The New England Journal of Statistics in Data Science

Dose proportionality is an essential aspect of pharmacokinetics (PK). We aim to enhance the efficiency of PK studies by incorporating interim analyses and utilizing data from past trials to increase precision and enable early termination of studies if applicable. In this paper, we extend the multisource exchangeability model (MEM) to the setting with correlated data with interim analyses. Simulation results indicate that the MEM estimators are efficient even with smaller sample sizes, although smaller sample sizes may have higher mean square error (MSE) and bias due to early stopping and more liberal data borrowing from non-exchangeable supplementary sources. Our recommendation is to use a constrained MEM approach when considering small sample sizes, with additional caution needed around the equivalence boundary to better control the inflated type I error rate, bias, and MSE. This research extends the application of MEMs from linear regression models to settings with correlated data using linear mixed effects regression models. It also innovatively applies MEMs to equivalence testing in the context of dose proportionality studies, thereby enhancing their efficiency.