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Activity Number:
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294
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2007 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #309491 |
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Title:
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A Bayesian Nonparametric Multivariate Model for Evaluation of Correlated Diagnostic Tests
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Author(s):
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Adam Branscum*+ and Timothy E. Hanson and Ian Gardner
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Companies:
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University of Kentucky and The University of Minnesota and University of California, Davis
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Address:
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College of Public Health, Lexington, KY, 40536,
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Keywords:
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Bayesian nonparametrics ; Polya trees ; Diagnostic tests
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Abstract:
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We develop a flexible nonparametric model for the statistical analysis of multivariate serologic data. In this setting, sampled individuals are tested for a condition of interest using several imperfect continuous diagnostic tests. To provide for a degree of robustness to the structure imposed by multivariate normal models, the data for diseased and non-diseased individuals are modeled with independent multivariate mixtures of Polya trees. Data-driven estimates are available for ROC curves and areas under them for all diagnostic tests under consideration. We consider a tractable two-stage empirical Bayesian estimation procedure that is applied to the evaluation of two ELISA tests for Johne's disease.
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