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Related Experiment Videos

Bootstrap methods for adaptive designs.

W F Rosenberger1, F Hu

  • 1Department of Mathematics and Statistics, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, Maryland 21250, USA.

Statistics in Medicine
|July 17, 1999
PubMed
Summary
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Adaptive clinical trial designs pose challenges for confidence interval estimation. This study introduces a simulation method using observed response rates to generate valid confidence intervals, even with complex trial features.

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Inference

Background:

  • Adaptive designs in clinical trials produce dependent random variables, complicating standard resampling methods for confidence intervals.
  • Traditional statistical methods may not adequately address the complexities introduced by adaptive sampling schemes.

Purpose of the Study:

  • To propose and evaluate a novel simulation-based procedure for constructing confidence intervals in adaptive clinical trials.
  • To assess the performance of bootstrap confidence intervals compared to asymptotic methods under adaptive designs.

Main Methods:

  • A simulation program was developed to generate sequences based on observed response rates from adaptive experiments.
  • Three bootstrap confidence intervals and one asymptotic confidence interval were compared via simulation.

Related Experiment Videos

  • The procedure was tested for two adaptive designs, considering factors like staggered entry and delayed response.

    • Main Results:

    • A simple ranking of simulated response rates provided a confidence interval approximation with coverage close to the nominal level (1-alpha) in most scenarios.
    • The proposed simulation method effectively handles complexities such as staggered entry and delayed response.
    • The approach demonstrated utility in a real-world clinical trial example for fluoxetine in depression.

    Conclusions:

    • The proposed simulation procedure offers a practical and effective method for confidence interval estimation in adaptive clinical trials.
    • This approach enhances the reliability of statistical inference for complex trial designs.
    • The method is valuable for incorporating real-world trial complexities into confidence interval calculations.