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

arXiv: https://arxiv.org/abs/2510.19374 · HTML: https://mlsanity.com/paper/2510.19374/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 31 references that are in the map.
- First posted on arXiv 22 Oct 2025.

## Builds on (most cited first, 2 of 2)

- [SurvSet: An open-source time-to-event dataset repository](https://mlsanity.com/paper/2203.03094/) (2022)
- [Testing and Confidence Intervals for High Dimensional Proportional Hazards Model](https://mlsanity.com/paper/1412.5158/) (2014)

## Abstract (arXiv)

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.