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A Unified Decision Framework for Phase I Dose-Finding Designs

Author

Listed:
  • Yunshan Duan

    (University of Texas at Austin)

  • Shijie Yuan

    (Laiya Consulting, Inc.)

  • Yuan Ji

    (University of Chicago)

  • Peter Mueller

    (University of Texas at Austin)

Abstract

The purpose of a phase I dose-finding clinical trial is to investigate the toxicity profiles of various doses for a new drug and identify the maximum tolerate dose. Over the past three decades, various dose-finding designs have been proposed and discussed, including conventional model-based designs, new model-based designs using toxicity probability intervals, and rule-based designs. We present a simple decision framework that can generate several popular designs as special cases. We show that these designs share common elements under the framework, such as the same likelihood function, the use of the loss functions, and the nature of the optimal decisions as Bayes rules. They differ mostly in the choice of the prior distributions. We present theoretical results on the decision framework and its link to specific and popular designs like mTPI, BOIN, and CRM. These results provide useful insights into the similar theoretical foundations of these designs. We also show that the designs exhibit similar operating characteristics. Therefore, the choice of a design for a practical trial among the ones we reviewed may be up to the statistician’s and clinician’s own preference, such as preference of more model-based approach or more simple and transparent decisions.

Suggested Citation

  • Yunshan Duan & Shijie Yuan & Yuan Ji & Peter Mueller, 2024. "A Unified Decision Framework for Phase I Dose-Finding Designs," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(1), pages 69-85, April.
  • Handle: RePEc:spr:stabio:v:16:y:2024:i:1:d:10.1007_s12561-023-09379-5
    DOI: 10.1007/s12561-023-09379-5
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    References listed on IDEAS

    as
    1. Yuan, Ying & Yin, Guosheng, 2011. "Robust EM Continual Reassessment Method in Oncology Dose Finding," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 818-831.
    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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