Cox Regression with Incomplete Covariate Measurements
Cox Regression with Incomplete Covariate Measurements
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DOI：
10.1080/01621459.1993.10476416
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发表时间：
1993
影响因子：
3.7
通讯作者：
D. Lin;Z. Ying
中科院分区：
文献类型：
--
作者：
D. Lin;Z. Ying
**摘要**：本文针对Cox回归模型下协变量数据缺失的问题提供了一种通用解法。回归参数向量的估计函数是对具有完整协变量测量值的偏似然得分函数的一种近似，在病例 - 队列设计的特殊情形下可简化为塞尔夫（Self）和普伦蒂斯（Prentice）的伪似然得分函数。由此得到的参数估计量是一致的且渐近正态，同时给出了其协方差矩阵的一个简单且一致的估计量。大量的模拟研究表明，大样本近似对于实际应用是足够的。所提出的方法往往比完全病例分析更有效，特别是对于失效不频繁的大型队列。对于病例 - 队列设计，新方法提供了一种方差 - 协方差估计量，它比现有的更容易计算，并且允许多个亚队列扩充以提高效率。取自……的真实数据
Abstract This article provides a general solution to the problem of missing covariate data under the Cox regression model. The estimating function for the vector of regression parameters is an approximation to the partial likelihood score function with full covariate measurements and reduces to the pseudolikelihood score function of Self and Prentice in the special setting of case-cohort designs. The resulting parameter estimator is consistent and asymptotically normal with a covariance matrix for which a simple and consistent estimator is provided. Extensive simulation studies show that the large-sample approximations are adequate for practical use. The proposed approach tends to be more efficient than the complete-case analysis, especially for large cohorts with infrequent failures. For case-cohort designs, the new methodology offers a variance-covariance estimator that is much easier to calculate than the existing ones and allows multiple subcohort augmentations to improve efficiency. Real data taken f...