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Bayesian semiparametric analysis of recurrent failure time data using copulas.

Renate Meyer1, Jose S Romeo1,2

  • 1Department of Statistics, University of Auckland, Private Bag 92109, Auckland, New Zealand.

Biometrical Journal. Biometrische Zeitschrift
|July 9, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Bayesian framework using parametric copulas to model multiple recurrent events, offering a flexible approach for analyzing complex medical data and covariate influences.

Keywords:
Archimedean copulaBayesian analysisRecurrent failuresShared frailtySurvival analysis

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Area of Science:

  • Biostatistics
  • Medical Statistics
  • Survival Analysis

Background:

  • Recurrent event data analysis is crucial for chronic diseases and cancer.
  • Traditional methods like shared frailty models implicitly handle event correlation.
  • Copula models, while used for parallel survival data, are less explored for recurrent events.

Purpose of the Study:

  • To extend copula-based models for analyzing more than two recurrent events.
  • To explicitly model the joint distribution of recurrent events using parametric copulas within a Bayesian framework.
  • To investigate the influence of covariates on both marginals and copula parameters.

Main Methods:

  • Utilized a Bayesian framework for analyzing recurrent event data.
  • Employed parametric copulas to model the joint distribution of multiple recurrent events.
  • Allowed for parametric or nonparametric modeling of marginal baseline hazards.
  • Incorporated covariate effects on marginals via proportional hazards and on copula parameters.

Main Results:

  • Demonstrated the flexibility of the proposed Bayesian copula approach for recurrent event data.
  • Successfully modeled the joint distribution of multiple recurrent events and covariate influences.
  • Applied the methodology to real-world data from an asthma prevention trial.

Conclusions:

  • The Bayesian parametric copula framework provides a powerful and flexible tool for analyzing complex recurrent event data.
  • This approach enhances understanding of disease progression and treatment effects in chronic conditions.
  • The methodology is applicable to various fields requiring analysis of multiple, correlated events over time.