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RABOTS: A Randomized Adaptive Bayesian Optimization Two-Stage Seamless Design for Dose Optimization and

Xiaochen Zhu1, Yiwei Li1, Jiaju Wu1

  • 1Global Biometrics & Data Sciences, Bristol Myers Squibb, Princeton, New Jersey, USA.

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Summary

This study introduces a new clinical trial design, Randomized Adaptive Bayesian Optimization Two-stage Seamless (RABOTS), for optimizing drug doses and assessing early efficacy. The RABOTS design effectively identifies optimal doses while prioritizing patient safety and enhancing statistical efficiency.

Keywords:
Bayesian methodclinical trialdose optimizationdynamic information borrowingproof‐of‐conceptseamless adaptive design

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Pharmacometrics
  • Oncology Drug Development

Background:

  • Dose optimization (DO) is crucial in clinical trials to balance drug efficacy and toxicity.
  • Current methods may lack efficiency in integrating DO with early efficacy assessments.

Purpose of the Study:

  • Propose and evaluate the Randomized Adaptive Bayesian Optimization Two-stage Seamless (RABOTS) design.
  • Integrate dose optimization with proof-of-concept (PoC) evaluation in clinical trials.
  • Enhance statistical efficiency through dynamic information borrowing.

Main Methods:

  • Stage I: Adaptive dose selection to eliminate sub-optimal doses.
  • Stage II: Bayesian Go/No-Go criterion for Go/No-Go decisions.
  • Dynamic borrowing approach to leverage information from dropped doses.
  • Simulation scenarios to evaluate performance across different borrowing methods.

Main Results:

  • RABOTS reliably identifies superior doses and favors lower doses when efficacy is similar, prioritizing safety.
  • The SAM prior borrowing method balances Bayesian power and Type I error rate.
  • Key parameters like clinically meaningful effect threshold and Stage I sample size influence operating characteristics.

Conclusions:

  • RABOTS provides a flexible and efficient framework for dose optimization and early efficacy assessment in oncology.
  • The design effectively integrates dose selection and proof-of-concept evaluation.
  • Future work may explore complex dose structures and alternative endpoints.