Model selection and averaging of nonlinear mixed-effect models for robust phase III dose selection

• Published in: Journal of pharmacokinetics and pharmacodynamics 2017-12, Vol.44 (6), p.581-597
• Main Authors: Aoki, Yasunori, Röshammar, Daniel, Hamrén, Bengt, Hooker, Andrew C.
• Format: Article
• Language: English
• Subjects: Accuracy; Acetates - administration & dosage; Acetates - pharmacokinetics; Anti-Asthmatic Agents - administration & dosage; Anti-Asthmatic Agents - pharmacokinetics; Asthma; Bias; Biochemistry; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Clinical trials; Clinical Trials, Phase II as Topic - statistics & numerical data; Computer simulation; Decision making; Dose finding study; Dose-effect relationship; Dose-Response Relationship, Drug; Economic models; Female; Humans; Indoles - administration & dosage; Indoles - pharmacokinetics; Male; Mathematical modelling; Model averaging; Model selection; Nonlinear Dynamics; Original Paper; Pharmacology/Toxicology; Pharmacometrics; Pharmacy; Phase IIb clinical trial; Shape functions; Uncertainty; Veterinary Medicine/Veterinary Science
• ISSN: 1567-567X; 1573-8744; 1573-8744
• DOI: 10.1007/s10928-017-9550-0

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.