Addressing Spatial Confounding for Non-Gaussian Outcomes and Areal Data

Thomas M. Menino Convention & Exhibition Center 

When studying associations between environmental exposures and health outcomes, modeling spatially-correlated variation in the health outcome improves precision of regression estimates, but can be insufficient when an exposure variable is collinear with a spatially-correlated random effect. This is commonly referred to as spatial confounding, and can result from a spatially-correlated, unobserved confounding variable, leading to bias and inaccurate variance estimates. Many methods have been proposed to address spatial confounding, but most have focused on Gaussian outcome and exposure data. Some have also only considered point-referenced spatial data. With Gaussian data, it is relatively easy to estimate the latent spatial trends and therefore adjust for spatial confounding. We extend several existing methods for Gaussian data under spatial confounding, including semiparametric methods and explicit Bayesian models for spatial confounding, to model binomial and Poisson response variables. We propose several extensions to add flexibility and improve performance. We compare performance of these methods for non-Gaussian data under spatial confounding using simulation. We find that adjusting for spatial confounding is significantly more difficult with non-Gaussian data, due to the relative lack of information on the spatial trends, and the quality of parameter estimates varies greatly between methods across simulated scenarios. We provide concrete guidance on which methods are best based on the data characteristics. We also extend existing semiparametric methods for point-referenced spatial data to allow areal spatial data. We analyze a spatially-correlated areal data set using the results and methods examined.

bias reduction

double machine learning

Gaussian process

generalized linear model

semiparametric regression

spatial confounding