Nonparametric estimation of the gap time distributions for serial events with censored data

被引:148
作者
Lin, DY
Sun, W
Ying, ZL
机构
[1] Univ Washington, Dept Biostat, Seattle, WA 98195 USA
[2] Schering Plough Corp, Res Inst, Kenilworth, NJ 07033 USA
[3] Rutgers State Univ, Dept Stat, Hill Ctr, Piscataway, NJ 08855 USA
关键词
bivariate distribution; correlated failure times; dependent censoring; Kaplan-Meier estimator; multiple events; multivariate failure time; recurrent events;
D O I
10.1093/biomet/86.1.59
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
In many follow-up studies, each subject can potentially experience a series of events, which may be repetitions of essentially the same event or may be events of entirely different natures. This paper provides a simple nonparametric estimator for the multivariate :distribution function of the gap times between successive events when the follow-up time is subject to right censoring. The estimator is consistent and, upon proper normalisation, converges weakly to a zero-mean Gaussian process with an easily estimated covariance function. Numerical studies demonstrate that both the distribution function estimator and its covariance function estimator perform well for practical sample sizes. An application to a colon cancer study is presented.
引用
收藏
页码:59 / 70
页数:12
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