Conditional Screening for Ultra-high Dimensional Covariates with Survival Outcomes

TL;DR

This paper introduces a conditional screening method for high-dimensional survival data that leverages prior biological knowledge to improve biomarker selection, demonstrating strong theoretical and empirical performance.

Contribution

The paper presents a novel conditional screening approach that incorporates prior information, enhancing detection of important biomarkers in ultra-high dimensional survival analysis.

Findings

Method has sure screening property.

Method achieves low false selection rate.

Validated with simulations and DLBCL data.

Abstract

Identifying important biomarkers that are predictive for cancer patients' prognosis is key in gaining better insights into the biological influences on the disease and has become a critical component of precision medicine. The emergence of large-scale biomedical survival studies, which typically involve excessive number of biomarkers, has brought high demand in designing efficient screening tools for selecting predictive biomarkers. The vast amount of biomarkers defies any existing variable selection methods via regularization. The recently developed variable screening methods, though powerful in many practical setting, fail to incorporate prior information on the importance of each biomarker and are less powerful in detecting marginally weak while jointly important signals. We propose a new conditional screening method for survival outcome data by computing the marginal contribution of each biomarker given priorly known biological information. This is based on the premise that some biomarkers are known to be associated with disease outcomes a priori. Our method possesses sure screening properties and a vanishing false selection rate. The utility of the proposal is further confirmed with extensive simulation studies and analysis of a Diffuse large B-cell lymphoma (DLBCL) dataset.