Cox Regression with Incomplete Covariate Measurements

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.