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Tests for inter-subject and total variabilities under crossover designs.

Yonghee Lee1, Jun Shao, Shein-Chung Chow

  • 1University of Wisconsin-Madison, Madison, Wisconsin, USA. ylee@statplusinc.com

Journal of Biopharmaceutical Statistics
|December 13, 2002
PubMed
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This study introduces a novel statistical test for crossover designs, addressing limitations of the standard F-test for comparing variabilities. The proposed method offers reliable type I error control for inter-subject and total variability analysis.

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Inference

Background:

  • Crossover designs are widely used in clinical trials, but standard statistical tests face limitations.
  • Estimators for inter-subject and total variability in crossover designs are not independent, precluding the use of the conventional F-test.
  • Accurate assessment of variability is crucial for treatment comparisons and sample size determination.

Purpose of the Study:

  • To develop and evaluate a statistical test for comparing inter-subject and total variabilities between treatments in replicated crossover designs.
  • To derive the asymptotic power of the proposed test and conduct a sensitivity analysis.
  • To compare methods for sample size calculation in crossover designs.

Main Methods:

  • Proposed a novel statistical test based on the extended modified large sample method for a 2 x 2 m replicated crossover design.

Related Experiment Videos

  • Derived asymptotic power and performed sensitivity analysis concerning inter-subject and intra-class correlations.
  • Evaluated two sample size calculation methods (Fisher-Cornish inversion vs. normal approximation) for a 2 x 4 crossover design.

    • Main Results:

    • The proposed test demonstrates satisfactory control of type I error in simulation studies.
    • Sensitivity analysis revealed how power is affected by correlation parameters.
    • The Fisher-Cornish inversion method for sample size calculation outperformed the normal approximation method.

    Conclusions:

    • The developed statistical test provides a valid approach for analyzing inter-subject and total variabilities in crossover designs.
    • The findings offer guidance on sample size calculations, favoring the Fisher-Cornish inversion method.
    • This research contributes to more robust statistical inference in clinical trial settings utilizing crossover designs.