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Updated: Jun 19, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

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Published on: October 23, 2020

Competing-triggering effect models for multitype recurrent event data.

Tianhao Song1, Jason Fine2, Payal Khincha3

  • 1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill,NC 27599, United States.

Biometrics
|June 17, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new Cox-type model for analyzing how different types of recurrent events influence each other over time. The method effectively models triggering effects in complex lifetime data, as shown in simulations and Li-Fraumeni syndrome patient data.

Keywords:
complex event history dataexponential decaymultiple event typesnonlinear regressionproportional intensity

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Epidemiology

Background:

  • Multitype recurrent events are common in lifetime data analysis.
  • Previous models focused on single event types and nonlinear triggering effects.
  • Existing proportional intensity models have limitations in capturing complex triggering dynamics.

Purpose of the Study:

  • To propose a general Cox-type structure for modeling triggering effects among multiple event types.
  • To allow for separate triggering effects for each event type on events of interest.
  • To develop a flexible nonlinear model formulation with common parameters across different event types.

Main Methods:

  • Developed a general Cox-type structure for multitype recurrent event data.
  • Derived partial likelihood estimators for model parameters.
  • Established consistency and asymptotic normality of the proposed estimators.
  • Provided plug-in variance estimators for practical application.

Main Results:

  • Demonstrated good performance of the proposed methods through simulation experiments.
  • Successfully applied the models to analyze triggering effects between breast and non-breast cancers in Li-Fraumeni syndrome patients.
  • Validated the model's ability to capture complex triggering relationships in real-world data.

Conclusions:

  • The proposed Cox-type structure provides a robust framework for analyzing multitype recurrent events.
  • The developed statistical methods are effective and perform well in simulations and real data applications.
  • This approach enhances understanding of event dependencies in lifetime data analysis, particularly in clinical settings.