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An extended and unified modeling framework for benchmark dose estimation for both continuous and binary data

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  • Marc Aerts
  • Matthew W. Wheeler
  • José Cortiñas Abrahantes

Abstract

Protection and safety authorities recommend the use of model averaging to determine the benchmark dose approach as a scientifically more advanced method compared with the no‐observed‐adverse‐effect‐level approach for obtaining a reference point and deriving health‐based guidance values. Model averaging however highly depends on the set of candidate dose–response models and such a set should be rich enough to ensure that a well‐fitting model is included. The currently applied set of candidate models for continuous endpoints is typically limited to two models, the exponential and Hill model, and differs completely from the richer set of candidate models currently used for binary endpoints. The objective of this article is to propose a general and wide framework of dose response models, which can be applied both to continuous and binary endpoints and covers the current models for both type of endpoints. In combination with the bootstrap, this framework offers a unified approach to benchmark dose estimation. The methodology is illustrated using two data sets, one with a continuous and another with a binary endpoint.

Suggested Citation

  • 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.
    • Handle: RePEc:wly:envmet:v:31:y:2020:i:7:n:e2630
    DOI: 10.1002/env.2630
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    References listed on IDEAS

    as
    1. 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).
    2. Kan Shao & Jeffrey S. Gift, 2014. "Model Uncertainty and Bayesian Model Averaged Benchmark Dose Estimation for Continuous Data," Risk Analysis, John Wiley & Sons, vol. 34(1), pages 101-120, January.
    3. Q. Fang & W. W. Piegorsch & K. Y. Barnes, 2015. "Bayesian benchmark dose analysis," Environmetrics, John Wiley & Sons, Ltd., vol. 26(5), pages 373-382, August.
    4. Linda J. Young & Melissa Whitney & Louise Ryan, 2013. "Uncertainty due to low‐dose extrapolation: modified BMD methodology for epidemiological data," Environmetrics, John Wiley & Sons, Ltd., vol. 24(5), pages 289-297, August.
    5. Lelys Bravo Guenni & Susan J. Simmons & R. Webster West & Walter W. Piegorsch & Edsel A. Peña & Lingling An & Wensong Wu & Alissa A. Wickens & Hui Xiong & Wenhai Chen, 2012. "The impact of model uncertainty on benchmark dose estimation," Environmetrics, John Wiley & Sons, Ltd., vol. 23(8), pages 706-716, December.
    6. Matthew W. Wheeler & Kan Shao & A. John Bailer, 2015. "Quantile benchmark dose estimation for continuous endpoints," Environmetrics, John Wiley & Sons, Ltd., vol. 26(5), pages 363-372, August.
    7. Christel Faes & Marc Aerts & Helena Geys & Geert Molenberghs, 2007. "Model Averaging Using Fractional Polynomials to Estimate a Safe Level of Exposure," Risk Analysis, John Wiley & Sons, vol. 27(1), pages 111-123, February.
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    1. Matthew W. Wheeler & Jose Cortiñas Abrahantes & Marc Aerts & Jeffery S. Gift & Jerry Allen Davis, 2022. "Continuous model averaging for benchmark dose analysis: Averaging over distributional forms," Environmetrics, John Wiley & Sons, Ltd., vol. 33(5), August.

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