This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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497
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 4, 2010 : 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 - #306722 |
Title:
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A Nonparametric Bayesian Approach to Biomarker Discovery
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Author(s):
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Peter Mueller*+ and Alejandro Jara
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Companies:
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MD Anderson Cancer Center and Pontificia Universidad Católica de Chile
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Address:
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, , ,
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Keywords:
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screening test ;
dirichlet process ;
nonparametric Bayes ;
biostatistics
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Abstract:
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We develop a nonparametric Bayesian model to implement retrospective validation and refinement of a biomarker panel.
We focus on two inference problems, (i) the specification of a screening test based on a panel of multiple biomarkers; and (ii) the assessment of operating characteristics summarized in an ROC curve. The motivating application is the development of a screening test for high risk ovarian cancer patients on the basis of a panel of 7 potential markers. The desired biomarker development is usually carried out in a multi-step process, using independent studies for steps (i) and (ii). Exploiting the flexible nature of non-parametric models allows a more honest representation of uncertainty about the distribution of biomarkers in the target population. This makes it possible to attempt joint inference for both steps in a single study. We use a DP mixture model.
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