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Sample size determination in step-up testing procedures for multiple comparisons with a control.

Koon Shing Kwong1, Siu Hung Cheung, Miin-Jye Wen

  • 1School of Economics, Singapore Management University, 90 Stamford Road, 178903 Singapore, Singapore. kskwong@smu.edu.sg

Statistics in Medicine
|August 28, 2010
PubMed
Summary
This summary is machine-generated.

Determining optimal sample size for step-up procedures in clinical trials is crucial. Square root allocation offers a more accurate sample size approximation than equal allocation, potentially reducing overall study requirements.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Power Analysis

Background:

  • Step-up procedures are effective for comparing multiple treatments against a control in clinical trials.
  • Accurate sample size determination is essential for achieving desired statistical power and efficient resource allocation.

Purpose of the Study:

  • To determine the optimal sample size for step-up procedures to achieve a pre-specified power level.
  • To evaluate different definitions of statistical power and their impact on sample size calculations.
  • To compare sample size allocation strategies, specifically equal versus square root allocation.

Main Methods:

  • The study discusses the determination of optimal sample size for step-up procedures.
  • It considers various definitions of statistical power, including all-pairs, any-pair, per-pair, and average power for one- and two-sided tests.
  • An extensive numerical study was conducted to compare allocation methods.

Main Results:

  • Square root allocation provides a better approximation of the optimal sample size compared to equal allocation.
  • Tables based on square root allocation are constructed for convenient sample size determination.
  • Optimal allocation can significantly reduce total sample size requirements in certain clinical study scenarios.

Conclusions:

  • Square root allocation is recommended for approximating optimal sample sizes in step-up procedures for clinical trials.
  • The findings offer practical tools (tables) for researchers to determine approximate sample sizes.
  • The study highlights the potential for substantial sample size reduction through optimized allocation strategies, particularly relevant for resource-constrained studies.