Evaluation of some properties of individual bioequivalence (IBE) from replicate-design studies

L. Tóthfalusi, L. Endrenyi

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Background: One of the claimed benefits of the individual bioequivalence (IBE) approach has been that the aggregate regulatory model rewards a test formulation when it has a within-subject variation smaller than the reference product. Hauck et al. [1996] demonstrated that, in the absence of random variations, this property of IBE was due to the tradeoff between the difference of the means and the deviation between the intrasubject variances of the two formulations. The tradeoff was a consequence of the aggregate regulatory model. However, calculations of Endrenyi and Hao [1998] showed that, in the presence of random variations, not only rewards but also penalties can arise due to chance alone. Methods: A data set of 55 investigations made public by the FDA in 1999 and containing replicate crossover designs was analyzed. Two parameters, AUC and Cmax, were determined in each investigation. Results: The analyses of the FDA data indicate that: rewards and penalties occur at similar frequencies, large rewards and penalties are recorded quite often, and the aggregate IBE model is rather insensitive to the difference between the estimated means and is compatible with the frequent occurrence of large deviations. Conclusion: Rewards and penalties, apparently arising from random variations, can affect regulatory decisions on the acceptance of IBE and can lead to incorrect conclusions.

Original languageEnglish
Pages (from-to)162-166
Number of pages5
JournalInternational Journal of Clinical Pharmacology and Therapeutics
Volume39
Issue number4
Publication statusPublished - 2001

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Therapeutic Equivalency
Reward
Cross-Over Studies
Area Under Curve

Keywords

  • Aggregate model
  • Individual bioequivalence
  • Regulatory decisions
  • Within-subject variations

ASJC Scopus subject areas

  • Toxicology
  • Pharmacology (medical)

Cite this

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abstract = "Background: One of the claimed benefits of the individual bioequivalence (IBE) approach has been that the aggregate regulatory model rewards a test formulation when it has a within-subject variation smaller than the reference product. Hauck et al. [1996] demonstrated that, in the absence of random variations, this property of IBE was due to the tradeoff between the difference of the means and the deviation between the intrasubject variances of the two formulations. The tradeoff was a consequence of the aggregate regulatory model. However, calculations of Endrenyi and Hao [1998] showed that, in the presence of random variations, not only rewards but also penalties can arise due to chance alone. Methods: A data set of 55 investigations made public by the FDA in 1999 and containing replicate crossover designs was analyzed. Two parameters, AUC and Cmax, were determined in each investigation. Results: The analyses of the FDA data indicate that: rewards and penalties occur at similar frequencies, large rewards and penalties are recorded quite often, and the aggregate IBE model is rather insensitive to the difference between the estimated means and is compatible with the frequent occurrence of large deviations. Conclusion: Rewards and penalties, apparently arising from random variations, can affect regulatory decisions on the acceptance of IBE and can lead to incorrect conclusions.",
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AB - Background: One of the claimed benefits of the individual bioequivalence (IBE) approach has been that the aggregate regulatory model rewards a test formulation when it has a within-subject variation smaller than the reference product. Hauck et al. [1996] demonstrated that, in the absence of random variations, this property of IBE was due to the tradeoff between the difference of the means and the deviation between the intrasubject variances of the two formulations. The tradeoff was a consequence of the aggregate regulatory model. However, calculations of Endrenyi and Hao [1998] showed that, in the presence of random variations, not only rewards but also penalties can arise due to chance alone. Methods: A data set of 55 investigations made public by the FDA in 1999 and containing replicate crossover designs was analyzed. Two parameters, AUC and Cmax, were determined in each investigation. Results: The analyses of the FDA data indicate that: rewards and penalties occur at similar frequencies, large rewards and penalties are recorded quite often, and the aggregate IBE model is rather insensitive to the difference between the estimated means and is compatible with the frequent occurrence of large deviations. Conclusion: Rewards and penalties, apparently arising from random variations, can affect regulatory decisions on the acceptance of IBE and can lead to incorrect conclusions.

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