Chiou Sy Han, Xu Gongjun, Yan Jun, Huang Chiung-Yu
Department of Mathematical Sciences, University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX 75080, United States of America.
Department of Statistics, University of Michigan, 1085 South University Avenue, Ann Arbor, MI 48109, United States of America.
J Stat Softw. 2023;105. doi: 10.18637/jss.v105.i05. Epub 2023 Jan 28.
Recurrent event analyses have found a wide range of applications in biomedicine, public health, and engineering, among others, where study subjects may experience a sequence of event of interest during follow-up. The R package offers a comprehensive collection of practical and easy-to-use tools for regression analysis of recurrent events, possibly with the presence of an informative terminal event. The regression framework is a general scale-change model which encompasses the popular Cox-type model, the accelerated rate model, and the accelerated mean model as special cases. Informative censoring is accommodated through a subject-specific frailty without any need for parametric specification. Different regression models are allowed for the recurrent event process and the terminal event. Also included are visualization and simulation tools.
复发事件分析在生物医学、公共卫生和工程等领域有广泛应用,在这些领域中,研究对象在随访期间可能会经历一系列感兴趣的事件。这个R软件包提供了一套全面的实用且易于使用的工具,用于对复发事件进行回归分析,可能还存在信息性终末事件。回归框架是一个通用的尺度变换模型,它包含了流行的Cox型模型、加速率模型和加速均值模型作为特殊情况。通过特定个体的脆弱性来处理信息性删失,无需任何参数设定。复发事件过程和终末事件允许使用不同的回归模型。还包括可视化和模拟工具。