Hierarchical Bayesian Spatio-Temporal Analysis of Childhood Cancer Trends

  • Author(s) / Creator(s)
  • In this paper, generalized additive mixed models are constructed for the analysis of geographical and temporal variability of cancer ratios. In this class of models, spatially correlated random effects and temporal components are adopted. Spatio-temporal models that use intrinsic conditionally autoregressive smoothing across the spatial dimension and B-spline smoothing over the temporal dimension are considered. We study the patterns of incidence ratios over time and identify areas with consistently high ratio estimates as areas for further investigation. A hierarchical Bayesian approach using Markov chain Monte Carlo techniques is employed for the analysis of the childhood cancer diagnoses in the province of Alberta, Canada during 1995-2004. We also evaluate the sensitivity of such analyses to prior assumptions in the Poisson context.

  • Date created
    2012
  • Subjects / Keywords
  • Type of Item
    Article (Published)
  • DOI
    https://doi.org/10.7939/R3DB7VX80
  • License
    Attribution-NonCommercial-NoDerivatives 3.0 International
  • Language
  • Citation for previous publication
    • Torabi M, Rosychuk RJ (2012). Hierarchical Bayesian Spatio-temporal Analysis of Childhood Cancer Trends. Geographical Analysis, 44, 109-120.