A study of regression calibration in a partially observed stratified Cox model

Regression calibration is a simple method for estimating regression models when covariate data are missing for some study subjects. It consists in replacing an unobserved covariate by an estimator of its conditional expectation given available covariates. Regression calibration has recently been investigated in various regression models such as the linear, generalized linear, and proportional hazards models. The aim of this paper is to investigate the appropriateness of this method for estimating the stratified Cox regression model with missing values of the covariate defining the strata. Despite its practical relevance, this problem has not yet been discussed in the literature. Asymptotic distribution theory is developed for the regression calibration estimator in this setting. A simulation study is also conducted to investigate the properties of this estimator.