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Cross-validation approaches for penalized Cox regression
Journal article   Peer reviewed

Cross-validation approaches for penalized Cox regression

Biyue Dai and Patrick Breheny
Statistical methods in medical research, Vol.33(4), pp.702-715
04/2024
DOI: 10.1177/09622802241233770
PMCID: PMC13232653
PMID: 38445300

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Abstract

Cross-validation is the most common way of 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 a new approach based on cross-validating the linear predictors of the Cox model and compare it to approaches that have been proposed elsewhere. We show that the proposed approach offers an attractive balance of performance and numerical stability, and illustrate these advantages using simulated data as well as analyzing a high-dimensional study of gene expression and survival in lung cancer patients.
 model selection Survival analysis cross- validation high-dimensional Lasso

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