Evaluating geographically weighted regression models for environmental chemical risk analysis

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 penalised version of GWR, the geographically weighted lasso, has been proposed to remediate the collinearity effects in GWR models. The 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. The results showed that GWR suffered from the reversal paradox, while GWL overpenalised the effects for the chemical most strongly related to the outcome.

Authors: Czarnota J, Wheeler DC, Gennings C. ;Full Source: Cancer Inform. 2015 May 5;14(Suppl 2):117-27. doi: 10.4137/CIN.S17296. eCollection 2015. ;