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A web-based tool for designing experimental studies to detect hormesis and estimate the threshold dose

Author

Listed:
  • Víctor Casero-Alonso

    (University of Castilla–La Mancha)

  • Andrey Pepelyshev

    (Cardiff University
    St. Petersburg State University)

  • Weng K. Wong

    (University of California at Los Angeles)

Abstract

Hormesis has been widely observed and debated in a variety of context in biomedicine and toxicological sciences. Detecting its presence can be an important problem with wide ranging implications. However, there is little work on constructing an efficient experiment to detect its existence or estimate the threshold dose. We use optimal design theory to develop a variety of locally optimal designs to detect hormesis, estimate the threshold dose and the zero-equivalent point (ZEP) for commonly used models in toxicology and risk assessment. To facilitate use of more efficient designs to detect hormesis, estimate threshold dose and estimate the ZEP in practice, we implement computer algorithms and create a user-friendly web site to help the biomedical researcher generate different types of optimal designs. The online tool facilitates the user to evaluate robustness properties of a selected design to various model assumptions and compare designs before implementation.

Suggested Citation

  • Víctor Casero-Alonso & Andrey Pepelyshev & Weng K. Wong, 2018. "A web-based tool for designing experimental studies to detect hormesis and estimate the threshold dose," Statistical Papers, Springer, vol. 59(4), pages 1307-1324, December.
    • Handle: RePEc:spr:stpapr:v:59:y:2018:i:4:d:10.1007_s00362-018-1038-5
    DOI: 10.1007/s00362-018-1038-5
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    References listed on IDEAS

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    1. J. López‐Fidalgo & C. Tommasi & P. C. Trandafir, 2007. "An optimal experimental design criterion for discriminating between non‐normal models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 231-242, April.
    2. Duarte, Belmiro P.M. & Sagnol, Guillaume & Wong, Weng Kee, 2018. "An algorithm based on semidefinite programming for finding minimax optimal designs," Computational Statistics & Data Analysis, Elsevier, vol. 119(C), pages 99-117.
    3. Wong, Weng Kee, 1994. "Comparing robust properties of A, D, E and G-optimal designs," Computational Statistics & Data Analysis, Elsevier, vol. 18(4), pages 441-448, November.
    4. Mariano Amo-Salas & Elvira Delgado-Márquez & Lenka Filová & Jesús López-Fidalgo, 2016. "Optimal designs for model discrimination and fitting for the flow of particles," Statistical Papers, Springer, vol. 57(4), pages 875-891, December.
    5. Daniel L. Hunt & Dale Bowman, 2004. "A Parametric Model for Detecting Hormetic Effects in Developmental Toxicity Studies," Risk Analysis, John Wiley & Sons, vol. 24(1), pages 65-72, February.
    6. Moerbeek, M., 2005. "Robustness properties of A-, D-, and E-optimal designs for polynomial growth models with autocorrelated errors," Computational Statistics & Data Analysis, Elsevier, vol. 48(4), pages 765-778, April.
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