On using the Cox proportional hazards model with missing covariates
On using the Cox proportional hazards model with missing covariates
复制标题
DOI：
10.1093/biomet/84.3.579
复制
发表时间：
1997-09-01
期刊：
影响因子：
2.7
通讯作者：
Tsai, WY
中科院分区：
文献类型：
--
作者：
Paik, MC;Tsai, WY
我们提出了两种使用 Cox 比例风险模型处理缺失协变量的方法。仅基于具有完整协变量的研究对象的最大部分似然估计量并未利用所有可用信息。当缺失的概率取决于失败或审查时间时，它也会有偏差。我们的建议是在给定可用信息的情况下，估算涉及缺失协变量的统计量的条件期望。当缺失概率取决于失败或审查时间以及观察到的协变量时，所提出的方法提供了一致的回归参数估计器。当数据完全随机丢失时，所提出的估计器比林格英（1993）先前建议的估计器更有效。
We propose two methods for handling missing covariates in using the Cox proportional hazards model. The maximum partial likelihood estimator based only on study subjects having complete covariates does not utilise all available information. Also it is biased when the probability of missingness depends on the failure or censoring time. Our suggestion is to impute the conditional expectation of the statistic involving missing covariates given the available information. The proposed method provides a consistent regression parameter estimator when the probability of missingness depends on the failure or censoring time as well as on the observed covariates. Also the proposed estimator is more efficient than the estimator suggested previously by Lin ge Ying (1993), when data are missing completely at random.