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Joint scale-change models for recurrent events and failure time.

Gongjun Xu1, Sy Han Chiou2, Chiung-Yu Huang3

  • 1Assistant Professor, School of Statistics, University of Minnesota, Minneapolis, MN 55455.

Journal of the American Statistical Association
|September 26, 2017
PubMed
Summary

This study introduces a robust joint scale-change model for analyzing recurrent events and failure times, offering marginal interpretations for regression parameters. The method avoids restrictive distributional assumptions for enhanced reliability in complex data analysis.

Keywords:
Accelerated failure time modelFrailtyInformative censoringMarginal modelsSemiparametric methods

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Recurrent event data are common in biomedical and social sciences.
  • These processes are often terminated by a correlated failure event, like death or treatment failure.
  • Existing joint modeling approaches may impose restrictive assumptions.

Purpose of the Study:

  • To propose a novel joint scale-change model for recurrent events and failure times.
  • To model the association between recurrent events and failure times using a shared frailty variable.
  • To provide regression parameter interpretations that are marginal, unlike Cox-type models.

Main Methods:

  • Developed a joint scale-change model incorporating a shared frailty variable.
  • Ensured robustness by not assuming a parametric distribution for the frailty variable.
  • Avoided the strong Poisson-type assumption for recurrent event processes.
  • Established consistency and asymptotic normality for semiparametric estimators.
  • Implemented a computationally efficient resampling-based procedure for variance estimation.

Main Results:

  • The proposed joint scale-change model offers marginal interpretations for regression parameters.
  • The semiparametric estimators are shown to be consistent and asymptotically normal.
  • A simulation study and real-world data analysis (Danish Psychiatric Central Register) demonstrated the method's performance.

Conclusions:

  • The proposed joint scale-change model provides a flexible and robust framework for analyzing recurrent event data and failure times.
  • This approach is advantageous due to its fewer parametric assumptions and interpretable regression coefficients.
  • The method is validated through simulation and application to hospitalization data.