Accelerated failure time models for counting processes

被引:148
作者
Lin, DY
Wei, LJ
Ying, ZL
机构
[1] Univ Washington, Dept Biostat, Seattle, WA 98195 USA
[2] Harvard Univ, Dept Biostat, Boston, MA 02115 USA
[3] Rutgers State Univ, Dept Stat, Piscataway, NJ 08855 USA
基金
美国国家卫生研究院; 美国国家科学基金会;
关键词
accelerated life model; censoring; cox regression; log-rank statistic; multiple events; Poisson process; proportional hazards; rank regression; recurrent events; survival data;
D O I
10.1093/biomet/85.3.605
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
We present a natural extension of the conventional accelerated failure time model for survival data to formulate the effects of covariates on the mean function of the counting process for recurrent events. A class of consistent and asymptotically normal rank estimators is developed for estimating the regression parameters of the proposed model. In addition, a Nelson-Aalen-type estimator for the mean function of the counting process is constructed, which is consistent and, properly normalised, converges weakly to a zero-mean Gaussian process. We assess the finite-sample properties of the proposed estimators and the associated inference procedures through Monte Carlo simulation and provide an application to a well-known bladder cancer study.
引用
收藏
页码:605 / 618
页数:14
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