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Regularized win ratio regression for variable selection and risk prediction, with an application to a cardiovascular trial
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Regularized win ratio regression for variable selection and risk prediction, with an application to a cardiovascular trial
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Journal Article
Regularized win ratio regression for variable selection and risk prediction, with an application to a cardiovascular trial
2025
Overview
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 performance metrics via a generalized concordance index. The procedures are implemented in the
wrnet
R-package, publicly available at
https://lmaowisc.github.io/wrnet/
.
Results
Simulation studies demonstrate that
wrnet
outperforms traditional (regularized) Cox regression for time-to-first-event analysis, particularly in scenarios with differing covariate effects on mortality and nonfatal events. When applied to data from the HF-ACTION trial, the method identified prognostic variables and achieved superior predictive accuracy compared to regularized Cox models, as measured by overall and component-specific concordance indices.
Conclusion
The
wrnet
approach combines the interpretability and clinical relevance of the win ratio with the scalability and robustness of elastic net regularization. The accompanying R-package provides a user-friendly interface for routine application of the procedures, whenever appropriate. Future research could explore additional applications or refine the methodology to address non-proportionalities in win-loss risks and nonlinearities in covariate effects.
Publisher
BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
