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Performance Measures in Dose‐Finding Experiments

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  • Nancy Flournoy
  • José Moler
  • Fernando Plo

Abstract

In the first phase of pharmaceutical development, and assuming that the probability of positive response increases with dose, the main statistical goal is to estimate a percentile of the dose–response function for a given target value Γ. We compare the Maximum Likelihood and centred isotonic regression estimators of the target dose and we discuss several performance criteria to assess inferential precision, the amount of toxicity exposure and the trade‐off between them for a set of some exemplary adaptive designs. We compare these designs using graphical tools. Several scenarios are considered using simulation, including the use of several start‐up rules, the change of slope of the dose‐toxicity function at the target dose and also different theoretical models, as logistic, normal or skew‐normal distribution functions.

Suggested Citation

  • Nancy Flournoy & José Moler & Fernando Plo, 2020. "Performance Measures in Dose‐Finding Experiments," International Statistical Review, International Statistical Institute, vol. 88(3), pages 728-751, December.
    • Handle: RePEc:bla:istatr:v:88:y:2020:i:3:p:728-751
    DOI: 10.1111/insr.12363
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    References listed on IDEAS

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    1. Sweeting, Michael & Mander, Adrian & Sabin, Tony, 2013. "bcrm: Bayesian Continual Reassessment Method Designs for Phase I Dose-Finding Trials," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 54(i13).
    2. Márcio Augusto Diniz & Mourad Tighiouart & André Rogatko, 2019. "Comparison between continuous and discrete doses for model based designs in cancer dose finding," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-15, January.
    3. Reiner, Ethan & Paoletti, Xavier & O'Quigley, John, 1999. "Operating characteristics of the standard phase I clinical trial design," Computational Statistics & Data Analysis, Elsevier, vol. 30(3), pages 303-315, May.
    4. Mario Stylianou & Nancy Flournoy, 2002. "Dose Finding Using the Biased Coin Up-and-Down Design and Isotonic Regression," Biometrics, The International Biometric Society, vol. 58(1), pages 171-177, March.
    5. Oron Assaf P. & Azriel David & Hoff Peter D., 2011. "Dose-Finding Designs: The Role of Convergence Properties," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-17, October.
    6. Biedermann, Stefanie & Dette, Holger & Zhu, Wei, 2006. "Optimal Designs for DoseResponse Models With Restricted Design Spaces," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 747-759, June.
    Full references (including those not matched with items on IDEAS)

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