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Bayesian optimal interval design for phase I oncology clinical trials

Author

Listed:
  • Bryan M. Fellman

    (MD Anderson Cancer Center)

  • Ying Yuan

    (MD Anderson Cancer Center)

Abstract

The Bayesian optimal interval (BOIN) design is a novel phase I trial design for finding the maximum tolerated dose (MTD). With the BOIN design, phase I trials are conducted as a sequence of decision-making steps for assigning an appropriate dose for each enrolled patient. The design optimizes the assignment of doses to patients by minimizing incorrect decisions of dose escalation or deescalation; that is, it decreases the chance of erroneously escalating or de-escalating the dose when the current dose is higher or lower than the MTD. This feature of the BOIN design strongly ensures adherence to ethical standards. The most prominent advantage of the BOIN design is that it simultaneously achieves design simplicity and superior performance in comparison with similar methods. The BOIN design can be implemented in a simple way that is similar to the 3 + 3 design, but it yields substantially better operating characteristics. Compared with the well-known continual reassessment method, the BOIN design yields average performance when selecting the MTD, but it has a substantially lower risk of assigning patients to subtherapeutic or overly toxic doses. In this article, we present a command (optinterval) for implementing the BOIN design in a phase I clinical trial setting. Copyright 2015 by StataCorp LP.

Suggested Citation

  • Bryan M. Fellman & Ying Yuan, 2015. "Bayesian optimal interval design for phase I oncology clinical trials," Stata Journal, StataCorp LP, vol. 15(1), pages 110-120, March.
    • Handle: RePEc:tsj:stataj:v:15:y:2015:i:1:p:110-120
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    Citations

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    Cited by:

    1. Qingyang Liu & Junxian Geng & Frank Fleischer & Qiqi Deng, 2022. "Efficacy-Driven Dose Finding with Toxicity Control in Phase I Oncology Studies," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(3), pages 413-431, December.
    2. Deborah Plana & Geoffrey Fell & Brian M. Alexander & Adam C. Palmer & Peter K. Sorger, 2022. "Cancer patient survival can be parametrized to improve trial precision and reveal time-dependent therapeutic effects," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    3. Yimei Li & Ying Yuan, 2020. "PA‐CRM: A continuous reassessment method for pediatric phase I oncology trials with concurrent adult trials," Biometrics, The International Biometric Society, vol. 76(4), pages 1364-1373, December.
    4. Souvik Banerjee & Triparna Bose & Vijay M. Patil & Atanu Bhattacharjee & Kumar Prabhash, 2023. "Bayesian Effective Biological Dose Determination in Immunotherapy Response Trial," Annals of Data Science, Springer, vol. 10(1), pages 209-223, February.
    5. Tianjian Zhou & Wentian Guo & Yuan Ji, 2020. "PoD-TPI: Probability-of-Decision Toxicity Probability Interval Design to Accelerate Phase I Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(2), pages 124-145, July.
    6. Chen Li & Haitao Pan, 2020. "A phase I dose-finding design with incorporation of historical information and adaptive shrinking boundaries," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-18, August.

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