Document Type
Article
Original Publication Date
2015
Journal/Book/Conference Title
Cancer Informatics
Volume
2015
Issue
14
DOI of Original Publication
10.4137/CIN.S17296
Date of Submission
December 2015
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.
Rights
Copyright © 2015 the author(s), publisher and licensee Libertas Academica Limited. This is an open-access article distributed under the terms of the Creative Commons CC-BY-NC 3.0 License.
Is Part Of
VCU Biostatistics Publications
Comments
Originally published at http://dx.doi.org/10.4137/CIN.S17296