Authors: Loingeville, Florence
Bertrand, Julie
Nguyen, Thu Thuy
Sharan, Satish
Feng, Kairui
Sun, Wanjie
Han, Jing
Grosser, Stella
Zhao, Liang
Fang, Lanyan
Möllenhoff, Kathrin
Dette, Holger
Mentré, France
Title: New model-based bioequivalence statistical approaches for pharmacokinetic studies with sparse sampling
Language (ISO): en
Abstract: In traditional pharmacokinetic (PK) bioequivalence analysis, two one-sided tests (TOST) are conducted on the area under the concentration-time curve and the maximal concentration derived using a non-compartmental approach. When rich sampling is unfeasible, a model-based (MB) approach, using nonlinear mixed effect models (NLMEM) is possible. However, MB-TOST using asymptotic standard errors (SE) presents increased type I error when asymptotic conditions do not hold. Methods : In this work, we propose three alternative calculations of the SE based on i) an adaptation to NLMEM of the correction proposed by Gallant, ii) the a posteriori distribution of the treatment coefficient using the Hamiltonian Monte Carlo algorithm, and iii) parametric random effects and residual errors bootstrap. We evaluate these approaches by simulations, for two-arms parallel and two-periods two-sequences cross-over design with rich (n=10) and sparse (n=3) sampling under the null and the alternative hypotheses, with MB-TOST. Results: All new approaches correct for the in ation of MB-TOST type I error in PK studies with sparse designs. The approach based on the a posteriori distribution appears to be the best compromise between controlled type I errors and computing times. Conclusion: MB-TOST using non-asymptotic SE controls type I error rate better than when using asymptotic SE estimates for bioequivalence on PK studies with sparse sampling.
Subject Headings: pharmacokinetics
non-asymptotic standard error
two 21 one-sided tests
nonlinear mixed effects model
Issue Date: 2020
Appears in Collections:Sonderforschungsbereich (SFB) 823

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