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

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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.

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Authors and Affiliations

  1. Department of Statistics “G. Parenti”, University of Florence, Viale Morgagni 59, 50134, Firenze, Italy

    Dolores Catelan & Annibale Biggeri

  2. Biostatistics Unit, Centro per lo Studio e la Prevenzione Oncologica, Florence, Presidio Villa delle Rose, Via Cosimo il Vecchio 2, 50134, Firenze, Italy

    Annibale Biggeri

  3. Department of Statistical Sciences, University of Udine, Via Treppo 18, 33100, Udine, Italy

    Corrado Lagazio

Authors
  1. Dolores Catelan
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  2. Annibale Biggeri
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  3. Corrado Lagazio
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Correspondence to Corrado Lagazio.

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Catelan, D., Biggeri, A. & Lagazio, C. On the clustering term in ecological analysis: how do different prior specifications affect results?. Stat Methods Appl 18, 49–61 (2009). https://doi.org/10.1007/s10260-007-0089-x

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  • DOI: https://doi.org/10.1007/s10260-007-0089-x

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