title: Evaluating geographically weighted regression models for environmental chemical risk analysis.
abstract: In the evaluation of cancer risk related to environmental chemical exposures, the effect of many correlated chemicals on disease is often of interest. The relationship between correlated environmental chemicals and health effects is not always constant across a study area, as exposure levels may change spatially due to various environmental factors. Geographically weighted regression (GWR) has been proposed to model spatially varying effects. However, concerns about collinearity effects, including regression coefficient sign reversal (ie, reversal paradox), may limit the applicability of GWR for environmental chemical risk analysis. A penalized version of GWR, the geographically weighted lasso, has been proposed to remediate the collinearity effects in GWR models. Our focus in this study was on assessing through a simulation study the ability of GWR and GWL to correctly identify spatially varying chemical effects for a mixture of correlated chemicals within a study area. Our results showed that GWR suffered from the reversal paradox, while GWL overpenalized the effects for the chemical most strongly related to the outcome.
authors.affiliation: Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, VA.
authors.forename: Jenna
authors.initials: J
authors.lastname: Czarnota
authors.affiliation: Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, VA.
authors.forename: David C
authors.initials: DC
authors.lastname: Wheeler
authors.affiliation: Department of Preventive Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
authors.forename: Chris
authors.initials: C
authors.lastname: Gennings
keywords: GWL; GWR; cancer risk; chemical mixtures; environment; lasso; reversal paradox