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Sample size recalculation in multicenter randomized controlled clinical trials based on noncomparative data.

Markus Harden1, Tim Friede1,2

  • 1Department of Medical Statistics, University Medical Centre Göttingen, Göttingen, Germany.

Biometrical Journal. Biometrische Zeitschrift
|March 5, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a sample size recalculation method for multicenter trials. It helps maintain statistical power by adjusting for site variations, crucial for reliable clinical trial planning.

Keywords:
adaptive designhierarchical modelinternal pilot studylinear mixed model

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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Multicenter Study Design

Background:

  • Multicenter clinical trials present unique statistical challenges due to hierarchical data structures.
  • Recruiting subjects across multiple sites can lead to a loss of statistical power compared to single-center trials.
  • Accurate sample size determination is critical for the success of late-phase clinical trials.

Purpose of the Study:

  • To propose and evaluate a sample size recalculation procedure for multicenter trials with continuous endpoints.
  • To address the issue of power loss in multicenter trials by estimating nuisance parameters at interim stages.
  • To offer a robust method for sample size adjustment that accommodates non-comparative interim data and unbalanced center sizes.

Main Methods:

  • Development of a sample size recalculation procedure based on a mixed-effects model.
  • Estimation of nuisance parameters using non-comparative data at interim analysis.
  • Monte Carlo simulations to assess the performance of the procedure under various parameters like between-center heterogeneity and treatment effect size.
  • Comparison of unadjusted and bias-adjusted estimators for between-center heterogeneity.

Main Results:

  • The proposed sample size recalculation procedure, particularly with the unadjusted estimator, maintains type 1 error probability and statistical power close to nominal levels.
  • The unadjusted estimator for between-center heterogeneity demonstrated favorable operating characteristics in simulations.
  • The bias-adjusted estimator showed some inflation in type 1 error rate.
  • The methodology proved effective in a simulated diabetes management system trial.

Conclusions:

  • The developed sample size recalculation procedure is recommended for multicenter trials to mitigate risks associated with inaccurate nuisance parameter specification.
  • The method offers an advantage for interim sample size adjustments, especially when dealing with unbalanced data across centers.
  • This approach enhances the reliability and efficiency of planning for multicenter clinical studies.