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Optimal designs for regional bridging studies using the Bayesian power prior method.

Mario Nagase1, Shinya Ueda2, Mitsuo Higashimori2

  • 1Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Boston, USA.

Pharmaceutical Statistics
|August 27, 2019
PubMed
Summary

This study proposes a Bayesian power prior method to optimize bridging study designs. It guides the borrowing rate and sample size for new regional trials, using dapagliflozin for type 2 diabetes as an example.

Keywords:
international bridgingpower priorregulatory sciencereturn on investmentsample size determinationstudy design

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Area of Science:

  • Clinical trial methodology
  • Pharmacoeconomics
  • Regulatory science

Background:

  • Bridging studies are essential per ICH E5 guidelines to extend global clinical trial findings to new regions or subpopulations.
  • Balancing the need to assess regional differences with minimizing study duplication and expediting medical needs is a key challenge.
  • Leveraging historical data (borrowing) from global studies offers an efficient strategy to address these challenges.

Purpose of the Study:

  • To propose a practical framework for optimizing the design of regional bridging studies.
  • To determine the optimal borrowing rate and sample size for new regional trials.
  • To enhance the scientific and regulatory goals of bridging studies through efficient data utilization.

Main Methods:

  • Development of a Bayesian power prior method to address global/regional data exchangeability limitations.
  • Optimization of bridging study design incorporating a return on investment perspective.
  • Demonstration of the proposed method using clinical data from global and Japanese dapagliflozin trials for type 2 diabetes.

Main Results:

  • The proposed method provides guidance on the optimal borrowing rate (percentage of subjects from prior studies).
  • The approach assists in determining the appropriate number of subjects for new regional bridging studies.
  • The framework effectively balances the use of historical data with the need for new regional evidence.

Conclusions:

  • The Bayesian power prior method offers a robust approach to optimize bridging study designs.
  • This methodology facilitates efficient use of global data while ensuring regional relevance.
  • The findings support expedited access to new treatments by improving bridging study efficiency and design.