High-Dimensional Variable Selection With Competing Events Using Cooperative Penalized Regression.

ABSTRACT: Variable selection is an important step in the analysis of high-dimensional data, yet there are limited options for survival outcomes in the presence of competing risks. Commonly employed penalized Cox regression considers each event type separately through cause-specific models, neglecting possibly shared information between them. We adapt the feature-weighted elastic net (fwelnet), an elastic net generalization, to survival outcomes and competing risks. For two causes, our proposed algorithm fits two alternating cause-specific models, where each model receives the coefficient vector of the complementary model as prior information. We dub this "cooperative penalized regression," as it enables the modeling of competing risk data with cause-specific models while accounting for shared effect

SUBMITTER: Burk L 

PROVIDER: S-EPMC11865700 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature