# Confidence intervals for high-dimensional Cox models

arXiv: https://arxiv.org/abs/1803.01150 · HTML: https://mlsanity.com/paper/1803.01150/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2018.

## Built on by (most cited first, 3 of 3)

- [Directional FDR Control for Sub-Gaussian Sparse GLMs](https://mlsanity.com/paper/2105.00393/) (2021)
- [Communication-Efficient Distributed Estimation and Inference for Cox's Model](https://mlsanity.com/paper/2302.12111/) (2023)
- [Statistical inference after variable selection in Cox models: A simulation study](https://mlsanity.com/paper/2602.07477/) (2026)

## Abstract (arXiv)

The purpose of this paper is to construct confidence intervals for the regression coefficients in high-dimensional Cox proportional hazards regression models where the number of covariates may be larger than the sample size. Our debiased estimator construction is similar to those in Zhang and Zhang (2014) and van de Geer et al. (2014), but the time-dependent covariates and censored risk sets introduce considerable additional challenges. Our theoretical results, which provide conditions under which our confidence intervals are asymptotically valid, are supported by extensive numerical experiments.