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Activity Number:
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525
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Type:
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Contributed
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Date/Time:
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Wednesday, August 5, 2009 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #305156 |
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Title:
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Estimation of Disease Prevalence from Imperfect Diagnostic Tests on Pooled Samples with Varying Pool Sizes
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Author(s):
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Christopher J. Williams*+
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Companies:
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University of Idaho
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Address:
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P.O. Box 441104, Moscow, ID, 83844-1104,
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Keywords:
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Bayesian methods ; sensitivity ; specificity ; WINBUGS
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Abstract:
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This research is motivated by estimation of disease prevalence in fish populations, using grouped (pooled) data of different sizes, with imperfect diagnostic tests. Bayesian methods currently in use for this problem typically assume one uniform pool size for all samples. We present a method for estimation of disease prevalence from diagnostic tests in which sensitivity and/or specificity are not perfect, with pooled data collected from a variety of pool sizes. We use a Bayesian approach which is implemented in WINBUGS. We provide examples and perform efficiency calculations to investigate the performance of these estimators relative to maximum likelihood estimators that assume perfect diagnostic tests. Our results illustrate that these estimators are often more efficient than estimators assuming perfect tests.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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