Kernel Meets Sieve: Transformed Hazards Models with Sparse Longitudinal Covariates

• 成果类型: Article; Early Access
• 署名作者: Sun, Dayu; Sun, Zhuowei; Zhao, Xingqiu; Cao, Hongyuan
• 署名单位: Indiana University System; Indiana University Bloomington; Jilin University; Dalian Medical University; Hong Kong Polytechnic University; State University System of Florida; Florida State University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2025.2476781
• 发表日期: 2025
• 关键词: regression-analysis; cox model; cauchy

摘要

We study the transformed hazards model with time-dependent covariates observed intermittently for the censored outcome. Existing work assumes the availability of the whole trajectory of the time-dependent covariates, which is unrealistic. We propose combining kernel-weighted log-likelihood and sieve maximum log-likelihood estimation to conduct statistical inference. The method is robust and easy to implement. We establish the asymptotic properties of the proposed estimator and contribute to a rigorous theoretical framework for general kernel-weighted sieve M-estimators. Numerical studies corroborate our theoretical results and show that the proposed method performs favorably over competing methods. The analysis of a dataset from a COVID-19 study in Wuhan identifies clinical predictors that otherwise cannot be obtained using existing methods. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.