Fully Bayesian experimental design for pharmacokinetic studies

Ryan, Elizabeth G., Drovandi, Christopher C., & Pettitt, Anthony N. (2015) Fully Bayesian experimental design for pharmacokinetic studies. Entropy, 17(3), pp. 1063-1089.

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Abstract

Utility functions in Bayesian experimental design are usually based on the posterior distribution. When the posterior is found by simulation, it must be sampled from for each future data set drawn from the prior predictive distribution. Many thousands of posterior distributions are often required. A popular technique in the Bayesian experimental design literature to rapidly obtain samples from the posterior is importance sampling, using the prior as the importance distribution. However, importance sampling will tend to break down if there is a reasonable number of experimental observations and/or the model parameter is high dimensional. In this paper we explore the use of Laplace approximations in the design setting to overcome this drawback. Furthermore, we consider using the Laplace approximation to form the importance distribution to obtain a more efficient importance distribution than the prior. The methodology is motivated by a pharmacokinetic study which investigates the effect of extracorporeal membrane oxygenation on the pharmacokinetics of antibiotics in sheep. The design problem is to find 10 near optimal plasma sampling times which produce precise estimates of pharmacokinetic model parameters/measures of interest. We consider several different utility functions of interest in these studies, which involve the posterior distribution of parameter functions.

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2 citations in Scopus
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5 citations in Web of Science®

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ID Code: 64190
Item Type: Journal Article
Refereed: Yes
Keywords: Bayesian design, Importance sampling, Laplace approximation, Pharmacokinetics, Utility function
DOI: 10.3390/e17031063
ISSN: 1099-4300
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Statistics not elsewhere classified (010499)
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 The Author(s)
Copyright Statement: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Deposited On: 11 Nov 2013 05:07
Last Modified: 10 Aug 2015 07:24

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