Accounting for Spatial Confounding in Epidemiological Studies with Individual-Level Exposures: An Exposure-Penalized Spline Approach. (12th April 2022)

• Record Type: Journal Article
• Authors: Bobb, Jennifer F.; Cruz, Maricela F.; Mooney, Stephen J.; Drewnowski, Adam; Arterburn, David; Cook, Andrea J.
• Is Part Of: Journal of the Royal Statistical Society. Volume 185:Number 3(2022)
• Page Start: 1271
• Page End: 1293
• Publication Date: 2022-04-12
• Subjects: big spatial data -- electronic health records -- Gaussian process models -- point process data -- spatial confounding; Social sciences -- Statistical methods -- Periodicals; Statistics -- Periodicals; 300.15195
• DOI: 10.1111/rssa.12831
• Languages: English
• ISSNs: 0964-1998

Abstract

Abstract: In the presence of unmeasured spatial confounding, spatial models may actually increase (rather than decrease) bias, leading to uncertainty as to how they should be applied in practice. We evaluated spatial modelling approaches through simulation and application to a big data electronic health record study. Whereas the risk of bias was high for purely spatial exposures (e.g. built environment), we found very limited potential for increased bias for individual-level exposures that cluster spatially (e.g. smoking status). We also proposed a novel exposure-penalized spline approach that selects the degree of spatial smoothing to explain spatial variability in the exposure. This approach appeared promising for efficiently reducing spatial confounding bias.