Cox regression analysis for distorted covariates with an unknown distortion function

We study inference for censored survival data where some covariates are distorted by some unknown functions of an observable confounding variable in a multiplicative form. An example of this kind of data in medical studies is normalizing some important observed exposure variables by patients' body mass index , weight, or age. Such a phenomenon also appears frequently in environmental studies where an ambient measure is used for normalization and in genomic studies where the library size needs to be normalized for the next generation sequencing of data. We propose a new covariate-adjusted Cox proportional hazards regression model and utilize the kernel smoothing method to estimate the distorting function, then employ an estimated maximum likelihood method to derive the estimator for the regression parameters. We establish the large sample properties of the proposed estimator. Extensive simulation studies demonstrate that the proposed estimator performs well in correcting the bias arising from distortion. A real dataset from the National Wilms' Tumor Study is used to illustrate the proposed approach.