A unified approach to variable selection for Cox’s proportional hazards model with interval-censored failure time data

Abstract

Cox’s proportional hazards model is the most commonly used model for regression analysis of failure time data and some methods have been developed for its variable selection under different situations. In this paper, we consider a general type of failure time data, case K interval-censored data, that include all of other types discussed as special cases, and propose a unified penalized variable selection procedure. In addition to its generality, another significant feature of the proposed approach is that unlike all of the existing variable selection methods for failure time data, the proposed approach allows dependent censoring, which can occur quite often and could lead to biased or misleading conclusions if not taken into account. For the implementation, a coordinate descent algorithm is developed and the oracle property of the proposed method is established. The numerical studies indicate that the proposed approach works well for practical situations and it is applied to a set of real data arising from Alzheimer’s Disease Neuroimaging Initiative study that motivated this study.

Analysis of Length-Biased and Partly Interval-Censored Survival Data with Mismeasured Covariates

“…(2017). For the interval‐censored survival data without length‐biased sampling, existing regularization methods have been applied for the Cox model (e.g., Du et al., 2021; Zhao et al., 2020), the transformation model (e.g., Scolas et al., 2016), and the conditional cumulative hazard function (e.g., Sun et al. 2021).…”

Variable Selection for Nonlinear Cox Regression Model via Deep Learning

“…More recently, the authors in Fan et al (2010) extended the iterative sure feature screening procedure to Cox proportional hazards model. The works in Du et al (2021); Yi et al (2020); Zhao et al (2019) considered the variable selection problem for interval-censored data. However, these methods rely on strong parametric assumptions that are often violated in practice.…”