Cure Models in Survival Analysis

被引：109
|
作者：
Amico, Mailis [1 ]
Van Keilegom, Ingrid [1 ]
机构：
[1] Katholieke Univ Leuven, Res Ctr Operat Res & Business Stat ORSTAT, B-3000 Leuven, Belgium
来源：
ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, VOL 5 | 2018年 / 5卷
基金：
欧盟地平线“2020”;
关键词：
survival analysis; mixture cure model; promotion time cure model; censoring; regression; PROPORTIONAL HAZARDS MODEL; LONG-TERM SURVIVORS; FAILURE TIME MODEL; BREAST-CANCER; PENALIZED LIKELIHOOD; VARIABLE SELECTION; REGRESSION-MODELS; MIXTURE-MODELS; SEMIPARAMETRIC MODELS; MAXIMUM-LIKELIHOOD;
D O I：
10.1146/annurev-statistics-031017-100101
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
When analyzing time-to-event data, it often happens that a certain fraction of the data corresponds to subjects who will never experience the event of interest. These event times are considered as infinite and the subjects are said to be cured. Survival models that take this feature into account are commonly referred to as cure models. This article reviews the literature on cure regression models in which the event time (response) is subject to random right censoring and has a positive probability to be equal to infinity.
引用
收藏
页码：311 / 342
页数：32
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