Regression Modeling for Recurrent Events Possibly with an Informative Terminal Event Using R Package reReg.

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Statistical Software Pub Date : 2023-01-01 Epub Date: 2023-01-28 DOI:10.18637/jss.v105.i05
Sy Han Chiou, Gongjun Xu, Jun Yan, Chiung-Yu Huang
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引用次数: 0

Abstract

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 reReg 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.

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使用 R 软件包 reReg 对可能带有信息终端事件的重复事件进行回归建模。
复发性事件分析在生物医学、公共卫生和工程学等领域有着广泛的应用,研究对象在随访过程中可能会经历一系列感兴趣的事件。R 软件包 reReg 提供了一系列实用、易用的回归分析工具,用于对可能存在信息性终末事件的复发性事件进行回归分析。回归框架是一个通用的标度变化模型,包括流行的 Cox 型模型、加速率模型和加速平均值模型等特例。信息剔除通过特定受试者的虚弱程度来解决,无需任何参数规范。对于重复事件过程和终末事件,允许采用不同的回归模型。此外,还包括可视化和模拟工具。
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来源期刊
Journal of Statistical Software
Journal of Statistical Software 工程技术-计算机:跨学科应用
CiteScore
10.70
自引率
1.70%
发文量
40
审稿时长
6-12 weeks
期刊介绍: The Journal of Statistical Software (JSS) publishes open-source software and corresponding reproducible articles discussing all aspects of the design, implementation, documentation, application, evaluation, comparison, maintainance and distribution of software dedicated to improvement of state-of-the-art in statistical computing in all areas of empirical research. Open-source code and articles are jointly reviewed and published in this journal and should be accessible to a broad community of practitioners, teachers, and researchers in the field of statistics.
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