Cross validation approaches for penalized Cox regression

TL;DR

This paper introduces two novel cross-validation methods for penalized Cox regression, demonstrating their effectiveness and stability through simulations and a lung cancer survival study.

Contribution

The paper proposes new cross-validation approaches tailored for penalized Cox models, addressing stability and performance issues in high-dimensional survival analysis.

Findings

Cross-validating linear predictors offers a good balance of performance and stability.

Proposed methods outperform existing approaches in simulations.

Application to lung cancer data illustrates practical advantages.

Abstract

Cross validation is commonly used for selecting tuning parameters in penalized regression, but its use in penalized Cox regression models has received relatively little attention in the literature. Due to its partial likelihood construction, carrying out cross validation for Cox models is not straightforward, and there are several potential approaches for implementation. Here, we propose two new cross-validation methods for Cox regression and compare them to approaches that have been proposed elsewhere. Our proposed approach of cross-validating the linear predictors seems to offer an attractive balance of performance and numerical stability. We illustrate these advantages using simulated data as well as using them to analyze data from a high-dimensional study of survival in lung cancer patients.