Nonparametric Estimation of a Recurrent Survival Function.
Nonparametric Estimation of a Recurrent Survival Function.
复制标题
DOI：
10.1080/01621459.1999.10473831
复制
发表时间：
1999-03
影响因子：
3.7
通讯作者：
Mei-Cheng Wang;Shu-Hui Chang
中科院分区：
文献类型：
--
作者：
Mei-Cheng Wang;Shu-Hui Chang
在纵向设计的研究中经常遇到复发事件数据。假设重现时间是两个连续重现事件之间的时间。在统计分析中，复发时间可以被视为一种相关的生存数据。一般来说，由于复发时间的有序性，适用于边缘模型中标准相关生存数据的统计方法可能不适用于复发时间数据。具体而言，为了估计边际生存函数，从汇总复发时间推导出的Kaplan-Meier估计量可作为标准相关生存数据的一致估计量，但不适用于复发时间数据。本文考虑非参数模型中边际生存函数的估计问题。引入了一类非参数估计。的适当性的估计证实了统计理论和模拟。精神分裂症数据的模拟和分析，以说明估计的性能。
Recurrent event data are frequently encountered in studies with longitudinal designs. Let the recurrence time be the time between two successive recurrent events. Recurrence times can be treated as a type of correlated survival data in statistical analysis. In general, because of the ordinal nature of recurrence times, statistical methods that are appropriate for standard correlated survival data in marginal models may not be applicable to recurrence time data. Specifically, for estimating the marginal survival function, the Kaplan-Meier estimator derived from the pooled recurrence times serves as a consistent estimator for standard correlated survival data but not for recurrence time data. In this article we consider the problem of how to estimate the marginal survival function in nonparametric models. A class of nonparametric estimators is introduced. The appropriateness of the estimators is confirmed by statistical theory and simulations. Simulation and analysis from schizophrenia data are presented to illustrate the estimators' performance.