α $$ \alpha $$ -KIDS: A Novel Feature Evaluation in the Ultrahigh-Dimensional Right-Censored Setting, With...

Recent advances in sequencing technologies have allowed the collection of massive genome-wide information that substantially enhances the diagnosis and prognosis of head and neck cancer. Identifying predictive markers for survival time is crucial for devising prognostic systems and learning the underlying molecular drivers of the cancer course. In this paper, we introduce $\alpha$ -KIDS, a model-free feature screening procedure with false discovery rate (FDR) control for ultrahigh-dimensional right-censored data, which is robust against unknown censoring mechanisms. Specifically, our two-stage procedure initially selects a set of important features with a dual screening mechanism using nonparametric reproducing-kernel-based ANOVA statistics, followed by identifying a refined set of features under FDR control through a unified knockoff procedure. The finite sample properties of our method and its novelty (in light of existing alternatives) are evaluated via simulation studies. Furthermore, we illustrate our methodology via application to a motivating right-censored head and neck (HN) cancer survival data derived from The Cancer Genome Atlas, with further validation on a similar HN cancer data from the Gene Expression Omnibus database. The methodology can be implemented using the R package aKIDS, which is available on GitHub.