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A general semiparametric Bayesian discrete-time recurrent events model.

Adam J King1, Robert E Weiss2

  • 1Department of Mathematics & Statistics, California State Polytechnic University, Pomona, 3801 West Temple Ave., Pomona, CA, USA.

Biostatistics (Oxford, England)
|August 3, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian discrete-time proportional hazards model for analyzing time-to-event data, particularly useful for patient-reported outcomes like drug cessation. The new model, implemented in the R package `brea`, offers improved performance over existing methods.

Keywords:
Competing risksCox modelDiscrete timeGeneralized additive modelsRecurrent eventsSemiparametric modelsSoftwareSubstance abuse

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Event time data, common in patient-reported outcomes, are often discrete (e.g., monthly drug use cessation).
  • Existing methods for discrete survival data have limitations, such as treating data as continuous or omitting crucial features like random effects.

Purpose of the Study:

  • To develop a general Bayesian discrete-time proportional hazards model that incorporates features from continuous-time models.
  • To provide a flexible and efficient modeling framework for discrete survival data, including competing risks and random effects.

Main Methods:

  • A Bayesian discrete-time proportional hazards model was developed.
  • The model incorporates competing risks, frailties, flexible baseline hazards, and semiparametric covariate effects.
  • Efficient Markov chain Monte Carlo (MCMC) inference algorithms were implemented in the R package `brea`.

Main Results:

  • The proposed model demonstrated superior performance in a substance abuse application compared to existing approaches.
  • The `brea` package facilitates reproducible analysis of discrete-time survival data.
  • The model was successfully applied to a clinical trial dataset on anesthesia administration.

Conclusions:

  • The developed Bayesian discrete-time proportional hazards model offers a robust and flexible alternative for analyzing discrete survival data.
  • The `brea` R package provides a valuable tool for researchers working with such data, enhancing analytical capabilities in biostatistics and clinical research.