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PoD-TPI: Probability-of-Decision Toxicity Probability Interval Design to Accelerate Phase I Trials

Author

Listed:
  • Tianjian Zhou

    (The University of Chicago)

  • Wentian Guo

    (Laiya Consulting, Inc.)

  • Yuan Ji

    (The University of Chicago)

Abstract

Cohort-based enrollment can slow down dose-finding trials since the outcomes of the previous cohort must be fully evaluated before the next cohort can be enrolled. This results in frequent suspension of patient enrollment. The issue is exacerbated in recent immune oncology trials where toxicity outcomes can take a long time to observe. We propose a novel phase I design, the probability-of-decision toxicity probability interval (PoD-TPI) design, to accelerate phase I trials. PoD-TPI enables dose assignment in real time in the presence of pending toxicity outcomes. With uncertain outcomes, the dose assignment decisions are treated as a random variable, and we calculate the posterior distribution of the decisions. The posterior distribution reflects the variability in the pending outcomes and allows a direct and intuitive evaluation of the confidence of all possible decisions. Optimal decisions are calculated based on 0-1 loss, and extra safety rules are constructed to enforce sufficient protection from exposing patients to risky doses. A new and useful feature of PoD-TPI is that it allows investigators and regulators to balance the trade-off between enrollment speed and making risky decisions by tuning a pair of intuitive design parameters. Through numerical studies, we evaluate the operating characteristics of PoD-TPI and demonstrate that PoD-TPI shortens trial duration and maintains trial safety and efficiency compared to existing time-to-event designs.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:stabio:v:12:y:2020:i:2:d:10.1007_s12561-019-09264-0
    DOI: 10.1007/s12561-019-09264-0
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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. 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.
    3. Ying Kuen Cheung & Rick Chappell, 2000. "Sequential Designs for Phase I Clinical Trials with Late-Onset Toxicities," Biometrics, The International Biometric Society, vol. 56(4), pages 1177-1182, December.
    4. Ruitao Lin, 2018. "Bayesian optimal interval design with multiple toxicity constraints," Biometrics, The International Biometric Society, vol. 74(4), pages 1320-1330, December.
    5. Suyu Liu & Ying Yuan, 2015. "Bayesian optimal interval designs for phase I clinical trials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 64(3), pages 507-523, April.
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    Cited by:

    1. Bo Huang & Naitee Ting, 2020. "Introduction to Special Issue on ‘Statistical Methods for Cancer Immunotherapy’," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(2), pages 79-82, July.

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