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Exploring the heterogeneity in recurrent episode lengths based on quantile regression.

Yi Liu1, Guillermo E Umpierrez2, Limin Peng1

  • 1Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, United States.

Biometrics
|September 19, 2025
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Understanding recurrent episode lengths in chronic diseases is key for personalized management. This study introduces a novel quantile regression method to analyze episode duration, improving upon existing approaches.

Keywords:
dependent truncationinformative cluster sizequantile regressionrecurrent episode lengthtime-dependent covariate

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Chronic Disease Research

Background:

  • Recurrent episode data are common in chronic disease studies.
  • Understanding episode length heterogeneity is crucial for tailored disease management.
  • Existing methods lack direct interpretation or make unrealistic assumptions.

Purpose of the Study:

  • To propose a novel statistical modeling strategy for recurrent episode data.
  • To specifically address the heterogeneity in recurrent episode lengths.
  • To overcome limitations of existing approaches in chronic disease research.

Main Methods:

  • Utilizing quantile regression to model episode lengths.
  • Incorporating time-dependent covariates into the model.
  • Treating recurrent episodes as clustered data, handling dependent censoring, truncation, and informative cluster size.

Main Results:

  • The proposed method offers direct interpretation of episode lengths.
  • The estimation procedure is computationally simple with desirable asymptotic properties.
  • Numerical studies show advantages over naive adaptations of existing methods.

Conclusions:

  • The developed quantile regression strategy effectively analyzes recurrent episode lengths in chronic diseases.
  • This approach provides a more flexible and interpretable tool for disease management.
  • The method addresses complex data features like dependent censoring and truncation.