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Model Averaging Software for Dichotomous Dose Response Risk Estimation

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  • Wheeler, Matthew W.
  • Bailer, A. John

Abstract

Model averaging has been shown to be a useful method for incorporating model uncertainty in quantitative risk estimation. In certain circumstances this technique is computationally complex, requiring sophisticated software to carry out the computation. We introduce software that implements model averaging for risk assessment based upon dichotomous dose-response data. This software, which we call Model Averaging for Dichotomous Response Benchmark Dose (MADr-BMD), fits the quantal response models, which are also used in the US Environmental Protection Agency benchmark dose software suite, and generates a model-averaged dose response model to generate benchmark dose and benchmark dose lower bound estimates. The software fulfills a need for risk assessors, allowing them to go beyond one single model in their risk assessments based on quantal data by focusing on a set of models that describes the experimental data.

Suggested Citation

  • Wheeler, Matthew W. & Bailer, A. John, 2008. "Model Averaging Software for Dichotomous Dose Response Risk Estimation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 26(i05).
    • Handle: RePEc:jss:jstsof:v:026:i05
    DOI: http://hdl.handle.net/10.18637/jss.v026.i05
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    Cited by:

    1. Matthew Wheeler & A. John Bailer, 2012. "Monotonic Bayesian Semiparametric Benchmark Dose Analysis," Risk Analysis, John Wiley & Sons, vol. 32(7), pages 1207-1218, July.
    2. Marc Aerts & Matthew W. Wheeler & José Cortiñas Abrahantes, 2020. "An extended and unified modeling framework for benchmark dose estimation for both continuous and binary data," Environmetrics, John Wiley & Sons, Ltd., vol. 31(7), November.
    3. Lizhen Lin & Walter W. Piegorsch & Rabi Bhattacharya, 2015. "Nonparametric Benchmark Dose Estimation with Continuous Dose-Response Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(3), pages 713-731, September.
    4. Mirjana Glisovic‐Bensa & Walter W. Piegorsch & Edward J. Bedrick, 2024. "Bayesian benchmark dose risk assessment with mixed‐factor quantal data," Environmetrics, John Wiley & Sons, Ltd., vol. 35(5), August.
    5. Matthew W. Wheeler & Walter W. Piegorsch & Albert John Bailer, 2019. "Quantal Risk Assessment Database: A Database for Exploring Patterns in Quantal Dose‐Response Data in Risk Assessment and its Application to Develop Priors for Bayesian Dose‐Response Analysis," Risk Analysis, John Wiley & Sons, vol. 39(3), pages 616-629, March.
    6. 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.
    7. 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.

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