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Activity Number: 292
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Risk Analysis
Abstract - #304373
Title: Nonparametric Bayesian Methods for Benchmark Dose Estimation
Author(s): Anindya Roy*+ and Nilabja Guha and Leonid Kopylev and Maria Spassova and John Fox and Paul White
Companies: University of Maryland Baltimore County and University of Maryland Baltimore County and EPA and EPA/NCEA and EPA/NCEA and EPA/NCEA
Address: Department of Mathematics and Statistics, Baltimore, MD, 21250-0001, United States
Keywords: BMDS software ; BMDL ; Dose-response ; Dirichlet distribution ; Integrated Brownian motion

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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