Augmented inverse probability weighted estimator for Cox missing covariate regression
Augmented inverse probability weighted estimator for Cox missing covariate regression
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DOI：
10.1111/j.0006-341x.2001.00414.x
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发表时间：
2001-06-01
期刊：
影响因子：
1.9
通讯作者：
Chen, HY
中科院分区：
文献类型：
--
作者：
Wang, CY;Chen, HY
本文研究协变量不完全时考克斯回归参数估计的增广逆选择概率加权估计。该估计量扩展了Horvitz和Thompson（1952，Journal of the American Statistical Association 47，663-685）加权估计量。这个估计是双重稳健的，因为只要选择概率模型或协变量的联合分布被正确指定，它就是一致的。估计方程的增广项取决于基线累积风险和可通过EM型算法实现的条件分布。通过仿真研究，这种方法与以前提出的一些估计。将该方法应用于一个真实的算例。
This article investigates an augmented inverse selection probability weighted estimator for Cox regression parameter estimation when covariate variables are incomplete. This estimator extends the Horvitz and Thompson (1952, Journal of the American Statistical Association 47, 663-685) weighted estimator. This estimator is doubly robust because it is consistent as long as either the selection probability model or the joint distribution of covariates is correctly specified. The augmentation term of the estimating equation depends on the baseline cumulative hazard and on a conditional distribution that can be implemented by using an EM-type algorithm. This method is compared with some previously proposed estimators via simulation studies. The method is applied to a real example.