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Optimal designs for dose finding experiments in toxicity studies

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  • Dette, Holger
  • Pepelyshev, Andrey
  • Wong, Weng Kee

Abstract

We construct optimal designs for estimating fetal malformation rate, prenatal death rate and an overall toxicity index in a toxicology study under a broad range of model assumptions. We use Weibull distributions to model these rates and assume that the number of implants depend on the dose level. We study properties of the optimal designs when the intra-litter correlation coefficient depends on the dose levels in different ways. Locally optimal designs are found, along with robustified versions of the designs that are less sensitive to mis-specification in the nominal values of the model parameters. We also report e?ciencies of commonly used designs in toxicological experiments and efficiencies of the proposed optimal designs when the true rates have non-Weibull distributions. Optimal design strategies for ?nding multiple-objective designs in toxicology studies are outlined as well.

Suggested Citation

  • Dette, Holger & Pepelyshev, Andrey & Wong, Weng Kee, 2008. "Optimal designs for dose finding experiments in toxicity studies," Technical Reports 2008,09, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    • Handle: RePEc:zbw:sfb475:200809
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    1. Mirjam Moerbeek & Aldert H. Piersma & Wout Slob, 2004. "A Comparison of Three Methods for Calculating Confidence Intervals for the Benchmark Dose," Risk Analysis, John Wiley & Sons, vol. 24(1), pages 31-40, February.
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    3. Jim Giles, 2006. "Animal experiments under fire for poor design," Nature, Nature, vol. 444(7122), pages 981-981, December.
    4. D. Krewski & Y. Zhu, 1995. "A Simple Data Transformation for Estimating Benchmark Doses in Developmental Toxicity Experiments," Risk Analysis, John Wiley & Sons, vol. 15(1), pages 29-39, February.
    5. Obaid M. Al-Saidy & Walter W. Piegorsch & R. Webster West & Daniela K. Nitcheva, 2003. "Confidence Bands for Low-Dose Risk Estimation with Quantal Response Data," Biometrics, The International Biometric Society, vol. 59(4), pages 1056-1062, December.
    6. Y. Zhu & D. Krewski & W. H. Ross, 1994. "Dose‐Response Models for Correlated Multinomial Data from Developmental Toxicity Studies," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 43(4), pages 583-598, December.
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    Cited by:

    1. Dette, Holger & Pepelyshev, Andrey & Shpilev, Piter & Wong, Weng Kee, 2009. "Optimal designs for estimating critical effective dose under model uncertainty in a dose response study," Technical Reports 2009,07, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    2. Jiajing Xu & Guosheng Yin & David Ohlssen & Frank Bretz, 2016. "Bayesian two-stage dose finding for cytostatic agents via model adaptation," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(3), pages 465-482, April.
    3. Dette, Holger & Pepelyshev, Andrey & Shpilev, Piter & Wong, Weng Kee, 2009. "Optimal designs for estimating critical effective dose under model uncertainty in a dose response study," Technical Reports 2009,09, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    4. Holger Dette & Laura Hoyden & Sonja Kuhnt & Kirsten Schorning, 2017. "Optimal designs for thermal spraying," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(1), pages 53-72, January.
    5. Dette, Holger & Holland-Letz, Tim, 2008. "A geometric characterization of c-optimal designs for heteroscedastic regression," Technical Reports 2008,26, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    6. Bretz, Frank & Dette, Holger & Pinheiro, José, 2008. "Practical considerations for optimal designs in clinical dose finding studies," Technical Reports 2008,22, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

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