On using the Cox proportional hazards model with missing covariates

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 & Ying (1993), when data are missing completely at random.