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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Regression Modeling for Recurrent Events Possibly with an Informative Terminal Event Using R Package reReg.

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  • 1Department of Mathematical Sciences, University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX 75080, United States of America.

Journal of Statistical Software
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Summary
This summary is machine-generated.

The reReg R package provides tools for analyzing recurrent events and informative terminal events. It offers a flexible regression framework accommodating various models and informative censoring for robust biomedical and public health research.

Keywords:
event plotfrailtyjoint modelmean cumulative functionsimulationsurvival data

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Computational Biology

Background:

  • Recurrent event data analysis is crucial in fields like medicine and public health.
  • Existing methods may not adequately handle informative terminal events or complex censoring mechanisms.
  • A need exists for flexible, integrated tools for recurrent event regression.

Purpose of the Study:

  • To introduce the reReg R package for comprehensive recurrent event data analysis.
  • To provide a unified regression framework for recurrent events and informative terminal events.
  • To offer practical tools for modeling, visualization, and simulation in survival analysis.

Main Methods:

  • Utilizes a general scale-change regression model encompassing Cox-type, accelerated rate, and accelerated mean models.
  • Accommodates informative censoring using subject-specific frailty without parametric assumptions.
  • Allows distinct regression models for recurrent and terminal event processes.

Main Results:

  • The reReg package offers a flexible and unified approach to recurrent event analysis.
  • It effectively handles informative terminal events and censoring.
  • Includes tools for data visualization and simulation to aid analysis.

Conclusions:

  • The reReg R package is a valuable, user-friendly tool for advanced recurrent event analysis.
  • It enhances the ability to model complex survival data in biomedical and public health research.
  • Facilitates robust statistical inference for recurrent events with informative censoring.