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A note on the robustness of the continual reassessment method

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  • Azriel, David

Abstract

The continual reassessment method (CRM) is a model-based design that aims at finding the maximum tolerated dose (MTD) of a given drug. As the CRM is a model-based technique, its use may be restricted to cases where the true dose-response curve satisfies its underlying working model. Shen and O’Quigley (1996) prove that the CRM is consistent (converges to the MTD) for a family of dose-response curves that satisfies several quite restrictive criteria. Cheung and Chappell (2002) conjecture that the CRM is consistent under a much weaker set of conditions and test their conjecture by a simulation study, but do not provide a formal proof for their claim. The current note fills this gap and provides a formal proof for the conjecture of Cheung and Chappell, thus giving a solid justification for the robustness of the CRM for misspecified model.

Suggested Citation

  • Azriel, David, 2012. "A note on the robustness of the continual reassessment method," Statistics & Probability Letters, Elsevier, vol. 82(5), pages 902-906.
    • Handle: RePEc:eee:stapro:v:82:y:2012:i:5:p:902-906
    DOI: 10.1016/j.spl.2012.01.026
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    References listed on IDEAS

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    1. Ying Kuen Cheung & Rick Chappell, 2002. "A Simple Technique to Evaluate Model Sensitivity in the Continual Reassessment Method," Biometrics, The International Biometric Society, vol. 58(3), pages 671-674, September.
    2. 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.
    3. David Azriel & Micha Mandel & Yosef Rinott, 2010. "The Treatment Versus Experimentation Dilemma in Dose-finding Studies," Discussion Paper Series dp559, The Federmann Center for the Study of Rationality, the Hebrew University, Jerusalem.
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    Cited by:

    1. Azriel, David, 2014. "Optimal sequential designs in phase I studies," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 288-297.
    2. M. Clertant & J. O’Quigley, 2017. "Semiparametric dose finding methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1487-1508, November.

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