Regression methods for gap time hazard functions of sequentially ordered multivariate failure time data

• 成果类型: Article
• 署名作者: Schaubel, DE; Cai, JW
• 署名单位: University of Michigan System; University of Michigan; University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/91.2.291
• 发表日期: 2004
• 页码: 291303
• 关键词: nonparametric-estimation; survival function; MODEL; transplantation; recipients; mortality; dialysis; events

摘要

Sequentially ordered multivariate failure time data are often observed in biomedical studies and inter-event, or gap, times are often of interest. Generally, standard hazard regression methods cannot be applied to the gap times because of identifiability issues and induced dependent censoring. We propose estimating equations for fitting proportional hazards regression models to the gap times. Model parameters are shown to be consistent and asymptotically normal. Simulation studies reveal the appropriateness of the asymptotic approximations in finite samples. The proposed methods are applied to renal failure data to assess the association between demographic covariates and both time until wait-listing and time from wait-listing to kidney transplantation.