Flexible survival regression with variable selection for heterogeneous population

TL;DR

This paper introduces a flexible survival regression method that simultaneously identifies latent sub-groups in heterogeneous populations and selects significant covariates within each group, improving prediction accuracy for time-to-event data.

Contribution

It proposes a novel approach that combines latent group detection with covariate selection in survival analysis, addressing heterogeneity and high-dimensional covariates.

Findings

Enhanced predictive accuracy for survival outcomes.

Effective identification of latent sub-groups.

Improved covariate significance evaluation within groups.

Abstract

Survival regression is widely used to model time-to-events data, to explore how covariates may influence the occurrence of events. Modern datasets often encompass a vast number of covariates across many subjects, with only a subset of the covariates significantly affecting survival. Additionally, subjects often belong to an unknown number of latent groups, where covariate effects on survival differ significantly across groups. The proposed methodology addresses both challenges by simultaneously identifying the latent sub-groups in the heterogeneous population and evaluating covariate significance within each sub-group. This approach is shown to enhance the predictive accuracy for time-to-event outcomes, via uncovering varying risk profiles within the underlying heterogeneous population and is thereby helpful to device targeted disease management strategies.