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

Bioequivalence trials are commonly conducted to assess therapeutic equivalence between a generic and an innovator brand formulations. In such trials, drug concentrations are obtained repeatedly over time and are summarized using a metric such as the area under the concentration vs. time curve (AUC) for each subject. The usual practice is to then conduct two one-sided tests using these areas to evaluate for average bioequivalence. A major disadvantage of this approach is the loss of information encountered when ignoring the correlation structure between repeated measurements in the computation of areas. In this article, we propose a general linear model approach that incorporates the within-subject covariance structure for making inferences on mean areas. The model-based method can be seen to arise naturally from the reparameterization of the AUC as a linear combination of outcome means. We investigate and compare the inferential properties of our proposed method with the traditional two one-sided tests approach using Monte Carlo simulation studies. We also examine the properties of the method in the event of missing data. Simulations show that the proposed approach is a cost-effective, viable alternative to the traditional method with superior inferential properties. Inferential advantages are particularly apparent in the presence of missing data. To illustrate our approach, a real working example from an asthma study is utilized.