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

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 2022) o ﬀ ers 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-speci ﬁ c frailty without any need for parametric speci ﬁ cation. Di ﬀ erent regression models are allowed for the recurrent event process and the terminal event. Also included are visualization and simulation tools. We next ﬁ t a generalized scale-change model in the following to gain more insights into the treatment e ﬀ ects on the recurrent event progress. The results suggest that patients in the combination treatment group tended to have a decelerated time to new lesions by a factor of 1 / exp( − 0 . 969) = 2 . 635 , p = 0 . 009. This implies patients in the combination treatment group were more likely to experience new lesions later. The hypothesis testing results suggest an accelerated rate model or an accelerated mean model on the recurrent event process.