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Confidence region approach for assessing bioequivalence and biosimilarity accounting for heterogeneity of variability

Publication ,  Journal Article
Li, J; Chow, SC
Published in: Journal of Probability and Statistics
January 1, 2015

For approval of generic drugs, the FDA requires that evidence of bioequivalence in average bioequivalence in terms of drug absorption be provided through the conduct of a bioequivalence study. A test product is said to be average bioequivalent to a reference (innovative) product if the 90% confidence interval of the ratio of means (after log-transformation) is totally within (80%, 125%). This approach is considered a one-parameter approach, which does not account for possible heterogeneity of variability between drug products. In this paper, we study a two-parameter approach (i.e., confidence region approach) for assessing bioequivalence, which can also be applied to assessing biosimilarity of biosimilar products. The proposed confidence region approach is compared with the traditional one-parameter approach both theoretically and numerically (i.e., simulation study) for finite sample performance.

Duke Scholars

Published In

Journal of Probability and Statistics

DOI

EISSN

1687-9538

ISSN

1687-952X

Publication Date

January 1, 2015

Volume

2015
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Li, J., & Chow, S. C. (2015). Confidence region approach for assessing bioequivalence and biosimilarity accounting for heterogeneity of variability. Journal of Probability and Statistics, 2015. https://doi.org/10.1155/2015/298647
Li, J., and S. C. Chow. “Confidence region approach for assessing bioequivalence and biosimilarity accounting for heterogeneity of variability.” Journal of Probability and Statistics 2015 (January 1, 2015). https://doi.org/10.1155/2015/298647.
Li, J., and S. C. Chow. “Confidence region approach for assessing bioequivalence and biosimilarity accounting for heterogeneity of variability.” Journal of Probability and Statistics, vol. 2015, Jan. 2015. Scopus, doi:10.1155/2015/298647.

Published In

Journal of Probability and Statistics

DOI

EISSN

1687-9538

ISSN

1687-952X

Publication Date

January 1, 2015

Volume

2015