Chen Ying Qing, Wang Mei-Cheng, Huang Yijian
Division of Biostatistics, School of Public Health, University of California, Berkeley, CA 94720-7360, USA.
Biostatistics. 2004 Apr;5(2):277-90. doi: 10.1093/biostatistics/5.2.277.
In longitudinal studies, individual subject may experience recurrent events of the same type over a relatively long period of time. The longitudinal pattern of gaps between successive recurrent events is often of great research interest. In this article, the probability structure of the recurrent gap times is first explored in the presence of censoring. According to the discovered structure, we introduce the stratified proportional reverse-time hazards models with unspecified baseline functions to accommodate individual heterogeneity, when the longitudinal pattern parameter is of main interest. Inference procedures are proposed and studied by way of proper riskset construction. The proposed methodology is demonstrated by the Monte Carlo simulations and an application to a well-known Denmark schizophrenia cohort study data set.
在纵向研究中，个体受试者可能在一段相对较长的时间内经历同一类型的复发事件。连续复发事件之间间隔的纵向模式通常具有很大的研究价值。在本文中，首先探讨了存在删失情况下复发间隔时间的概率结构。根据所发现的结构，当纵向模式参数是主要研究对象时，我们引入具有未指定基线函数的分层比例逆时风险模型，以适应个体异质性。通过适当的风险集构建提出并研究了推断程序。通过蒙特卡罗模拟和对一个著名的丹麦精神分裂症队列研究数据集的应用，展示了所提出的方法。
