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Comparison of Model Averaging and Model Selection in Dose Finding Trials Analyzed by Nonlinear Mixed Effect Models
F Hoffmann La Rache Ltd, Roche Innovat Ctr Basel, Roche Pharma Res & Early Dev, Pharmaceut Sci, Basel, Switzerland; Inst Roche, Boulogne, France; Univ Paris Diderot, Sorbonne Paris Cite, INSERM, UMR 1137, IAME, Paris, France.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.ORCID iD: 0000-0002-3712-0255
F Hoffmann La Rache Ltd, Roche Innovat Ctr Basel, Roche Pharma Res & Early Dev, Pharmaceut Sci, Basel, Switzerland.
F Hoffmann La Rache Ltd, Roche Innovat Ctr Basel, Roche Pharma Res & Early Dev, Pharmaceut Sci, Basel, Switzerland; Inst Roche, Boulogne, France.
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2018 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 20, no 3, article id UNSP 56Article in journal (Refereed) Published
Abstract [en]

In drug development, pharmacometric approaches consist in identifying via a model selection (MS) process the model structure that best describes the data. However, making predictions using a selected model ignores model structure uncertainty, which could impair predictive performance. To overcome this drawback, model averaging (MA) takes into account the uncertainty across a set of candidate models by weighting them as a function of an information criterion. Our primary objective was to use clinical trial simulations (CTSs) to compare model selection (MS) with model averaging (MA) in dose finding clinical trials, based on the AIC information criterion. A secondary aim of this analysis was to challenge the use of AIC by comparing MA and MS using five different information criteria. CTSs were based on a nonlinear mixed effect model characterizing the time course of visual acuity in wet age-related macular degeneration patients. Predictive performances of the modeling approaches were evaluated using three performance criteria focused on the main objectives of a phase II clinical trial. In this framework, MA adequately described the data and showed better predictive performance than MS, increasing the likelihood of accurately characterizing the dose-response relationship and defining the minimum effective dose. Moreover, regardless of the modeling approach, AIC was associated with the best predictive performances.

Place, publisher, year, edition, pages
2018. Vol. 20, no 3, article id UNSP 56
Keywords [en]
dose-response relationship, model averaging, model selection, nonlinear mixed effect models
National Category
Pharmaceutical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-356080DOI: 10.1208/s12248-018-0205-xISI: 000431135700006PubMedID: 29600418OAI: oai:DiVA.org:uu-356080DiVA, id: diva2:1233007
Available from: 2018-07-13 Created: 2018-07-13 Last updated: 2018-07-13Bibliographically approved

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