IDEAS home Printed from https://ideas.repec.org/a/wly/envmet/v31y2020i7ne2630.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/env.2630
    Download Restriction: no
    File URL: https://libkey.io/10.1002/env.2630?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. Signe M. Jensen & Christian Ritz, 2015. "Simultaneous Inference for Model Averaging of Derived Parameters," Risk Analysis, John Wiley & Sons, vol. 35(1), pages 68-76, January.
    4. Edsel A. Peña & Wensong Wu & Walter Piegorsch & Ronald W. West & LingLing An, 2017. "Model Selection and Estimation with Quantal‐Response Data in Benchmark Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 37(4), pages 716-732, April.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Matthew Wheeler & A. John Bailer, 2012. "Monotonic Bayesian Semiparametric Benchmark Dose Analysis," Risk Analysis, John Wiley & Sons, vol. 32(7), pages 1207-1218, July.
    10. 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.
    11. Jessica Kratchman & Bing Wang & John Fox & George Gray, 2018. "Correlation of Noncancer Benchmark Doses in Short‐ and Long‐Term Rodent Bioassays," Risk Analysis, John Wiley & Sons, vol. 38(5), pages 1052-1069, May.
    12. Kaatje Bollaerts & Marc Aerts & Christel Faes & Koen Grijspeerdt & Jeroen Dewulf & Koen Mintiens, 2008. "Human Salmonellosis: Estimation of Dose‐Illness from Outbreak Data," Risk Analysis, John Wiley & Sons, vol. 28(2), pages 427-440, April.
    13. Hojin Moon & Steven B. Kim & James J. Chen & Nysia I. George & Ralph L. Kodell, 2013. "Model Uncertainty and Model Averaging in the Estimation of Infectious Doses for Microbial Pathogens," Risk Analysis, John Wiley & Sons, vol. 33(2), pages 220-231, February.
    14. Fereshteh Kalantari & Joakim Ringblom & Salomon Sand & Mattias Öberg, 2017. "Influence of Distribution of Animals between Dose Groups on Estimated Benchmark Dose and Animal Distress for Quantal Responses," Risk Analysis, John Wiley & Sons, vol. 37(9), pages 1716-1728, September.
    15. Robert B. Noble & A. John Bailer & Robert Park, 2009. "Model‐Averaged Benchmark Concentration Estimates for Continuous Response Data Arising from Epidemiological Studies," Risk Analysis, John Wiley & Sons, vol. 29(4), pages 558-564, April.
    16. Maria A. Sans‐Fuentes & Walter W. Piegorsch, 2021. "Benchmark dose risk analysis with mixed‐factor quantal data in environmental risk assessment," Environmetrics, John Wiley & Sons, Ltd., vol. 32(5), August.
    17. Steven B. Kim & Ralph L. Kodell & Hojin Moon, 2014. "A Diversity Index for Model Space Selection in the Estimation of Benchmark and Infectious Doses via Model Averaging," Risk Analysis, John Wiley & Sons, vol. 34(3), pages 453-464, March.
    18. Matthew W. Wheeler & A. John Bailer & Tarah Cole & Robert M. Park & Kan Shao, 2017. "Bayesian Quantile Impairment Threshold Benchmark Dose Estimation for Continuous Endpoints," Risk Analysis, John Wiley & Sons, vol. 37(11), pages 2107-2118, November.
    19. Sushil B. Tamrakar & Anne Haluska & Charles N. Haas & Timothy A. Bartrand, 2011. "Dose‐Response Model of Coxiella burnetii (Q Fever)," Risk Analysis, John Wiley & Sons, vol. 31(1), pages 120-128, January.
    20. Kan Shao & Bruce C. Allen & Matthew W. Wheeler, 2017. "Bayesian Hierarchical Structure for Quantifying Population Variability to Inform Probabilistic Health Risk Assessments," Risk Analysis, John Wiley & Sons, vol. 37(10), pages 1865-1878, October.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:envmet:v:31:y:2020:i:7:n:e2630. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/1180-4009/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.