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Abstract Details
Activity Number:
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607
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 4, 2011 : 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 - #302909 |
Title:
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Bayesian Monotonic Semiparametric Benchmark Dose Analysis
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Author(s):
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Matthew W. Wheeler*+ and A. John Bailer
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Companies:
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Centers for Disease Control and Prevention/NIOSH and Miami University
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Address:
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4676 Columbia Parkway, Cincinnati, OH, 45226,
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Keywords:
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Benchmark dose estimation ;
Monotonic dose-response ;
Bayesian ;
Quantal response models ;
Dichotomous response
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Abstract:
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Quantitative risk assessment proceeds by first estimating a dose-response curve and then inverting this model to determine/estimate the dose that corresponds to some pre-specified level of exposure. The parametric form of the dose-response model often plays a large role in determining this dose. Consequently the choice of the proper model is a major source of uncertainty when estimating such endpoints. Though methods exist which attempt to incorporate the uncertainty by forming an estimate based upon all models considered, such methods often fail when the true model is on the edge of the space of models considered and can not be formed from a weighted sum of constituent models. We propose a semi-parametric model for modeling dose-response data and deriving a dose estimate. In this model no particular dose-response functional form is specified a-priori, and the only restriction on the model is that it be monotonic. We use this model to estimate the dose response curve from a long term cancer bioassay, as well as compare this to extant methods which account for uncertainty. We show,in a small simulation study, that the method is superior to the previous methods.
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