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.
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.
Authors: G A Maier; G F Lockwood; J A Oppermann; G Wei; P Bauer; J Fedler-Kelly; T Grasela Journal: Eur J Drug Metab Pharmacokinet Date: 1999 Jul-Sep Impact factor: 2.441
Authors: A Dubois; S Gsteiger; S Balser; E Pigeolet; J L Steimer; G Pillai; F Mentré Journal: Clin Pharmacol Ther Date: 2011-12-28 Impact factor: 6.875
Authors: Mahmoud E Soliman; Adeniyi T Adewumi; Oluwole B Akawa; Temitayo I Subair; Felix O Okunlola; Oluwayimika E Akinsuku; Shahzeb Khan Journal: AAPS PharmSciTech Date: 2022-03-15 Impact factor: 3.246