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Immunology, Hematology, Oncology & Palliative Care (iHOPE)

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  1. Hierarchical Bayesian Spatio-Temporal Analysis of Childhood Cancer Trends [Download]

    Title: Hierarchical Bayesian Spatio-Temporal Analysis of Childhood Cancer Trends
    Creator: Torabi, Mahmoud
    Description: 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.
    Subjects: spatio-temporal models, hierarchical Bayesian, childhood cancer
  2. An examination of five spatial disease clustering methodologies for the identification of childhood cancer clusters in Alberta, Canada [Download]

    Title: An examination of five spatial disease clustering methodologies for the identification of childhood cancer clusters in Alberta, Canada
    Creator: Torabi, Mahmoud
    Description: Cluster detection is an important part of spatial epidemiology because it may help suggest potential factors associated with disease and thus, guide further investigation of the nature of diseases. Many different methods have been proposed to test for disease clusters. In this paper, we study five popular methods for detecting spatial clusters. These methods are Besag- Newell (BN), circular spatial scan statistic (CSS), flexible spatial scan statistic (FSS), Tango’s maximized excess events test (MEET), and Bayesian disease mapping (BYM). We study these five different methods by analyzing a data set of malignant cancer diagnoses in children in the province of Alberta, Canada during 1983-2004. Our results show that the potential clusters are located in the south-central part of the province. Although, all methods performed very well to detect clusters, the BN and MEET methods identified local as well as general clusters.
    Subjects: Bayesian statistic, cancer cases, geographic epidemiology, spatial cluster detection
    Date Created: 2011