A Two-Stage Feature Screening Framework for Ultrahigh-Dimensional Survival Data

Identifying important features associated with right-censored survival time in ultrahigh-dimensional survival data is a challenging task due to the curse of dimensionality and information loss caused by censoring. To address these challenges, we propose a two-stage feature screening framework consisting of an imputation step and a feature screening step. The use of Buckley–James imputation leverages information from censoring and can therefore enhance the overall screening performance, particularly when the censoring rate is relatively high. We establish the sure screening properties of the two screening procedures proposed under this framework and illustrate their advantages through simulations. A real-world example is also provided to demonstrate the practical usefulness of the proposed approach.