A comparison of the Two One-Sided Tests Procedure and the Power Approach for assessing the equivalence of average bioavailability

@article{Schuirmann2005ACO,
  title={A comparison of the Two One-Sided Tests Procedure and the Power Approach for assessing the equivalence of average bioavailability},
  author={Donald J. Schuirmann},
  journal={Journal of Pharmacokinetics and Biopharmaceutics},
  year={2005},
  volume={15},
  pages={657-680}
}
  • Donald J. Schuirmann
  • Published 1 December 1987
  • Mathematics
  • Journal of Pharmacokinetics and Biopharmaceutics
The statistical test of the hypothesis of no difference between the average bioavailabilities of two drug formulations, usually supplemented by an assessment of what the power of the statistical test would have been if the true averages had been inequivalent, continues to be used in the statistical analysis of bioavailability/bioequivalence studies. In the present article, this Power Approach (which in practice usually consists of testing the hypothesis of no difference at level 0.05 and… 
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Testing bioequivalence for multiple formulations with power and sample size calculations
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It is shown that given the same overall type I error and power, a BE crossover trial designed to test multiple formulations simultaneously only requires a small increase in the total sample size compared with a simple 2 × 2 crossover design evaluating only one test formulation.
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TLDR
The test statistics and the power function for the ratio of mean differences hypothesis and the required sample size for a three-arm clinical trial are derived and compared by the traditional mean difference equivalence test.
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TLDR
A general linear model approach that incorporates the within-subject covariance structure for making inferences on mean areas and is a cost-effective, viable alternative to the traditional method with superior inferential properties is proposed.
Asymptotic properties of the two one-sided t-tests – new insights and the Schuirmann-constant
Abstract The two one-sided t-tests (TOST) method is the most popular statistical equivalence test with many areas of application, i.e., in the pharmaceutical industry. Proper sample size calculation
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