Regularized win ratio regression for variable selection and risk prediction, with an application to a cardiovascular trial.

ABSTRACT:

Background

The win ratio has been widely used in the analysis of hierarchical composite endpoints, which prioritize critical outcomes such as mortality over nonfatal, secondary events. Although a regression framework exists to incorporate covariates, it is limited to low-dimensional datasets and may struggle with numerous predictors. This gap necessitates a robust variable selection method tailored to the win ratio framework.

Methods

We propose an elastic net-type regularization approach for win ratio regression, extending the proportional win-fractions (PW) model in low-dimensional settings. The method addresses key challenges, including adapting pairwise comparisons to penalized regression, optimizing model selection through subject-level cross-validation, and defining per

SUBMITTER: Mao L 

PROVIDER: S-EPMC12004665 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature