  The classical approach to analyze pharmacokinetic (PK) data in bioequivalence
studies aiming to compare two different formulations is to perform
noncompartmental analysis (NCA) followed by two one-sided tests (TOST). In this
regard the PK parameters $AUC$ and $C_{max}$ are obtained for both treatment
groups and their geometric mean ratios are considered. According to current
guidelines by the U.S. Food and Drug Administration and the European Medicines
Agency the formulations are declared to be sufficiently similar if the $90\%$-
confidence interval for these ratios falls between $0.8$ and $1.25$. As NCA is
not a reliable approach in case of sparse designs, a model-based alternative
has already been proposed for the estimation of $AUC$ and $C_{max}$ using
non-linear mixed effects models. Here we propose another, more powerful test
than the TOST and demonstrate its superiority through a simulation study both
for NCA and model-based approaches. For products with high variability on PK
parameters, this method appears to have closer type I errors to the
conventionally accepted significance level of $0.05$, suggesting its potential
use in situations where conventional bioequivalence analysis is not applicable.
