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Related Experiment Videos

Average bioequivalence for two-sequence two-period crossover design with incomplete data.

Jae-Young Lee1, Byung-Chun Kim, Sang-Gue Park

  • 1Division of Management Engineering, KAIST Graduate School of Management, Seoul, Korea. rane@kgsm.kaist.ac.kr

Journal of Biopharmaceutical Statistics
|August 5, 2005
PubMed
Summary

This study introduces a statistical method for bioequivalence trials with missing data, proposing a modified test for average bioequivalence under missing at random assumptions.

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Area of Science:

  • Biostatistics
  • Pharmacokinetics
  • Clinical Trial Design

Background:

  • Bioequivalence trials are crucial for generic drug approval.
  • Missing data in clinical trials can bias results and reduce statistical power.
  • Two-sequence, two-period crossover designs are common but can be affected by missing observations.

Purpose of the Study:

  • To develop a statistical procedure for analyzing two-sequence, two-period crossover bioequivalence trials with missing data.
  • To estimate parameters in the average bioequivalence model under missing at random (MAR) assumptions.
  • To propose and evaluate a modified two one-sided test (TOST) for average bioequivalence.

Main Methods:

  • Utilized maximum likelihood estimation (MLE) for parameter estimation under MAR.

Related Experiment Videos

  • Developed a modified two one-sided test (TOST) procedure.
  • Employed Monte Carlo simulations to assess the performance of the proposed method.

    • Main Results:

    • Maximum likelihood estimators for the average bioequivalence model were derived.
    • The modified TOST demonstrated acceptable performance in simulations.
    • The study provides a viable approach for handling missing data in crossover bioequivalence studies.

    Conclusions:

    • The proposed statistical procedure effectively addresses missing data in two-sequence, two-period crossover bioequivalence trials.
    • The modified TOST is a reliable method for assessing average bioequivalence when data are missing at random.
    • This methodology enhances the robustness of bioequivalence assessments in the presence of incomplete data.