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A hierarchical Bayesian approach to multiple testing in disease mapping

We propose a Bayesian approach to multiple testing in disease mapping. This study was motivated by a real example regarding the mortality rate for lung cancer, males, in the Tuscan region (Italy). The data are relative to the period 1995–1999 for 287 municipalities. We develop a tri-level hierarchic...

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Detalles Bibliográficos
Autores principales: Catelan, Dolores, Lagazio, Corrado, Biggeri, Annibale
Formato: Texto
Lenguaje:English
Publicado: WILEY-VCH Verlag 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3040294/
https://www.ncbi.nlm.nih.gov/pubmed/20809523
http://dx.doi.org/10.1002/bimj.200900209
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author Catelan, Dolores
Lagazio, Corrado
Biggeri, Annibale
author_facet Catelan, Dolores
Lagazio, Corrado
Biggeri, Annibale
author_sort Catelan, Dolores
collection PubMed
description We propose a Bayesian approach to multiple testing in disease mapping. This study was motivated by a real example regarding the mortality rate for lung cancer, males, in the Tuscan region (Italy). The data are relative to the period 1995–1999 for 287 municipalities. We develop a tri-level hierarchical Bayesian model to estimate for each area the posterior classification probability that is the posterior probability that the municipality belongs to the set of non-divergent areas. We show also the connections of our model with the false discovery rate approach. Posterior classification probabilities are used to explore areas at divergent risk from the reference while controlling for multiple testing. We consider both the Poisson-Gamma and the Besag, York and Mollié model to account for extra Poisson variability in our Bayesian formulation. Posterior inference on classification probabilities is highly dependent on the choice of the prior. We perform a sensitivity analysis and suggest how to rely on subject-specific information to derive informative a priori distributions. Hierarchical Bayesian models provide a sensible way to model classification probabilities in the context of disease mapping.
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spelling pubmed-30402942011-02-19 A hierarchical Bayesian approach to multiple testing in disease mapping Catelan, Dolores Lagazio, Corrado Biggeri, Annibale Biom J Research Article We propose a Bayesian approach to multiple testing in disease mapping. This study was motivated by a real example regarding the mortality rate for lung cancer, males, in the Tuscan region (Italy). The data are relative to the period 1995–1999 for 287 municipalities. We develop a tri-level hierarchical Bayesian model to estimate for each area the posterior classification probability that is the posterior probability that the municipality belongs to the set of non-divergent areas. We show also the connections of our model with the false discovery rate approach. Posterior classification probabilities are used to explore areas at divergent risk from the reference while controlling for multiple testing. We consider both the Poisson-Gamma and the Besag, York and Mollié model to account for extra Poisson variability in our Bayesian formulation. Posterior inference on classification probabilities is highly dependent on the choice of the prior. We perform a sensitivity analysis and suggest how to rely on subject-specific information to derive informative a priori distributions. Hierarchical Bayesian models provide a sensible way to model classification probabilities in the context of disease mapping. WILEY-VCH Verlag 2010-12 /pmc/articles/PMC3040294/ /pubmed/20809523 http://dx.doi.org/10.1002/bimj.200900209 Text en Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim http://creativecommons.org/licenses/by/2.5/ Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation.
spellingShingle Research Article
Catelan, Dolores
Lagazio, Corrado
Biggeri, Annibale
A hierarchical Bayesian approach to multiple testing in disease mapping
title A hierarchical Bayesian approach to multiple testing in disease mapping
title_full A hierarchical Bayesian approach to multiple testing in disease mapping
title_fullStr A hierarchical Bayesian approach to multiple testing in disease mapping
title_full_unstemmed A hierarchical Bayesian approach to multiple testing in disease mapping
title_short A hierarchical Bayesian approach to multiple testing in disease mapping
title_sort hierarchical bayesian approach to multiple testing in disease mapping
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3040294/
https://www.ncbi.nlm.nih.gov/pubmed/20809523
http://dx.doi.org/10.1002/bimj.200900209
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