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Regional Consistency Evaluation and Sample Size Calculation Under Two MRCTs.

Kunhai Qing1, Xinru Ren1, Shuping Jiang2

  • 1School of Statistics, East China Normal University, Shanghai, China.

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|November 7, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a systematic approach for evaluating regional consistency in multiregional clinical trials (MRCTs). It provides methods for sample size calculation to ensure drug effectiveness is consistently demonstrated across regions for global registration.

Keywords:
MRCTconsistency assessmentsample size calculation

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Drug Development

Background:

  • Multiregional clinical trials (MRCTs) are essential for drug development and global registration.
  • Regulatory bodies like the FDA require substantial evidence of effectiveness for market approval.
  • Evaluating regional consistency is crucial when multiple MRCTs are needed to establish drug efficacy for a specific indication.

Purpose of the Study:

  • To present existing regional consistency evaluations in a unified framework.
  • To extend and evaluate consistency assessment criteria for two pivotal MRCTs.
  • To provide methods for regional sample size calculation under a fixed effects model.

Main Methods:

  • Unified presentation of existing regional consistency evaluation methods.
  • Extension of MHLW (2007) consistency assessment criteria for two MRCTs.
  • Development of regional sample size calculation methods and numerical studies for evaluation.

Main Results:

  • The proposed regional sample size calculation method achieves the desired probability of demonstrating regional consistency.
  • The extended consistency criteria provide a robust framework for evaluating regional effects in MRCTs.
  • Numerical studies validate the effectiveness of the proposed methods.

Conclusions:

  • A systematic approach for regional consistency evaluation and sample size calculation in MRCTs is presented.
  • The methods facilitate drug development and global registration by ensuring consistent evidence of effectiveness.
  • An R package is provided for practical implementation of the proposed methods.