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Optimal Experimental Design Strategies for Detecting Hormesis

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  • Holger Dette
  • Andrey Pepelyshev
  • Weng Kee Wong

Abstract

Hormesis is a widely observed phenomenon in many branches of life sciences, ranging from toxicology studies to agronomy, with obvious public health and risk assessment implications. We address optimal experimental design strategies for determining the presence of hormesis in a controlled environment using the recently proposed Hunt‐Bowman model. We propose alternative models that have an implicit hormetic threshold, discuss their advantages over current models, and construct and study properties of optimal designs for (i) estimating model parameters, (ii) estimating the threshold dose, and (iii) testing for the presence of hormesis. We also determine maximin optimal designs that maximize the minimum of the design efficiencies when we have multiple design criteria or there is model uncertainty where we have a few plausible models of interest. We apply these optimal design strategies to a teratology study and show that the proposed designs outperform the implemented design by a wide margin for many situations.

Suggested Citation

  • Holger Dette & Andrey Pepelyshev & Weng Kee Wong, 2011. "Optimal Experimental Design Strategies for Detecting Hormesis," Risk Analysis, John Wiley & Sons, vol. 31(12), pages 1949-1960, December.
  • Handle: RePEc:wly:riskan:v:31:y:2011:i:12:p:1949-1960
    DOI: 10.1111/j.1539-6924.2011.01625.x
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    References listed on IDEAS

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    1. Pronzato, Luc, 2009. "Asymptotic properties of nonlinear estimates in stochastic models with finite design space," Statistics & Probability Letters, Elsevier, vol. 79(21), pages 2307-2313, November.
    2. 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.
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    Cited by:

    1. Steven Kim & Jeffrey Wand & Christina Magana‐Ramirez & Jenel Fraij, 2021. "Logistic Regression Models with Unspecified Low Dose–Response Relationships and Experimental Designs for Hormesis Studies," Risk Analysis, John Wiley & Sons, vol. 41(1), pages 92-109, January.
    2. Steven B Kim & Dong Sub Kim & Christina Magana-Ramirez, 2021. "Applications of statistical experimental designs to improve statistical inference in weed management," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-21, September.
    3. Regina G Belz & Hans-Peter Piepho, 2012. "Modeling Effective Dosages in Hormetic Dose-Response Studies," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-10, March.

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