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Adaptive Cohort Size Determination Method for Bayesian Optimal Interval Phase I/II Design to Shorten Clinical Trial

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This study introduces an adaptive cohort size method for oncology trials. It shortens trial duration by increasing cohort size using real-time data while maintaining accurate optimal biologic dose selection.

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Area of Science:

  • Clinical Trials
  • Oncology
  • Biostatistics

Background:

  • Dose optimization in oncology increasingly uses phase II randomized controlled trials.
  • Optimal biologic dose (OBD) selection from phase I data is crucial for phase II trials.
  • Current OBD trials with fixed cohort sizes lead to extended trial durations.

Purpose of the Study:

  • To propose a novel adaptive cohort size determination method for phase I oncology trials.
  • To shorten trial duration for OBD selection while maintaining accuracy.
  • To improve the efficiency of dose-finding studies in cancer research.

Main Methods:

  • Developed an adaptive cohort size method based on desirability probability derived from toxicity and efficacy data.
  • Utilized a pre-generated table for cohort size expansion, eliminating real-time desirability probability calculation.
  • Evaluated the method's performance through a simulation study across 16 scenarios.

Main Results:

  • The proposed method reduced trial duration by an average of 20% compared to conventional designs.
  • The accuracy of optimal biologic dose selection remained comparable to traditional methods.
  • Simulation results demonstrated the method's effectiveness in diverse scenarios.

Conclusions:

  • The adaptive cohort size determination method effectively reduces trial duration in oncology dose-finding studies.
  • This approach maintains the accuracy of optimal biologic dose selection.
  • The method offers a more efficient strategy for clinical trial design in oncology.