Cox Regression with Incomplete Covariate Measurements
针对Cox回归模型中协变量数据缺失问题，提出一种通用估计方法，其估计量一致且渐近正态，比完整病例分析更有效，尤其适用于大样本且失效事件稀疏的队列研究。
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 from clinical and epidemiologic studies are analyzed.