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Joint model for bivariate zero-inflated recurrent event data with terminal events.

Yang-Jin Kim1

  • 1Department of Statistics, Sookmyung Women's University, Seoul, South Korea.

Journal of Applied Statistics
|June 16, 2022
PubMed
Summary

This study introduces a statistical model for bivariate recurrent event data, addressing scenarios with zero events due to terminal events. The model effectively handles zero inflation and terminal events simultaneously using bivariate frailty effects.

Keywords:
Bivariate recurrent eventcure rate modelfrailty effectjoint modelpiecewise baselineterminal event

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Epidemiology

Background:

  • Bivariate recurrent event data analysis is complex, especially when subjects may not experience events or have a terminal event.
  • Existing models may not adequately capture scenarios with both zero event occurrences and a competing terminal event.

Purpose of the Study:

  • To propose a joint statistical model for bivariate recurrent event data that accounts for zero inflation and terminal events.
  • To develop a flexible modeling approach for complex event data structures.

Main Methods:

  • Implementation of a joint model incorporating bivariate frailty effects.
  • Utilizing simulation studies to validate the performance and robustness of the proposed model.

Main Results:

  • The joint model successfully accommodates both zero-inflated data and the presence of terminal events.
  • Simulation results demonstrate the model's effectiveness in various scenarios.

Conclusions:

  • The developed joint model provides a robust framework for analyzing bivariate recurrent event data with zero inflation and terminal events.
  • The model offers valuable insights for applications in medical research, such as analyzing infection data in acute myeloid leukemia patients.