TY - JOUR
T1 - Simulation-based fully Bayesian experimental design for mixed effects models
AU - Ryan, Elizabeth G.
AU - Drovandi, Christopher C.
AU - Pettitt, Anthony N.
N1 - Funding Information:
E.G. Ryan was supported by an APA(I) Scholarship which came from an ARC Linkage Grant with Roche Palo Alto ( LP0991602 ). The work of A.N. Pettitt was also supported by an ARC Discovery Project ( DP110100159 ). The authors would like to thank Dr James McGree from Queensland University of Technology for his helpful comments throughout the process of writing this paper; for supplying the posterior samples for the horse PK model; and for consultation about his paper ( McGree et al., 2012 ). The authors would like to thank the reviewers and the editor for their helpful comments during the revision process of this manuscript.
Publisher Copyright:
© 2015 Elsevier B.V.
PY - 2015/12/1
Y1 - 2015/12/1
N2 - Bayesian inference has commonly been performed on nonlinear mixed effects models. However, there is a lack of research into performing Bayesian optimal design for nonlinear mixed effects models, especially those 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. Fully Bayesian experimental designs for nonlinear mixed effects models are presented, which involve the use of simulation-based optimal design methods to search over both continuous and discrete design spaces. 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 can be addressed using the methods presented.
AB - Bayesian inference has commonly been performed on nonlinear mixed effects models. However, there is a lack of research into performing Bayesian optimal design for nonlinear mixed effects models, especially those 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. Fully Bayesian experimental designs for nonlinear mixed effects models are presented, which involve the use of simulation-based optimal design methods to search over both continuous and discrete design spaces. 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 can be addressed using the methods presented.
KW - Bayesian optimal design
KW - Nonlinear mixed effects models
KW - Population design
KW - Sampling strategies
KW - Stochastic optimisation
UR - http://www.scopus.com/inward/record.url?scp=84939537770&partnerID=8YFLogxK
U2 - 10.1016/j.csda.2015.06.007
DO - 10.1016/j.csda.2015.06.007
M3 - Article
AN - SCOPUS:84939537770
SN - 0167-9473
VL - 92
SP - 26
EP - 39
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
M1 - 6103
ER -