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Activity Number:
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113
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Epidemiology
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| Abstract - #309642 |
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Title:
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Power Study of a Semiparametric Cluster Detection Method
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Author(s):
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Shihua Wen*+ and Benjamin Kedem
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Companies:
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University of Maryland and University of Maryland
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Address:
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Mathematics Building, College Park, MD, 20742,
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Keywords:
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semi-parametric density ratio model ; false discovery rate ; q-value ; cluster detection ; scan statistics ; Combined data
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Abstract:
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A semi-parametric density ratio method which borrows strength from two or more samples can be applied to moving windows of variable size in cluster detection. The method requires neither the prior knowledge of the underlying distribution nor the number of cases before scanning. In this paper, the semi-parametric cluster detection procedure combined with controlling of the false discovery rate (FDR) for multiple testing is studied. It is shown by simulations that for binary data, using Kulldorff's Northeastern benchmark data, the semi-parametric method and Kulldorff's method performs similarly. When the data are not binary, the semi-parametric methodology still works in many cases, but Kulldorff's method requires to choose the correct scan statistic in order to achieve power comparable to that achieved by the semi-parametric method.
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