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Related Experiment Videos

Sample size calculation for simulation-based multiple-testing procedures.

Heejung Bang1, Sin-Ho Jung, Stephen L George

  • 1Division of Biostatistics and Epidemiology, Department of Public Health, Weill Medical College of Cornell University, New York 10021, USA. heb2013@med.cornell.edu

Journal of Biopharmaceutical Statistics
|November 11, 2005
PubMed
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This study introduces a straightforward sample size and power calculation method for simulation-based multiple testing. It offers a sharper critical value than Bonferroni, ideal for correlated statistics in clinical trial design.

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Methodology

Background:

  • Standard multiple testing procedures, like Bonferroni, can be conservative, especially with correlated test statistics.
  • Accurate sample size and power calculations are crucial for efficient clinical trial design and reliable results.
  • Existing methods may not adequately address scenarios with highly correlated outcomes or multiple endpoints.

Purpose of the Study:

  • To present a simple and effective method for calculating sample size and statistical power for simulation-based multiple testing procedures.
  • To provide a sharper critical value compared to the traditional Bonferroni method, particularly beneficial for correlated data.
  • To offer a practical tool for researchers designing clinical trials with multiple or correlated endpoints.

Main Methods:

Related Experiment Videos

  • Development of a novel formula for sample size and power calculation based on simulation.
  • Comparison of the proposed method's critical value against the standard Bonferroni correction.
  • Application of the method to a real-world clinical trial scenario involving quality-of-life data.

Main Results:

  • The proposed simulation-based method yields a sharper critical value than the Bonferroni method.
  • The developed formula simplifies sample size and power calculations for complex multiple testing scenarios.
  • The method demonstrated utility in a quality-of-life study for early-stage prostate cancer patients.

Conclusions:

  • The presented method offers an improvement over standard techniques for multiple testing, especially with correlated statistics.
  • This approach provides a valuable tool for optimizing the design of clinical trials with multiple or dependent endpoints.
  • The methodology is broadly applicable to various statistical comparisons, including those involving multiple independent groups.