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Activity Number:
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491
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Type:
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Invited
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Date/Time:
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Thursday, August 10, 2006 : 8:30 AM to 10:20 AM
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Sponsor:
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ENAR
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| Abstract - #305033 |
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Title:
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Bayesian Semiparametric Inferences for Disease Risk, ROC Curves, and Prevalence
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Author(s):
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Wesley O. Johnson*+ and Adam Branscum
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Companies:
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University of California, Irvine and University of Kentucky
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Address:
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Donald Bren School of Information and Computer Science, Irvine, CA, 92697-1250,
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Keywords:
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sensitivity ; specificity ; receiver operating characteristic curve ; diagnosis ; mixture model
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Abstract:
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We discuss the application of mixtures of Polya Trees to the semiparametric estimation of mixture distributions in the context of assessing disease risk and assessing the quality of a diagnostic procedure. Sampled individuals are either diseased or not, and their status is unknown. Diagnostic procedures result in a score from a corresponding mixture distribution where the mixing parameter is the prevalence of the disease. Bayesian nonparametric and semiparametric inferences are provided for Receiver Operating Characteristic curves and areas under them, as well as for prevalences. Covariates are modeled for the purpose of classification of individuals with unknown status as either "diseased" or "nondiseased" and incorporated as factors that might affect the quality of a diagnostic test.
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