Regression analysis of current status data with auxiliary covariates and informative observation times

This paper discusses regression analysis of current status failure time data with information observations and continuous auxiliary covariates. Under the additive hazards model, we employ a frailty model to describe the relationship between the failure time of interest and censoring time through some latent variables and propose an estimated partial likelihood estimator of regression parameters that makes use of the available auxiliary information. Asymptotic properties of the resulting estimators are established. To assess the finite sample performance of the proposed method, an extensive simulation study is conducted, and the results indicate that the proposed method works well. An illustrative example is also provided.