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GRASS-NB: Group-structured variable selection for spatial negative binomial data with applications to cancer registry

Chloe Mattila1, Brian Neelon1, Kalyani Sonawane1

  • 1Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA.

Biorxiv : the Preprint Server for Biology
|November 24, 2025
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Summary

This study introduces a Bayesian negative binomial regression model for analyzing complex spatial count data. It enhances feature selection by incorporating a novel group-structured prior, improving identification of key risk factors and biomarkers.

Keywords:
hierarchical shrinkagehorseshoe priornegative binomial distributionspatial count dataspike and slab prior

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Area of Science:

  • Biostatistics
  • Spatial Epidemiology
  • Computational Biology

Background:

  • Analyzing spatially structured, overdispersed count data with many predictors is common in epidemiology and spatial omics.
  • Effective feature selection is crucial for identifying risk factors or biomarkers in such datasets.
  • Existing Bayesian negative binomial regression models with variable selection priors often lack spatial considerations and group structure.

Purpose of the Study:

  • To propose a flexible Bayesian negative binomial regression model for spatially autocorrelated count data.
  • To introduce a novel group-structured prior by combining spike-and-slab and continuous horseshoe methods for enhanced feature selection.
  • To evaluate the model's performance, including specificity, precision, and computational efficiency, particularly in high-dimensional settings.

Main Methods:

  • Developed a Bayesian negative binomial regression framework incorporating spatial autocorrelation.
  • Introduced a hybrid group-structured prior combining spike-and-slab and continuous horseshoe shrinkage.
  • Evaluated model performance using simulations, including 'large p, small n' scenarios, and applied it to real-world datasets.

Main Results:

  • The proposed model effectively handles spatial autocorrelation and high-dimensional predictors in count data.
  • The novel group-structured prior demonstrated robust performance in feature selection.
  • The model was successfully applied to identify cancer risk factors and predict gene expression in spatial omics data.

Conclusions:

  • The developed Bayesian negative binomial regression model with a novel group-structured prior offers a flexible and powerful tool for analyzing complex spatial count data.
  • This approach enhances the identification of influential predictors in fields ranging from population epidemiology to spatial omics.
  • An R package is available to facilitate the application of this methodology.