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Robustness of the BYM model in absence of spatial variation in the residuals

BACKGROUND: In the context of ecological studies, the Bayesian hierarchical Poisson model is of prime interest when studying the association between environmental exposure and rare diseases. However, adding spatially structured extra-variability in the model fitted to the data when such extra-variab...

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Autores principales: Latouche, Aurélien, Guihenneuc-Jouyaux, Chantal, Girard, Claire, Hémon, Denis
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2241594/
https://www.ncbi.nlm.nih.gov/pubmed/17883857
http://dx.doi.org/10.1186/1476-072X-6-39
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author Latouche, Aurélien
Guihenneuc-Jouyaux, Chantal
Girard, Claire
Hémon, Denis
author_facet Latouche, Aurélien
Guihenneuc-Jouyaux, Chantal
Girard, Claire
Hémon, Denis
author_sort Latouche, Aurélien
collection PubMed
description BACKGROUND: In the context of ecological studies, the Bayesian hierarchical Poisson model is of prime interest when studying the association between environmental exposure and rare diseases. However, adding spatially structured extra-variability in the model fitted to the data when such extra-variability does not exist conditionally on the covariates included in the model (over-fitting) may bias the estimation of the ecological association between covariates and relative risks toward the null. In order to investigate that possibility, a simulation study of the impact of introducing unnecessary residual spatial structure in the estimation model was conducted. RESULTS: In the case where no underlying extra-variability from the Poisson process exists, the simulation results show that models accounting for structured and unstructured residuals do not underestimate the ecological association, unless covariates have a very strong autocorrelation structure, i.e., 0.98 at 100 km on a territory of diameter 1000 km."
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spelling pubmed-22415942008-02-13 Robustness of the BYM model in absence of spatial variation in the residuals Latouche, Aurélien Guihenneuc-Jouyaux, Chantal Girard, Claire Hémon, Denis Int J Health Geogr Research BACKGROUND: In the context of ecological studies, the Bayesian hierarchical Poisson model is of prime interest when studying the association between environmental exposure and rare diseases. However, adding spatially structured extra-variability in the model fitted to the data when such extra-variability does not exist conditionally on the covariates included in the model (over-fitting) may bias the estimation of the ecological association between covariates and relative risks toward the null. In order to investigate that possibility, a simulation study of the impact of introducing unnecessary residual spatial structure in the estimation model was conducted. RESULTS: In the case where no underlying extra-variability from the Poisson process exists, the simulation results show that models accounting for structured and unstructured residuals do not underestimate the ecological association, unless covariates have a very strong autocorrelation structure, i.e., 0.98 at 100 km on a territory of diameter 1000 km." BioMed Central 2007-09-20 /pmc/articles/PMC2241594/ /pubmed/17883857 http://dx.doi.org/10.1186/1476-072X-6-39 Text en Copyright © 2007 Latouche et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Latouche, Aurélien
Guihenneuc-Jouyaux, Chantal
Girard, Claire
Hémon, Denis
Robustness of the BYM model in absence of spatial variation in the residuals
title Robustness of the BYM model in absence of spatial variation in the residuals
title_full Robustness of the BYM model in absence of spatial variation in the residuals
title_fullStr Robustness of the BYM model in absence of spatial variation in the residuals
title_full_unstemmed Robustness of the BYM model in absence of spatial variation in the residuals
title_short Robustness of the BYM model in absence of spatial variation in the residuals
title_sort robustness of the bym model in absence of spatial variation in the residuals
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2241594/
https://www.ncbi.nlm.nih.gov/pubmed/17883857
http://dx.doi.org/10.1186/1476-072X-6-39
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