A new approach for outliers in a bioavailability/bioequivalence study

It is well known that outliers can have a significant effect on the conclusion of a bioavailability/bioequivalence study. Existing approaches for outlier detection are ANOVA type based on the assumptions on log-AUC, and they are disconnected from the pharmacokinetics (PK) literature. However, the observations from a bioavailability/bioequivalence study are the correlated concentrations, not the AUCs. Thus, the estimate of AUC and the related variance estimate may not be accurate because of the exclusion of the correlation nature. In this paper, based on the predicted concentrations from a functional linear model which takes into consideration of the correlation structure of concentrations, a residual analysis is proposed to detect the outliers. With this approach, the distributional assumption is on the observed raw concentration instead of the summarized parameter AUC, and this approach takes the repeated measurements nature of the concentration curve into consideration, which is in line with population PK concept and could result in a more accurate variance estimate. A real data set is used to demonstrate the proposed approach.