Show simple item record

dc.contributor.authorOzonoff, Alen_US
dc.contributor.authorWebster, Thomasen_US
dc.contributor.authorVieira, Veronicaen_US
dc.contributor.authorWeinberg, Janiceen_US
dc.contributor.authorOzonoff, Daviden_US
dc.contributor.authorAschengrau, Annen_US
dc.date.accessioned2011-12-29T22:21:55Z
dc.date.available2011-12-29T22:21:55Z
dc.date.copyright2005en_US
dc.date.issued2005-9-15en_US
dc.identifier.citationOzonoff, 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.issn1476-069Xen_US
dc.identifier.urihttp://hdl.handle.net/2144/2579
dc.description.abstractBACKGROUND: 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.sponsorshipNational Institutes of Health (RO1-AI28076); National Library of Medicine (RO1-LM007677); Superfund Basic Research Program (5P42ES 07381)en_US
dc.language.isoenen_US
dc.publisherBioMed Centralen_US
dc.rightsCopyright 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.urihttp://creativecommons.org/licenses/by/2.0en_US
dc.titleCluster Detection Methods Applied to the Upper Cape Cod Cancer Dataen_US
dc.typearticleen_US
dc.identifier.doi10.1186/1476-069X-4-19en_US
dc.identifier.pubmedid16164750en_US
dc.identifier.pmcid1242352en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

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.