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November 1996 Bioequivalence trials, intersection-union tests and equivalence confidence sets
Roger L. Berger, Jason C. Hsu
Statist. Sci. 11(4): 283-319 (November 1996). DOI: 10.1214/ss/1032280304

Abstract

The bioequivalence problem is of practical importance because the approval of most generic drugs in the United States and the European Community (EC) requires the establishment of bioequivalence between the brand-name drug and the proposed generic version. The problem is theoretically interesting because it has been recognized as one for which the desired inference, instead of the usual significant difference, is practical equivalence. The concept of intersection-union tests will be shown to clarify, simplify and unify bioequivalence testing. A test more powerful than the one currently specified by the FDA and EC guidelines will be derived. The claim that the bioequivalence problem defined in terms of the ratio of parameters is more difficult than the problem defined in terms of the difference of parameters will be refuted. The misconception that size-$\alpha$ bioequivalence tests generally correspond to $100(1 - 2 \alpha)%$ confidence sets will be shown to lead to incorrect statistical practices, and should be abandoned. Techniques for constructing $100(1 - \alpha)%$ confidence sets that correspond to size-$\alpha$ bioequivalence tests will be described. Finally, multiparameter bioequivalence problems will be discussed.

Citation

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Roger L. Berger. Jason C. Hsu. "Bioequivalence trials, intersection-union tests and equivalence confidence sets." Statist. Sci. 11 (4) 283 - 319, November 1996. https://doi.org/10.1214/ss/1032280304

Information

Published: November 1996
First available in Project Euclid: 17 September 2002

zbMATH: 0955.62555
MathSciNet: MR1445984
Digital Object Identifier: 10.1214/ss/1032280304

Keywords: bioavailability , Bioequivalence , Confidence interval , equivalence test , hypotheses test , intersection-union , level , pharmacokinetic , size , unbiased

Rights: Copyright © 1996 Institute of Mathematical Statistics

Vol.11 • No. 4 • November 1996
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