Simulation-based fully Bayesian experimental design for mixed effects models

Ryan, Elizabeth, Drovandi, Christopher C., & Pettitt, Anthony N. (2015) Simulation-based fully Bayesian experimental design for mixed effects models. Computational Statistics and Data Analysis, 92, pp. 26-39.

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In this paper, we present fully Bayesian experimental designs for nonlinear mixed effects models, in which we develop simulation-based optimal design methods to search over both continuous and discrete design spaces. Although Bayesian inference has commonly been performed on nonlinear mixed effects models, there is a lack of research into performing Bayesian optimal design for nonlinear mixed effects models that require searches to be performed over several design variables. This is likely due to the fact that it is much more computationally intensive to perform optimal experimental design for nonlinear mixed effects models than it is to perform inference in the Bayesian framework. In this paper, the design problem is to determine the optimal number of subjects and samples per subject, as well as the (near) optimal urine sampling times for a population pharmacokinetic study in horses, so that the population pharmacokinetic parameters can be precisely estimated, subject to cost constraints. The optimal sampling strategies, in terms of the number of subjects and the number of samples per subject, were found to be substantially different between the examples considered in this work, which highlights the fact that the designs are rather problem-dependent and require optimisation using the methods presented in this paper.

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ID Code: 72237
Item Type: Journal Article
Refereed: Yes
Keywords: Bayesian optimal design, Nonlinear mixed effects models, Population design, Sampling strategies, Stochastic optimisation
DOI: 10.1016/j.csda.2015.06.007
ISSN: 0167-9473
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000)
Divisions: Current > Research Centres > ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS)
Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2015 Elsevier
Copyright Statement: Licensed under the Creative Commons Attribution; Non-Commercial; No-Derivatives 4.0 International. DOI: 10.1016/j.csda.2015.06.007
Deposited On: 29 May 2014 22:20
Last Modified: 11 Nov 2015 09:10

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