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Activity Number:
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299
- Risk Analysis: New Data, New Approaches, and New Interfaces
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Type:
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Invited
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Date/Time:
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Wednesday, August 11, 2021 : 3:30 PM to 5:20 PM
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Sponsor:
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Section on Risk Analysis
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Abstract #316735
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Title:
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Bayesian Dose-Response Analysis for Benchmark Dose Estimation in Chemical Risk Assessment
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Author(s):
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Kan Shao*
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Companies:
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Indiana University
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Keywords:
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Dose-response;
benchmark dose;
Bayesian;
Informative prior
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Abstract:
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The benchmark dose (BMD) methodology better utilizes dose-response data than the traditional NOAEL approach to estimate toxicity value in a more scientifically justifiable manner. Despite many advantages, the substantial potentials of the BMD approach have been considerably limited by prevailing modeling strategies and practice. The recently developed Bayesian benchmark dose (BBMD) modeling framework not only provides a useful tool for probabilistic dose-response assessment, but also offers an opportunity to enhance the reliability of BMD estimation. The presentation will introduce BMD analysis in a Bayesian framework followed by a discussion on the important features in the BBMD system, such as model-averaged BMD estimation. Then, the study will discuss how empirical informative prior (IP) derived from toxicological data may impact BMD estimation. Results show that IP can substantially reduce the uncertainty in BMD estimation when the IP is compatible with current data, but the accuracy can also be impaired due to limited flexibility of model shape imposed by IP. The results highlight the importance to use toxicologically based informative prior in dose-response assessment.
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Authors who are presenting talks have a * after their name.
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