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Cluster Detection Methods Applied to the Upper Cape Cod Cancer Data

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dc.contributor.author Ozonoff, Al en_US
dc.contributor.author Webster, Thomas en_US
dc.contributor.author Vieira, Veronica en_US
dc.contributor.author Weinberg, Janice en_US
dc.contributor.author Ozonoff, David en_US
dc.contributor.author Aschengrau, Ann en_US
dc.date.accessioned 2011-12-29T22:21:55Z
dc.date.available 2011-12-29T22:21:55Z
dc.date.copyright 2005 en_US
dc.date.issued 2005-9-15 en_US
dc.identifier.citation Ozonoff, Al, Thomas Webster, Veronica Vieira, Janice Weinberg, David Ozonoff, Ann Aschengrau. "Cluster detection methods applied to the Upper Cape Cod cancer data" Environmental Health 4:19. (2005) en_US
dc.identifier.issn 1476-069X en_US
dc.identifier.uri http://hdl.handle.net/2144/2579
dc.description.abstract BACKGROUND: A variety of statistical methods have been suggested to assess the degree and/or the location of spatial clustering of disease cases. However, there is relatively little in the literature devoted to comparison and critique of different methods. Most of the available comparative studies rely on simulated data rather than real data sets. METHODS: We have chosen three methods currently used for examining spatial disease patterns: the M-statistic of Bonetti and Pagano; the Generalized Additive Model (GAM) method as applied by Webster; and Kulldorff's spatial scan statistic. We apply these statistics to analyze breast cancer data from the Upper Cape Cancer Incidence Study using three different latency assumptions. RESULTS: The three different latency assumptions produced three different spatial patterns of cases and controls. For 20 year latency, all three methods generally concur. However, for 15 year latency and no latency assumptions, the methods produce different results when testing for global clustering. CONCLUSION: The comparative analyses of real data sets by different statistical methods provides insight into directions for further research. We suggest a research program designed around examining real data sets to guide focused investigation of relevant features using simulated data, for the purpose of understanding how to interpret statistical methods applied to epidemiological data with a spatial component. en_US
dc.description.sponsorship National Institutes of Health (RO1-AI28076); National Library of Medicine (RO1-LM007677); Superfund Basic Research Program (5P42ES 07381) en_US
dc.language.iso en en_US
dc.publisher BioMed Central en_US
dc.rights Copyright 2005 Ozonoff et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution 2.0 License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. en_US
dc.rights.uri http://creativecommons.org/licenses/by/2.0 en_US
dc.title Cluster Detection Methods Applied to the Upper Cape Cod Cancer Data en_US
dc.type article en_US
dc.identifier.doi 10.1186/1476-069X-4-19 en_US
dc.identifier.pubmedid 16164750 en_US
dc.identifier.pmcid 1242352 en_US


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Copyright 2005 Ozonoff et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution 2.0 License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Except where otherwise noted, this item's license is described as Copyright 2005 Ozonoff et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution 2.0 License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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