We study how different prior assumptions on the spatially structured heterogeneity term of the convolution hierarchical Bayesian model for spatial disease data could affect the results of an ecological analysis when response and exposure exhibit a strong spatial pattern.We show that in this case the estimate of the regression parameter could be strongly biased, both by analyzing the association between lung cancer mortality and education level on a real dataset and by a simulation experiment. The analysis is based on a hierarchical Bayesian model with a time dependent covariate in which we allow for a latency period between exposure and mortality, with time and space random terms and misaligned exposure-disease data.

On the clustering term in ecological analysis: how do different prior specifications affect results? / D. Catelan; A. Biggeri; C. Lagazio. - In: STATISTICAL METHODS & APPLICATIONS. - ISSN 1618-2510. - STAMPA. - 18:(2009), pp. 49-61.

On the clustering term in ecological analysis: how do different prior specifications affect results?

CATELAN, DOLORES;BIGGERI, ANNIBALE;LAGAZIO, CORRADO
2009

Abstract

We study how different prior assumptions on the spatially structured heterogeneity term of the convolution hierarchical Bayesian model for spatial disease data could affect the results of an ecological analysis when response and exposure exhibit a strong spatial pattern.We show that in this case the estimate of the regression parameter could be strongly biased, both by analyzing the association between lung cancer mortality and education level on a real dataset and by a simulation experiment. The analysis is based on a hierarchical Bayesian model with a time dependent covariate in which we allow for a latency period between exposure and mortality, with time and space random terms and misaligned exposure-disease data.
18
49
61
D. Catelan; A. Biggeri; C. Lagazio
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2158/355941
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