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Regression analysis of mixed panel count data with dependent terminal events.

Guanglei Yu1, Liang Zhu2, Yang Li3

  • 1Department of Statistics, University of Missouri, Columbia, MO, U.S.A.

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This study introduces a new regression analysis method for mixed recurrent event and panel count data, accounting for terminal events. The proposed method demonstrates effective performance in practical scenarios, including childhood cancer research.

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Epidemiology

Background:

  • Event history studies generate recurrent event data (continuous follow-up) or panel count data (discrete observations).
  • A mixed data type, combining both recurrent and panel count data, can occur in practice.
  • Dependent terminal events may affect the occurrence of recurrent events, complicating analysis.

Purpose of the Study:

  • To develop a regression analysis framework for mixed recurrent event and panel count data.
  • To incorporate the impact of a dependent terminal event in the analysis.
  • To estimate regression parameters for this complex data structure.

Main Methods:

  • An estimating equation-based approach is proposed for parameter estimation.
  • Asymptotic properties of the proposed estimator are theoretically established.
  • A simulation study is conducted to evaluate the finite-sample performance.

Main Results:

  • The proposed method effectively handles mixed recurrent event and panel count data with terminal events.
  • Simulation results indicate good finite-sample performance of the estimator.
  • The methodology is successfully applied to a real-world childhood cancer study.

Conclusions:

  • The developed regression analysis method provides a robust approach for mixed event history data.
  • The findings are applicable to various fields employing event history studies, particularly in health research.
  • The study addresses a gap in analyzing complex event data structures common in longitudinal studies.