Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Loingeville, Florence | - |
dc.contributor.author | Bertrand, Julie | - |
dc.contributor.author | Nguyen, Thu Thuy | - |
dc.contributor.author | Sharan, Satish | - |
dc.contributor.author | Feng, Kairui | - |
dc.contributor.author | Sun, Wanjie | - |
dc.contributor.author | Han, Jing | - |
dc.contributor.author | Grosser, Stella | - |
dc.contributor.author | Zhao, Liang | - |
dc.contributor.author | Fang, Lanyan | - |
dc.contributor.author | Möllenhoff, Kathrin | - |
dc.contributor.author | Dette, Holger | - |
dc.contributor.author | Mentré, France | - |
dc.date.accessioned | 2020-07-17T14:37:37Z | - |
dc.date.available | 2020-07-17T14:37:37Z | - |
dc.date.issued | 2020 | - |
dc.identifier.uri | http://hdl.handle.net/2003/39208 | - |
dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-21125 | - |
dc.description.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. | en |
dc.language.iso | en | de |
dc.relation.ispartofseries | Discussion Paper / SFB823;19/2020 | en |
dc.subject | pharmacokinetics | en |
dc.subject | non-asymptotic standard error | en |
dc.subject | two 21 one-sided tests | en |
dc.subject | nonlinear mixed effects model | en |
dc.subject | bioequivalence | en |
dc.subject.ddc | 310 | - |
dc.subject.ddc | 330 | - |
dc.subject.ddc | 620 | - |
dc.title | New model-based bioequivalence statistical approaches for pharmacokinetic studies with sparse sampling | en |
dc.type | Text | de |
dc.type.publicationtype | workingPaper | de |
dcterms.accessRights | open access | - |
eldorado.secondarypublication | false | de |
Appears in Collections: | Sonderforschungsbereich (SFB) 823 |
Files in This Item:
File | Description | Size | Format | |
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DP_1920_SFB823_Loingeville_Bertrand_Nguyen_Sharan_Feng_Dette_etal..pdf | DNB | 481.36 kB | Adobe PDF | View/Open |
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