# Statistical inference after variable selection in Cox models: A simulation study

arXiv: https://arxiv.org/abs/2602.07477 · HTML: https://mlsanity.com/paper/2602.07477/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 43 references that are in the map.
- First posted on arXiv 7 Feb 2026.

## Builds on (most cited first, 6 of 6)

- [Exact post-selection inference, with application to the lasso](https://mlsanity.com/paper/1311.6238/) (2013)
- [Optimal Inference After Model Selection](https://mlsanity.com/paper/1410.2597/) (2014)
- [Using simulation studies to evaluate statistical methods](https://mlsanity.com/paper/1712.03198/) (2017)
- [Confidence intervals for high-dimensional Cox models](https://mlsanity.com/paper/1803.01150/) (2018)
- [Statistical Inference for Cox Proportional Hazards Models with a Diverging Number of Covariates](https://mlsanity.com/paper/2106.03244/) (2021)
- [Evaluating methods for Lasso selective inference in biomedical research by a comparative simulation study](https://mlsanity.com/paper/2005.07484/) (2020)

## Abstract (arXiv)

Choosing relevant predictors is central to the analysis of biomedical time-to-event data. Classical frequentist inference, however, presumes that the set of covariates is fixed in advance and does not account for data-driven variable selection. As a consequence, naive post-selection inference may be biased and misleading. In right-censored survival settings, these issues may be further exacerbated by the additional uncertainty induced by censoring. We investigate several inference procedures applied after variable selection for the coefficients of the Lasso and its extension, the adaptive Lasso, in the context of the Cox model. The methods considered include sample splitting, exact post-selection inference, and the debiased Lasso. Their performance is examined in a neutral simulation study reflecting realistic covariate structures and censoring rates commonly encountered in biomedical applications. To complement the simulation results, we illustrate the practical behavior of these procedures in an applied example using a publicly available survival dataset.