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Analysis of recurrent event data with spatial random effects using a Bayesian approach.

Jin Jin1, Liuquan Sun2,3, Huang-Tz Ou4

  • 1School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, China.

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|October 7, 2024
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Summary
This summary is machine-generated.

This study introduces a Bayesian model for recurrent event data, incorporating spatial correlations to improve risk prediction. The proposed model significantly outperforms non-spatial models when spatial effects are present.

Keywords:
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Area of Science:

  • Biostatistics
  • Spatial Epidemiology
  • Health Data Science

Background:

  • Recurrent event data are prevalent in observational studies, often exhibiting spatial correlations.
  • Integrating spatial information with health and environmental data can enhance risk prediction accuracy.

Purpose of the Study:

  • To propose a comprehensive Bayesian proportional intensity model for recurrent event data that accounts for spatial random effects.
  • To evaluate the model's performance for both areal and georeferenced spatial data.
  • To assess different baseline intensity functions, including constant and piecewise constant.

Main Methods:

  • Bayesian approach utilizing Markov chain Monte Carlo (MCMC) with Metropolis-Hastings and adaptive Metropolis algorithms.
  • Incorporation of spatial random effects for both areal and georeferenced data.
  • Model performance evaluation using deviance information criterion (DIC) and log pseudo-marginal likelihood (LPML).

Main Results:

  • Simulation studies confirmed the proposed model's superiority over non-spatial models when spatial correlations exist.
  • The model effectively handles recurrent event data with spatial dependencies.
  • The approach was successfully applied to cardiovascular disease recurrence data.

Conclusions:

  • The proposed Bayesian model offers a robust framework for analyzing recurrent event data with spatial dependencies.
  • Accounting for spatial correlations is crucial for accurate risk prediction in health studies.
  • The model provides a valuable tool for epidemiological research involving geographically structured data.