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Abstract Details
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Activity Number:
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292
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Risk Analysis
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| Abstract - #304373 |
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Title:
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Nonparametric Bayesian Methods for Benchmark Dose Estimation
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Author(s):
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Anindya Roy*+ and Nilabja Guha and Leonid Kopylev and Maria Spassova and John Fox and Paul White
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Companies:
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University of Maryland Baltimore County and University of Maryland Baltimore County and EPA and EPA/NCEA and EPA/NCEA and EPA/NCEA
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Address:
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Department of Mathematics and Statistics, Baltimore, MD, 21250-0001, United States
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Keywords:
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BMDS software ;
BMDL ;
Dose-response ;
Dirichlet distribution ;
Integrated Brownian motion
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Abstract:
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We propose and investigate the performance of two Bayesian non-parametric estimation procedures in the context of benchmark dose estimation in toxicological animal experiments. The methodology is illustrated using several existing animal dose-response data sets and is compared with traditional parametric methods available in standard benchmark dose estimation software (BMDS), as well as with a published modelaveraging approach. These comparisons together with simulation studies suggest that the nonparametric methods provide a lot of exibility in terms of model t and can be a very useful tool in benchmark dose estimation studies, especially when standard parametric models fail to t to the data adequately.
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