Abstract
Cancer cluster detection in small communities is an important but complicated field of cancer epidemiology, due to large statistical errors of both types associated with the detection. In this paper, authors show the use of a new approach to this problem. This approach is based on three complementary techniques. One is aimed at detection of the cluster, and two others are applied after cluster detection in order to confirm or reject the cluster. Included is application of the approach in small agricultural-industrial communities of the South of Israel. The approach reduces both types of statistical errors, increases the chance to detect a true clustering and enables a first step in the identification of the cause of a cluster detected.
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Kordysh, E., Bolotin, A., Barchana, M., Chen, R. (2001). Cancer Epidemiology of Small Communities: Using a Novel Approach to Detecting Clusters. In: Crespo, J., Maojo, V., Martin, F. (eds) Medical Data Analysis. ISMDA 2001. Lecture Notes in Computer Science, vol 2199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45497-7_19
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DOI: https://doi.org/10.1007/3-540-45497-7_19
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