  Feature screening is an important tool in analyzing ultrahigh-dimensional
data, particularly in the field of Omics and oncology studies. However, most
attention has been focused on identifying features that have a linear or
monotonic impact on the response variable. Detecting a sparse set of variables
that have a nonlinear or non-monotonic relationship with the response variable
is still a challenging task. To fill the gap, this paper proposed a robust
model-free screening approach for right-censored survival data by providing a
new perspective of quantifying the covariate effect on the restricted mean
survival time, rather than the routinely used hazard function. The proposed
measure, based on the difference between the restricted mean survival time of
covariate-stratified and overall data, is able to identify comprehensive types
of associations including linear, nonlinear, non-monotone, and even local
dependencies like change points. This approach is highly interpretable and
flexible without any distribution assumption. The sure screening property is
established and an iterative screening procedure is developed to address
multicollinearity between high-dimensional covariates. Simulation studies are
carried out to demonstrate the superiority of the proposed method in selecting
important features with a complex association with the response variable. The
potential of applying the proposed method to handle interval-censored failure
time data has also been explored in simulations, and the results have been
promising. The method is applied to a breast cancer dataset to identify
potential prognostic factors, which reveals potential associations between
breast cancer and lymphoma.
