Statistical Science

Bioequivalence trials, intersection-union tests and equivalence confidence sets

Roger L. Berger and Jason C. Hsu

Full-text: Open access

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.

Article information

Source
Statist. Sci., Volume 11, Number 4 (1996), 283-319.

Dates
First available in Project Euclid: 17 September 2002

Permanent link to this document
https://projecteuclid.org/euclid.ss/1032280304

Digital Object Identifier
doi:10.1214/ss/1032280304

Mathematical Reviews number (MathSciNet)
MR1445984

Zentralblatt MATH identifier
0955.62555

Keywords
Bioequivalence bioavailability hypotheses test confidence interval intersection-union size level equivalence test pharmacokinetic unbiased

Citation

Berger, Roger L.; Hsu, Jason C. Bioequivalence trials, intersection-union tests and equivalence confidence sets. Statist. Sci. 11 (1996), no. 4, 283--319. doi:10.1214/ss/1032280304. https://projecteuclid.org/euclid.ss/1032280304


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See also

  • Includes: Walter W. Hauck, Sharon Anderson. Comment.
  • Includes: Michael P. Meredith, Mark A. Heise. Comment.
  • Includes: Jen-pei Liu, Shein-Chung Chow. Comment.
  • Includes: Donald J. Schuirmann. Comment.
  • Includes: J. T. Gene Hwang. Comment.
  • Includes: Roger L. Berger, Jason C. Hsu. Rejoinder.