High-dimensional feature selection in competing risks modeling: A stable approach using a split-and-merge ensemble algorithm.

ABSTRACT: Variable selection is critical in competing risks regression with high-dimensional data. Although penalized variable selection methods and other machine learning-based approaches have been developed, many of these methods often suffer from instability in practice. This paper proposes a novel method named Random Approximate Elastic Net (RAEN). Under the proportional subdistribution hazards model, RAEN provides a stable and generalizable solution to the large-p-small-n variable selection problem for competing risks data. Our general framework allows the proposed algorithm to be applicable to other time-to-event regression models, including competing risks quantile regression and accelerated failure time models. We show that variable selection and parameter estimation improved markedly using

SUBMITTER: Sun H 

PROVIDER: S-EPMC10087963 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature