JSM 2004 - Toronto

Abstract #301503

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Activity Number: 332
Type: Topic Contributed
Date/Time: Wednesday, August 11, 2004 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #301503
Title: Bayesian Nonparametric ROC Curve Estimation
Author(s): Adam Branscum*+
Companies: University of California, Davis
Address: One Shields Ave., Davis, CA, 95616,
Keywords: Polya trees
Abstract:

Nonparametric and semiparametric models are used to provide flexible inferences for example relaxing unimodality and other consequences of strong distributional assumptions. A standard epidemiologic problem involves the estimation of receiver operating characteristic curves based on serologic data that have been collected on diseased and nondiseased individuals respectively. There are many frequentist parametric and nonparametric solutions to this problem in the literature. We illustrate the Bayesian nonparametric approach to ROC curve estimation, in the absence of a gold standard test based on a single sample of serologic outcomes with unknown disease status. In addition, we are able to estimate the mixing proportion, or prevalence of disease in the sampled population, using this approach.

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Revised March 2004