Moellenhoff, Kathrin, Loingeville, Florence ORCID: 0000-0002-4278-2974, Bertrand, Julie ORCID: 0000-0002-6568-1041, Nguyen, Thu Thuy, Sharan, Satish, Zhao, Liang, Fang, Lanyan, Sun, Guoying, Grosser, Stella, Mentre, France ORCID: 0000-0002-7045-1275 and Dette, Holger (2022). Efficient model-based bioequivalence testing. Biostatistics, 23 (1). S. 314 - 328. OXFORD: OXFORD UNIV PRESS. ISSN 1468-4357

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Abstract

The classical approach to analyze pharmacokinetic (PK) data in bioequivalence studies aiming to compare two different formulations is to perform noncompartmental analysis (NCA) followed by two one-sided tests (TOST). In this regard, the PK parameters area under the curve (AUC) and Cmax are obtained for both treatment groups and their geometric mean ratios are considered. According to current guidelines by the U.S. Food and Drug Administration and the European Medicines Agency, the formulations are declared to be sufficiently similar if the 90% confidence interval for these ratios falls between 0.8 and 1.25. As NCA is not a reliable approach in case of sparse designs, a model-based alternative has already been proposed for the estimation of AUC and Cmax using nonlinear mixed effects models. Here we propose another, more powerful test than the TOST and demonstrate its superiority through a simulation study both for NCA and model-based approaches. For products with high variability on PK parameters, this method appears to have closer type I errors to the conventionally accepted significance level of 0.05, suggesting its potential use in situations where conventional bioequivalence analysis is not applicable.

Item Type: Journal Article
Creators:
CreatorsEmailORCIDORCID Put Code
Moellenhoff, KathrinUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Loingeville, FlorenceUNSPECIFIEDorcid.org/0000-0002-4278-2974UNSPECIFIED
Bertrand, JulieUNSPECIFIEDorcid.org/0000-0002-6568-1041UNSPECIFIED
Nguyen, Thu ThuyUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Sharan, SatishUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Zhao, LiangUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Fang, LanyanUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Sun, GuoyingUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Grosser, StellaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Mentre, FranceUNSPECIFIEDorcid.org/0000-0002-7045-1275UNSPECIFIED
Dette, HolgerUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
URN: urn:nbn:de:hbz:38-686477
DOI: 10.1093/biostatistics/kxaa026
Journal or Publication Title: Biostatistics
Volume: 23
Number: 1
Page Range: S. 314 - 328
Date: 2022
Publisher: OXFORD UNIV PRESS
Place of Publication: OXFORD
ISSN: 1468-4357
Language: English
Faculty: Unspecified
Divisions: Unspecified
Subjects: no entry
Uncontrolled Keywords:
KeywordsLanguage
MIXED-EFFECTS MODELS; EQUIVALENCE; SIMILARITY; TRIALS; POWERMultiple languages
Mathematical & Computational Biology; Statistics & ProbabilityMultiple languages
URI: http://kups.ub.uni-koeln.de/id/eprint/68647

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