A Novel Approach to Testing for Average Bioequivalence Based on Modeling the Within-Period Dependence Structure

Area of Science:

• Pharmacokinetics and Biopharmaceutics
• Statistical Modeling in Clinical Trials

Background:

• Bioequivalence trials assess therapeutic equivalence between generic and innovator drug formulations.
• Traditional analysis uses two one-sided tests on area under the curve (AUC), losing information from repeated drug concentration measurements.
• Ignoring within-subject correlation in AUC calculations is a major limitation.

Purpose of the Study:

• To propose a general linear model approach for bioequivalence trials that incorporates within-subject covariance structure.
• To compare the inferential properties of the proposed model-based method with the traditional two one-sided tests approach.
• To evaluate the performance of the proposed method, especially in the presence of missing data.

Main Methods:

• A general linear model was developed to analyze repeated drug concentration measurements, incorporating the within-subject covariance structure.
• Area under the concentration-time curve (AUC) was reparameterized as a linear combination of outcome means within the model.
• Monte Carlo simulation studies were conducted to compare the proposed method with traditional two one-sided tests.

Main Results:

• The proposed general linear model approach demonstrated superior inferential properties compared to the traditional two one-sided tests.
• The model-based method showed significant advantages when dealing with missing data in bioequivalence trials.
• Simulations confirmed the proposed approach as a cost-effective and viable alternative.

Conclusions:

• The general linear model approach offers a more powerful and robust method for bioequivalence trial analysis.
• This method effectively utilizes the correlation structure of repeated measurements, improving statistical inference.
• The proposed approach is particularly beneficial in scenarios with missing data, enhancing the reliability of bioequivalence assessments.