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Bayesian Autoregressive Frailty Models for Inference in Recurrent Events.

Marta Tallarita1, Maria De Iorio1, Alessandra Guglielmi2

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The International Journal of Biostatistics
|November 23, 2019
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We developed new Bayesian models for recurrent event gap times, enabling covariate effect analysis and individual clustering. These models account for time dependencies and handle missing data, aiding medical research.

Keywords:
Dirichlet process mixturesautoregressive modelsmodel selection

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Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Machine Learning

Background:

  • Recurrent events are common in medical research, such as repeated hospitalizations or infections.
  • Analyzing the time gaps between these events is crucial for understanding disease progression and treatment efficacy.
  • Existing models often struggle to capture complex dependencies and individual variability.

Purpose of the Study:

  • To propose novel autoregressive Bayesian semi-parametric models for analyzing gap times between recurrent events.
  • To enable inference on time-varying covariate effects on these gap times.
  • To facilitate clustering of individuals based on their recurrent event time trajectories.

Main Methods:

  • Development of autoregressive Bayesian semi-parametric models incorporating frailty parameters.
  • Utilizing Dirichlet process mixtures for flexible modeling.
  • Employing efficient Markov Chain Monte Carlo (MCMC) algorithms for posterior inference.
  • Considering model selection for unknown autoregression order.

Main Results:

  • The proposed models effectively capture time-dependency in recurrent event gap times.
  • Covariate effects, including time-varying ones, can be reliably inferred.
  • Individuals can be clustered based on their event time patterns.
  • The methodology accommodates censoring and missing data.

Conclusions:

  • The autoregressive Bayesian semi-parametric models offer a flexible and powerful framework for recurrent event data analysis.
  • These models provide valuable insights into disease dynamics and patient stratification.
  • The approach is applicable to various medical scenarios, including cancer patient hospitalizations and urinary tract infections.