Feature screening via concordance indices for left-truncated and right-censored survival data

Abstract

Ultrahigh-dimensional data analysis has been a popular topic in decades. In the framework of ultrahigh-dimensional setting, feature screening methods are key techniques to retain informative covariates and screen out non-informative ones when the dimension of covariates is extremely larger than the sample size. In the presence of incomplete data caused by censoring, several valid methods have also been developed to deal with ultrahigh-dimensional covariates for time-to-event data. However, little approach is available to handle feature screening for survival data subject to biased sample, which is usually induced by left-truncation. In this paper, we extend the C-index estimation proposed by Hartman et al. (2023) to develop a valid feature screening procedure to deal with left-truncated and right-censored survival data subject to ultrahigh-dimensional covariates. The sure screening property is also rigorously established to justify the proposed method. Numerical results also verify the validity of the proposed procedure.