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Bayesian nonparametric inference for panel count data with an informative observation process.

Ye Liang1, Yang Li2, Bin Zhang3

  • 1Department of Statistics, Oklahoma State University, Stillwater, OK, 74074, USA.

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|February 23, 2018
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Summary
This summary is machine-generated.

This study introduces a new statistical model for analyzing recurrent events, like tumor or infection relapses. The bivariate Gaussian Cox process model improves the analysis of panel count data in clinical and observational research.

Keywords:
Gaussian processHamiltonian Monte Carlodependent frailtynonhomogeneous Poisson processrecurrent event

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Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Epidemiology

Background:

  • Recurrent events, such as tumor or infection recurrences, are common in clinical trials and observational studies.
  • Accurate analysis of recurrent event data is crucial for understanding disease progression and treatment efficacy.
  • Existing methods may not fully capture the complexities of recurrent event processes and associated factors.

Purpose of the Study:

  • To propose a novel bivariate Gaussian Cox process model for the joint analysis of recurrent event and observation processes.
  • To develop a Bayesian nonparametric inference framework for estimating model parameters, including regression coefficients, frailty effects, and baseline intensity functions.
  • To demonstrate the utility and efficiency of the proposed method through simulation studies and a real-world clinical trial dataset.

Main Methods:

  • A bivariate Gaussian Cox process model was developed to jointly model recurrent events and the observation process.
  • Bayesian nonparametric inference was employed for simultaneous estimation of regression parameters, bivariate frailty effects, and baseline intensity functions.
  • Markov chain Monte Carlo (MCMC) methods with advanced computational techniques were utilized for inference, including predictive inference.

Main Results:

  • Simulation studies demonstrated the efficiency and accuracy of the proposed bivariate Gaussian Cox process model.
  • The model effectively estimated regression parameters, frailty effects, and baseline intensity functions.
  • Application to a skin cancer clinical trial dataset provided valuable insights into recurrent event patterns.

Conclusions:

  • The proposed bivariate Gaussian Cox process model offers a robust and efficient approach for panel count data analysis of recurrent events.
  • The Bayesian nonparametric framework facilitates comprehensive inference, including parameter estimation and prediction.
  • This methodology enhances the analysis of recurrent events in both clinical and observational research settings, as illustrated by the skin cancer data analysis.