Journal
STATISTICS IN MEDICINE
Volume 25, Issue 5, Pages 867-881Publisher
WILEY
DOI: 10.1002/sim.2424
Keywords
disease clustering; space-time modelling; Bayesian inference; reversible jump MCMC; DIC; breast cancer
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Modelling disease clustering over space and time can be helpful in providing indications of possible exposures and planning corresponding public health practices. Though a considerable number of studies focus on modelling spatio-temporal patterns of disease, most of them do not directly model a spatio-temporal clustering structure and could be ineffective for detecting clusters. In this paper, we extend a purely spatial cluster model to accommodate space-time clustering. Inference is performed in a Bayesian framework using reversible jump Markov chain Monte Carlo. This idea is illustrated using data on female breast cancer mortality from Japan. A hierarchical parametric space-time model for mapping disease is used for comparison. Copyright (c) 2006 John Wiley & Sons, Ltd.
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