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Sample size determination in bioequivalence studies using statistical assurance.

A Ring1,2, B Lang3, C Kazaroho4

  • 1University of the Free State, Bloemfontein, South Africa.

British Journal of Clinical Pharmacology
|July 6, 2019
PubMed
Summary
This summary is machine-generated.

Bioequivalence trials can now quantify uncertainty in drug formulation T/R-ratios using statistical assurance. This method provides a probability of success independent of specific ratio assumptions, improving trial design.

Keywords:
bioequivalencecrossover trialsample size determinationstatistical powertrial design

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Area of Science:

  • Pharmacokinetics and Drug Development
  • Biostatistics and Clinical Trial Design
  • Pharmaceutical Sciences

Background:

  • Bioequivalence (BE) trials assess drug formulation similarity using pharmacokinetic metrics.
  • Traditional sample size calculations rely on fixed assumptions for the Test/Reference (T/R)-ratio, overlooking inherent uncertainty.
  • The T/R-ratio's exact value is unknown pre-trial, often necessitating a 5% difference assumption.

Purpose of the Study:

  • To introduce and evaluate the concept of statistical assurance for Bioequivalence (BE) trials.
  • To characterize uncertainty in the T/R-ratio using a distribution for the log(T/R)-ratio.
  • To provide a method for assessing the probability of BE trial success that accounts for input parameter variability.

Main Methods:

  • Statistical assurance is derived by integrating trial power over the distribution of the log(T/R)-ratio.
  • A normal distribution with assumed mean log(θ)=0 and standard deviation σu quantifies the uncertainty in the T/R-ratio.
  • The relationship between power and assurance is analyzed through numerical comparisons.

Main Results:

  • The assurance concept allows uncertainty to be directly expressed as a variability parameter.
  • For practical scenarios (0.95 ≤ θ ≤ 1.05), sample size is minimally affected when σ = |log(θ)|.
  • Assurance offers a probability of success measure independent of specific T/R-ratio values.

Conclusions:

  • Statistical assurance enhances BE trial design by directly incorporating T/R-ratio uncertainty.
  • This approach provides a more robust measure of trial success probability.
  • Assurance is a valuable tool for optimizing sample size and improving the reliability of BE trial outcomes.