Regression Modeling for Recurrent Events Using R Package reReg

TL;DR

The paper introduces the R package reReg, which provides versatile tools for regression analysis of recurrent events, accommodating informative censoring and multiple modeling approaches in biomedicine and related fields.

Contribution

It presents a comprehensive R package implementing a flexible regression framework for recurrent events, including models for informative censoring and separate modeling of terminal events.

Findings

Provides practical tools for recurrent event analysis in R

Supports various regression models including Cox-type and accelerated models

Includes visualization and simulation functionalities

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 (Chiou and Huang 2021) 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 no 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.