Purpose: The main objective of this work is to compare the standard bioequivalence tests based on individual estimates of the area under the curve and the maximal concentration obtained by non-compartmental analysis (NCA) to those based on individual empirical Bayes estimates (EBE) obtained by nonlinear mixed effects models.
Methods: We evaluate by simulation the precision of sample means estimates and the type I error of bioequivalence tests for both approaches. Crossover trials are simulated under H ( 0 ) using different numbers of subjects (N) and of samples per subject (n). We simulate concentration-time profiles with different variability settings for the between-subject and within-subject variabilities and for the variance of the residual error.
Results: Bioequivalence tests based on NCA show satisfactory properties with low and high variabilities, except when the residual error is high, which leads to a very poor type I error, or when n is small, which leads to biased estimates. Tests based on EBE lead to an increase of the type I error, when the shrinkage is above 20%, which occurs notably when NCA fails.
Conclusions: For small n or data with high residual error, tests based on a global data analysis should be considered instead of those based on individual estimates.