Confidence intervals for high-dimensional Cox models

Understand

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.