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Spatial confounding in multivariate areal data analysis
Kyle Lin Wu1, Sudipto Banerjee1
1Department of Biostatistics, University of California Los Angeles (UCLA), Los Angeles, CA 90095, United States.
Biometrics
|August 11, 2026
Summary
Spatial analysis in Bayesian coregionalized areal regression models limits variance inflation and improves precision, even with spatial confounding and multivariate disease dependence. Hierarchical spatial models are recommended for accurate results.
Area of Science:
- Biostatistics
- Spatial Epidemiology
- Geographic Information Systems (GIS)
Background:
- Spatial confounding can bias regression coefficients in disease mapping.
- Limited research exists on spatial confounding's interaction with multivariate disease dependence.
- Bayesian areal regression models are commonly used for disease analysis.
Purpose of the Study:
- To investigate spatial confounding in Bayesian coregionalized areal regression models with multivariate disease dependence.
- To derive novel results on the impact of spatial confounding on model precision.
- To compare spatial and non-spatial analysis approaches in the presence of spatial confounding.
Main Methods:
- Developed a Bayesian coregionalized areal regression model.
- Investigated spatial confounding from both analysis and data generation perspectives.
- Conducted simulation experiments to evaluate model performance.
- Analyzed US county-level data on obesity, diabetes, and cancer mortality.
Main Results:
- Posterior variance inflation is limited in multivariate areal models under spatial confounding.
- Spatial point estimators of fixed effects show higher precision than non-spatial counterparts.
- Spatial analysis outperformed non-spatial models in simulations, even with misspecified spatial structures.
Conclusions:
- Hierarchical spatial models are preferable for disease analysis, especially with spatial confounding.
- Spatial analysis provides more precise estimates in the presence of spatial confounding and multivariate disease dependence.
- The study supports the use of spatial methods for analyzing complex health data.
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