Loingeville, FlorenceBertrand, JulieNguyen, Thu ThuySharan, SatishFeng, KairuiSun, WanjieHan, JingGrosser, StellaZhao, LiangFang, LanyanMöllenhoff, KathrinDette, HolgerMentré, France2020-07-172020-07-172020http://hdl.handle.net/2003/3920810.17877/DE290R-21125In 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.enDiscussion Paper / SFB823;19/2020pharmacokineticsnon-asymptotic standard errortwo 21 one-sided testsnonlinear mixed effects modelbioequivalence310330620working paper