.2006.00407.x
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发表时间：
2006-01-01
影响因子：
2
通讯作者：
Vansteelandt, Stijn
中科院分区：
文献类型：
--
作者：
Carpenter, James R.;Kenward, Michael G.;Vansteelandt, Stijn
多重推算现在是一种成熟的技术，用于分析某些单位观测不完整的数据集。如果推算模型是正确的，那么结果的估计是一致的。另一种方法是按单位上观察完整数据的逆概率进行加权，概念上很简单，涉及的建模假设较少，但众所周知，这种方法效率低(相对于完全参数方法)，而且对加权模型的选择敏感。在过去的十年里，有相当多的理论工作来改善逆概率加权的性能，导致了“双重稳健”或“双重保护”估计量的发展。我们直观地回顾了这些发展，并从理论和实践两个角度将这些估计量与多重归因进行了比较。
Multiple imputation is now a well-established technique for analysing data sets where some units have incomplete observations. Provided that the imputation model is correct, the resulting estimates are consistent. An alternative, weighting by the inverse probability of observing complete data on a unit, is conceptually simple and involves fewer modelling assumptions, but it is known to be both inefficient (relative to a fully parametric approach) and sensitive to the choice of weighting model. Over the last decade, there has been a considerable body of theoretical work to improve the performance of inverse probability weighting, leading to the development of 'doubly robust' or 'doubly protected' estimators. We present an intuitive review of these developments and contrast these estimators with multiple imputation from both a theoretical and a practical viewpoint.