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Model selection and averaging of nonlinear mixed-effect models for robust phase III dose selection
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Natl Inst Informat, Tokyo, Japan..ORCID iD: 0000-0002-5881-2023
AstraZeneca, IMED Biotech Unit, Quantitat Clin Pharmacol Innovat Med & Early Dev, Gothenburg, Sweden.;SGS Exprimo, Mechelen, Belgium..
AstraZeneca, IMED Biotech Unit, Quantitat Clin Pharmacol Innovat Med & Early Dev, Gothenburg, Sweden..
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Farmakometri)ORCID iD: 0000-0002-2676-5912
2017 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 44, no 6, p. 581-597Article in journal (Refereed) Published
Abstract [en]

Population model-based (pharmacometric) approaches are widely used for the analyses of phase IIb clinical trial data to increase the accuracy of the dose selection for phase III clinical trials. On the other hand, if the analysis is based on one selected model, model selection bias can potentially spoil the accuracy of the dose selection process. In this paper, four methods that assume a number of pre-defined model structure candidates, for example a set of dose-response shape functions, and then combine or select those candidate models are introduced. The key hypothesis is that by combining both model structure uncertainty and model parameter uncertainty using these methodologies, we can make a more robust model based dose selection decision at the end of a phase IIb clinical trial. These methods are investigated using realistic simulation studies based on the study protocol of an actual phase IIb trial for an oral asthma drug candidate (AZD1981). Based on the simulation study, it is demonstrated that a bootstrap model selection method properly avoids model selection bias and in most cases increases the accuracy of the end of phase IIb decision. Thus, we recommend using this bootstrap model selection method when conducting population model-based decision-making at the end of phase IIb clinical trials.

Place, publisher, year, edition, pages
2017. Vol. 44, no 6, p. 581-597
Keywords [en]
Model averaging, Model selection, Pharmacometrics, Phase IIb clinical trial, Dose finding study, Mathematical modelling, Dose-effect relationship
National Category
Pharmacology and Toxicology
Identifiers
URN: urn:nbn:se:uu:diva-342205DOI: 10.1007/s10928-017-9550-0ISI: 000415375800006PubMedID: 29103208OAI: oai:DiVA.org:uu-342205DiVA, id: diva2:1183998
Available from: 2018-02-20 Created: 2018-02-20 Last updated: 2018-02-20Bibliographically approved

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