Checking the Cox Proportional Hazards Model with Interval-Censored Data

• 成果类型: Article; Early Access
• 署名作者: Xu, Yangjianchen; Zeng, Donglin; Lin, D. Y.
• 署名单位: University of Waterloo; University of Michigan System; University of Michigan; University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2025.2520460
• 发表日期: 2025
• 关键词: maximum-likelihood-estimation; regression-models; RESIDUALS; tests; RISK

摘要

This article presents a general framework for checking the adequacy of the Cox proportional hazards model with interval-censored data, which arise when the event of interest is known only to occur over a random time interval. Specifically, we construct certain stochastic processes that are informative about various aspects of the model, that is, the functional forms of covariates, the exponential link function and the proportional hazards assumption. We establish their weak convergence to zero-mean Gaussian processes under the assumed model through empirical process theory. We then approximate the limiting distributions by Monte Carlo simulation and develop graphical and numerical procedures to check model assumptions and improve goodness of fit. We evaluate the performance of the proposed methods through extensive simulation studies and provide an application to the Atherosclerosis Risk in Communities Study. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.