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A log-normal model for individual bioequivalence

K F Phillips1

  • 1Department of Clinical and Scientific Affairs, Pfizer Pharmaceuticals, New York, New York 10017.

Journal of Biopharmaceutical Statistics
|September 1, 1993
PubMed
Summary
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A new log-normal model aids in testing bioequivalence by assessing the probability of subject responses falling within specified limits. This statistical approach offers greater power for demonstrating mean bioequivalence compared to individual bioequivalence.

Area of Science:

  • Biostatistics
  • Pharmacokinetics
  • Regulatory Science

Background:

  • Bioequivalence studies are crucial for generic drug approval.
  • Assessing individual bioequivalence is statistically challenging.
  • Existing methods like the TIER rule have limitations.

Purpose of the Study:

  • To develop a log-normal model for testing pi 1, the probability of a subject's response within bioequivalence limits.
  • To provide a parametric alternative to Anderson and Hauck's TIER rule.
  • To compare the statistical power of the new model with the TIER rule.

Main Methods:

  • Development of a log-normal statistical model.
  • Derivation of confidence intervals and hypothesis tests.
  • Comparative analysis of statistical power against the TIER rule.

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Main Results:

  • The log-normal model effectively tests pi 1 for bioequivalence.
  • Statistical power of the log-normal model was compared to the TIER rule.
  • Demonstrating mean bioequivalence was found to be significantly more probable than individual bioequivalence.

Conclusions:

  • The developed log-normal model offers a robust method for bioequivalence testing.
  • The model provides a parametric analog to existing non-parametric approaches.
  • Findings highlight the importance of distinguishing between mean and individual bioequivalence in regulatory assessments.