Document Detail


A model for space-time cluster detection using spatial clusters with flexible temporal risk patterns.
MedLine Citation:
PMID:  20564730     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Maps of estimated disease rates over multiple time periods are useful tools for gaining etiologic insights regarding potential exposures associated with specific locations and times. In this paper, we describe an extension of the Gangnon-Clayton model for spatial clustering to spatio-temporal data. As in the purely spatial model, a large set of circular regions of varying radii centered at observed locations are considered as potential clusters, e.g. subregions with a different pattern of risk than the remainder of the study region. Within the spatio-temporal model, no specific parametric form is imposed on the temporal pattern of risk within each cluster. In addition to the clusters, the proposed model incorporates spatial and spatio-temporal heterogeneity effects and can readily accommodate regional covariates. Inference is performed in a Bayesian framework using MCMC. Although formal inferences about the number of clusters could be obtained using a reversible jump MCMC algorithm, we use local Bayes factors from models with a fixed, but overly large, number of clusters to draw inferences about both the number and the locations of the clusters. We illustrate the approach with two applications of the model to data on female breast cancer mortality in Japan and evaluate its operating characteristics in a simulation study.
Authors:
Ronald E Gangnon
Publication Detail:
Type:  Comparative Study; Journal Article    
Journal Detail:
Title:  Statistics in medicine     Volume:  29     ISSN:  1097-0258     ISO Abbreviation:  Stat Med     Publication Date:  2010 Sep 
Date Detail:
Created Date:  2010-09-15     Completed Date:  2010-12-29     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8215016     Medline TA:  Stat Med     Country:  England    
Other Details:
Languages:  eng     Pagination:  2325-37     Citation Subset:  IM    
Affiliation:
Departments of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53726, USA. ronald@biostat.wisc.edu
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MeSH Terms
Descriptor/Qualifier:
Bayes Theorem*
Breast Neoplasms / mortality
Computer Simulation
Female
Humans
Japan
Models, Biological*
Models, Statistical*
Space-Time Clustering*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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