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Nonparametric Bayesian Methods for Benchmark Dose Estimation

Author

Listed:
  • Nilabja Guha
  • Anindya Roy
  • Leonid Kopylev
  • John Fox
  • Maria Spassova
  • Paul White

Abstract

The article proposes and investigates the performance of two Bayesian nonparametric 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 model‐averaging approach and a frequentist nonparametric approach. These comparisons together with simulation studies suggest that the nonparametric methods provide a lot of flexibility in terms of model fit and can be a very useful tool in benchmark dose estimation studies, especially when standard parametric models fail to fit to the data adequately.

Suggested Citation

  • Nilabja Guha & Anindya Roy & Leonid Kopylev & John Fox & Maria Spassova & Paul White, 2013. "Nonparametric Bayesian Methods for Benchmark Dose Estimation," Risk Analysis, John Wiley & Sons, vol. 33(9), pages 1608-1619, September.
    • Handle: RePEc:wly:riskan:v:33:y:2013:i:9:p:1608-1619
    DOI: 10.1111/risa.12004
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    References listed on IDEAS

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    Cited by:

    1. Walter W. Piegorsch & Hui Xiong & Rabi N. Bhattacharya & Lizhen Lin, 2014. "Benchmark Dose Analysis via Nonparametric Regression Modeling," Risk Analysis, John Wiley & Sons, vol. 34(1), pages 135-151, January.
    2. Signe M. Jensen & Felix M. Kluxen & Christian Ritz, 2019. "A Review of Recent Advances in Benchmark Dose Methodology," Risk Analysis, John Wiley & Sons, vol. 39(10), pages 2295-2315, October.
    3. Matthew W. Wheeler & Todd Blessinger & Kan Shao & Bruce C. Allen & Louis Olszyk & J. Allen Davis & Jeffrey S Gift, 2020. "Quantitative Risk Assessment: Developing a Bayesian Approach to Dichotomous Dose–Response Uncertainty," Risk Analysis, John Wiley & Sons, vol. 40(9), pages 1706-1722, September.

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