Partially Linear Additive Hazards Regression With Varying Coefficients

• 成果类型: Article
• 署名作者: Yin, Guosheng; Li, Hui; Zeng, Donglin
• 署名单位: University of Texas System; UTMD Anderson Cancer Center; Beijing Normal University; 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: 10.1198/016214508000000463
• 发表日期: 2008
• 页码: 1200-1213
• 关键词: time-dependent coefficients; local partial-likelihood; efficient estimation; survival analysis; models; intensity

摘要

To explore the nonlinear interactions between some covariates and an exposure variable, we propose the partially linear additive hazards model for survival data. In a semiparametric setting, we construct a local pseudoscore function to estimate the varying and constant coefficients and establish the asymptotic normality of the proposed estimators. Moreover, we develop the weak convergence property for the local estimator of the baseline cumulative hazard function. We conduct simulation studies to empirically examine the finite-sample performance of the proposed methods and use real data from a breast cancer study for illustration.