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Related Concept Videos

Sample Size Calculation01:19

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Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
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Consistency Assessment and Regional Sample Size Calculation for MRCT Under Random Effects Model.

Xinru Ren1, Jin Xu1,2

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

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PubMed
Summary
This summary is machine-generated.

This study introduces a random effects model for multi-regional clinical trials (MRCTs) to assess regional treatment effect consistency. The proposed method effectively determines sample sizes, ensuring desired consistency probabilities for drug registration.

Keywords:
Bayesian shrinkage estimatorMRCTconsistency assessmentrandom effects modelregional sample size

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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Pharmaceutical Development

Background:

  • Multi-regional clinical trials (MRCTs) are standard for drug development and global registration.
  • Demonstrating regional consistency in treatment effects is crucial for regulatory approval.
  • Existing methods based on fixed effects models may not fully capture treatment effect heterogeneity.

Purpose of the Study:

  • To propose a random effects model for designing MRCTs and assessing regional consistency.
  • To develop methods for calculating overall sample size and regional sample fractions.
  • To provide theoretical properties for consistency probability assessment using an empirical shrinkage estimator.

Main Methods:

  • Utilizing a random effects model to account for treatment effect heterogeneity across regions.
  • Developing sample size determination methods for overall and regional allocation.
  • Applying an empirical shrinkage estimator for consistency probability assessment, based on MHLW Method 1.
  • Elaborating on applications for normal, binary, and survival endpoints.

Main Results:

  • The proposed random effects model effectively retains the desired consistency probability.
  • Simulation studies validate the method's performance across different endpoint types.
  • The empirical shrinkage estimator aids in determining regional sample sizes of interest.

Conclusions:

  • The random effects model offers a more effective approach for MRCT design and inference compared to fixed effects models.
  • The presented methodology provides a robust framework for ensuring regional consistency in drug development.
  • The R package facilitates the practical implementation of these advanced statistical methods in real-world trials.