New model-based bioequivalence statistical approaches for pharmacokinetic studies with sparse sampling

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dc.contributor.authorLoingeville, Florence
dc.contributor.authorBertrand, Julie
dc.contributor.authorNguyen, Thu Thuy
dc.contributor.authorSharan, Satish
dc.contributor.authorFeng, Kairui
dc.contributor.authorSun, Wanjie
dc.contributor.authorHan, Jing
dc.contributor.authorGrosser, Stella
dc.contributor.authorZhao, Liang
dc.contributor.authorFang, Lanyan
dc.contributor.authorMöllenhoff, Kathrin
dc.contributor.authorDette, Holger
dc.contributor.authorMentré, France
dc.date.accessioned2020-07-17T14:37:37Z
dc.date.available2020-07-17T14:37:37Z
dc.date.issued2020
dc.description.abstractIn 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.en
dc.identifier.urihttp://hdl.handle.net/2003/39208
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-21125
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;19/2020en
dc.subjectpharmacokineticsen
dc.subjectnon-asymptotic standard erroren
dc.subjecttwo 21 one-sided testsen
dc.subjectnonlinear mixed effects modelen
dc.subjectbioequivalenceen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleNew model-based bioequivalence statistical approaches for pharmacokinetic studies with sparse samplingen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access
eldorado.secondarypublicationfalsede

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