Survival of the fittest Cox model: Pivotal variable selection for time-to-event data

2025/10/22 by Maxime van Cutsem, van Cutsem, Maxime, Sylvain Sardy +1
Engineering · Mathematics · Computer Science · #Sparse and Compressive Sensing Techniques #Statistical Methods and Inference #Neural Networks and Applications

paper · pdf · doi:10.48550/arxiv.2510.19374

Abstract

We revisit Cox's proportional hazards model to improve variable selection in survival analysis. A square-root transformation of the partial likelihood renders the selection of the regularization parameter pivotal, free of the unknown baseline hazard and censoring mechanism. The resulting criterion borrows from information criteria such as BIC and from penalized regression methods such as the lasso, taking the best of both. On simulated and real data, our method substantially improves upon state-of-the-art approaches used daily in support recovery.

Citations

• A pivotal transform for the high-dimensional location-scale model
• SurvSet: An open-source time-to-event dataset repository
• Testing and Confidence Intervals for High Dimensional Proportional Hazards Model
• Comparison and anti-concentration bounds for maxima of Gaussian random vectors
• Nearly unbiased variable selection under minimax concave penalty
• Decoding by Linear Programming
• Ideal spatial adaptation by wavelet shrinkage
• Estimating the Dimension of a Model

Related

• Modified Cox regression with current status data
• Stable variable selection for right censored data: comparison of methods
• Should univariate Cox regression be used for feature selection with respect to time-to-event outcomes?
• pcoxtime: Penalized Cox Proportional Hazard Model for Time-dependent Covariates
• Beyond Cox Models: Assessing the Performance of Machine-Learning Methods in Non-Proportional Hazards and Non-Linear Survival Analysis