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A dose-finding design for phase I clinical trials based on Bayesian stochastic approximation.

Jin Xu1,2, Dapeng Zhang3, Rongji Mu4

  • 1School of Statistics, East China Normal University, 3663 North Zhongshan Road, 200062, Shanghai, China. jxu@stat.ecnu.edu.cn.

BMC Medical Research Methodology
|October 1, 2022
PubMed
Summary

This study introduces a new Bayesian dose-finding design for early-phase clinical trials, improving maximum tolerated dose selection efficiency and reducing costs. The novel method enhances accuracy, especially for early or middle dose ranges.

Keywords:
Adaptive designDose-findingMaximum tolerated doseStochastic approximation

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Pharmacology

Background:

  • Current phase I clinical trial dose-finding designs have limitations in efficiency and cost-effectiveness.
  • Existing methods achieve maximum tolerated dose (MTD) selection rates between 30-80% with approximately 30 subjects.

Purpose of the Study:

  • To develop a novel, efficient, and cost-saving dose-finding design for phase I clinical trials.
  • To improve the accuracy and safety of MTD selection in early-phase drug development.

Main Methods:

  • A Bayesian stochastic approximation approach is proposed for dose-finding.
  • The design incorporates local adaptive modeling, flexible toxicity targets, and optional historical data utilization.
  • Simulations were conducted to compare the proposed design against established methods.

Main Results:

  • The novel design improves MTD selection accuracy to approximately 60% in early to middle dose ranges.
  • Performance is comparable to existing methods in other scenarios.
  • A user-friendly online software package and decision tree are provided.

Conclusions:

  • The proposed Bayesian dose-finding design offers enhanced efficiency and cost savings for phase I clinical trials.
  • Application in future cancer trials can shorten development timelines and reduce expenses.