Abstract Details
Activity Number:
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74
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Type:
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Contributed
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Date/Time:
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Sunday, August 3, 2014 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #312561
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View Presentation
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Title:
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Bayesian Nonparametric Youden Index Modeling
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Author(s):
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Miguel de Carvalho*+ and Vanda Inacio and Adam Branscum
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Companies:
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Pontificia Universidad Católica de Chile and Pontificia Universidad Católica de Chile and Oregon State University
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Keywords:
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Bayesian nonparametrics ;
diagnostic testing ;
Dirichlet process mixtures ;
Markov chain Monte Carlo ;
Youden index
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Abstract:
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Accurate diagnosis of disease is of crucial importance in medical research.The accuracy of the test at any given threshold can be measured by the probability of a true positive (sensitivity) and a true negative (specificity).The receiver operating characteristic (ROC) curve plots the sensitivity, Se(c), against 1-specificity, 1-Sp(c), as the threshold c varies through the range of possible test results. To evaluate the discriminatory ability of a test it is common to summarize the information of the ROC curve into a single global value or index. In this work we focus on one of such indices, the Youden index (YI), which can be defined as YI = max_c{Se(c) + Sp(c)-1} and has the attractive feature of providing a criterion for choosing the optimal threshold value, c^{*}, to screen subjects in practice. With the aim of having a flexible model that can handle skewness, multimodality and other nonstandard features of the data, we propose to estimate the Youden index and its associated optimal threshold c^{*} using Dirichlet process mixtures. The performance of the estimator is evaluated through a simulation study and a real data application is provided.
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Authors who are presenting talks have a * after their name.
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