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Related Experiment Videos

Dynamic random effects models for times between repeated events.

D Y Fong1, K F Lam, J F Lawless

  • 1Clinical Trials Centre, Faculty of Medicine, University of Hong Kong, Pokfulam Road, Hong Kong, China.

Lifetime Data Analysis
|January 5, 2002
PubMed
Summary
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This study introduces dynamic models for recurrent event data, allowing random effects to vary stochastically over time. These models offer flexible correlation structures for gap times, enhancing analysis in survival research.

Area of Science:

  • Statistics
  • Biostatistics
  • Survival Analysis

Background:

  • Recurrent event data analysis often assumes exchangeable correlation for subject gap times using random effects.
  • Existing methods struggle with non-exchangeable correlation structures, limiting flexibility.

Purpose of the Study:

  • To explore dynamic models where random effects vary stochastically over gap times.
  • To extend Gaussian variance components models and evaluate proportional hazards models for recurrent events.

Main Methods:

  • Developed dynamic models incorporating stochastic variation in random effects.
  • Extended traditional Gaussian variance components models.
  • Evaluated a proportional hazards model via simulation and examples.
  • Considered semiparametric estimation for proportional hazards models.

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Main Results:

  • Gaussian models offer straightforward interpretation of variance structures.
  • Proportional hazards models are well-suited for survival analysis, particularly for regression parameter interpretation.
  • Proportional hazards models show robustness to baseline hazard function choice but sensitivity to random effects model selection.

Conclusions:

  • Dynamic models provide a more flexible approach to modeling recurrent event data with non-exchangeable correlations.
  • Both Gaussian and proportional hazards models are practical, with distinct advantages for interpretation and application.
  • The choice of random effects model is crucial for proportional hazards models in recurrent event analysis.