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Two-stage recurrent events random effects models.

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  • 1Section of Biostatistics, Department of Public Health, University Of Copenhagen, Øster farimagsgade 5, DK-1014, Copenhagen, Denmark. thsc@sund.ku.dk.

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Summary
This summary is machine-generated.

This study introduces novel semiparametric random-effects models for analyzing recurrent events alongside terminal events. These models effectively capture dependencies without requiring tuning parameters, offering a robust statistical approach.

Keywords:
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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Recurrent events and terminal events often occur together in longitudinal studies.
  • Existing models may not fully capture the complex dependency structures between these event types.

Purpose of the Study:

  • To develop and evaluate semiparametric random-effects models for recurrent events in the presence of a terminal event.
  • To model the dependency between recurrent and terminal events using shared random effects.

Main Methods:

  • Utilized proportional marginal rate or mean models for recurrent events and a proportional model for the terminal event.
  • Formulated two-stage models allowing for full or partial sharing of random effects.
  • Employed a parameter estimation procedure that avoids tuning parameters and numerical integration.
  • Standard errors were calculated using bootstrapping.

Main Results:

  • The proposed estimation procedure is numerically stable and effective.
  • The models successfully capture the dependency between recurrent and terminal events.
  • The methods were validated using data from the Taichung Peritoneal Dialysis Study.

Conclusions:

  • The developed semiparametric random-effects models provide a flexible and efficient framework for analyzing recurrent events with a terminal event.
  • The two-stage estimation approach offers a practical alternative to methods requiring numerical integration.
  • The application to dialysis patient data demonstrates the utility of the models in real-world health research.