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Feature screening is an important tool in analyzing ultrahigh-dimensionaldata, particularly in the field of Omics and oncology studies. However, mostattention has been focused on identifying features that have a linear ormonotonic impact on the response variable. Detecting a sparse set of variablesthat have a nonlinear or non-monotonic relationship with the response variableis still a challenging task. To fill the gap, this paper proposed a robustmodel-free screening approach for right-censored survival data by providing anew perspective of quantifying the covariate effect on the restricted meansurvival time, rather than the routinely used hazard function. The proposedmeasure, based on the difference between the restricted mean survival time ofcovariate-stratified and overall data, is able to identify comprehensive typesof associations including linear, nonlinear, non-monotone, and even localdependencies like change points. This approach is highly interpretable andflexible without any distribution assumption. The sure screening property isestablished and an iterative screening procedure is developed to addressmulticollinearity between high-dimensional covariates. Simulation studies arecarried out to demonstrate the superiority of the proposed method in selectingimportant features with a complex association with the response variable. Thepotential of applying the proposed method to handle interval-censored failuretime data has also been explored in simulations, and the results have beenpromising. The method is applied to a breast cancer dataset to identifypotential prognostic factors, which reveals potential associations betweenbreast cancer and lymphoma.

High-dimensional Feature Screening for Nonlinear Associations With Survival Outcome Using Restricted Mean Survival Time