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Robust time selection for interim analysis in the Bayesian phase 2 exploratory clinical trial.

Bo Feng1, Benny Zee1

  • 1Division of Biostatistics, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, HKSAR, China.

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Summary

This study introduces a new utility function for Bayesian phase 2 clinical trials to optimize interim analysis timing. This method ensures robust Go/No-Go decisions, improving clinical trial efficiency.

Keywords:
Bayesian statisticsUtility functioninterim analysisphase 2 exploratory clinical trialrobustness

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Area of Science:

  • Clinical Trials Methodology
  • Bayesian Statistics in Clinical Research
  • Drug Development Decision Making

Background:

  • Phase 2 clinical trials require timely Go/No-Go decisions during interim analysis (IA).
  • Optimal IA timing is crucial but often depends on specific alternative hypotheses.
  • Existing utility functions primarily focus on minimizing sample size or cost in confirmatory trials.

Purpose of the Study:

  • To propose a novel utility function for Bayesian phase 2 exploratory clinical trials.
  • To evaluate the predictability and robustness of Go/No-Go decisions made at IA.
  • To enable robust selection of IA timing irrespective of treatment effect assumptions.

Main Methods:

  • Development of a new utility function tailored for Bayesian phase 2 exploratory trials.
  • Evaluation of the proposed function's performance in decision-making.
  • Assessment of robustness across different treatment effect scenarios.

Main Results:

  • The proposed utility function facilitates robust time selection for interim analysis.
  • Decisions made during IA demonstrate enhanced predictability and robustness.
  • The method is effective regardless of underlying treatment effect assumptions.

Conclusions:

  • A new utility function offers a reliable approach for optimizing interim analysis timing in phase 2 trials.
  • This methodology enhances the decision-making process for Go/No-Go in early-phase drug development.
  • The proposed approach improves the efficiency and reliability of clinical trial design.