Bioequivalence review for drug interchangeability.
Publication
, Journal Article
Chow, SC; Shao, J
Published in: J Biopharm Stat
August 1999
To monitor the performance of the approved generic copies of a brand-name drug, we propose some methods in assessing bioequivalence among generic copies and the brand-name drug, and among generic copies themselves, using data from several bioequivalence studies adopting the standard 2 x 2 crossover design without carryover effects. We propose a meta-analysis method that increases statistical power when the between-subject variability is not large. A nonmeta-analysis is also considered. A numerical example of applying both methods is presented for illustration.
Duke Scholars
Published In
J Biopharm Stat
DOI
ISSN
1054-3406
Publication Date
August 1999
Volume
9
Issue
3
Start / End Page
485 / 497
Location
England
Related Subject Headings
- Therapeutic Equivalency
- Statistics & Probability
- Reference Standards
- Models, Statistical
- Meta-Analysis as Topic
- Mathematical Computing
- Humans
- Drugs, Generic
- Cross-Over Studies
- Biometry
Citation
APA
Chicago
ICMJE
MLA
NLM
Chow, S. C., & Shao, J. (1999). Bioequivalence review for drug interchangeability. J Biopharm Stat, 9(3), 485–497. https://doi.org/10.1081/BIP-100101189
Chow, S. C., and J. Shao. “Bioequivalence review for drug interchangeability.” J Biopharm Stat 9, no. 3 (August 1999): 485–97. https://doi.org/10.1081/BIP-100101189.
Chow SC, Shao J. Bioequivalence review for drug interchangeability. J Biopharm Stat. 1999 Aug;9(3):485–97.
Chow, S. C., and J. Shao. “Bioequivalence review for drug interchangeability.” J Biopharm Stat, vol. 9, no. 3, Aug. 1999, pp. 485–97. Pubmed, doi:10.1081/BIP-100101189.
Chow SC, Shao J. Bioequivalence review for drug interchangeability. J Biopharm Stat. 1999 Aug;9(3):485–497.
Published In
J Biopharm Stat
DOI
ISSN
1054-3406
Publication Date
August 1999
Volume
9
Issue
3
Start / End Page
485 / 497
Location
England
Related Subject Headings
- Therapeutic Equivalency
- Statistics & Probability
- Reference Standards
- Models, Statistical
- Meta-Analysis as Topic
- Mathematical Computing
- Humans
- Drugs, Generic
- Cross-Over Studies
- Biometry