A sequential Monte Carlo approach to derive sampling times and windows for population pharmacokinetic studies
- 249 Downloads
Here we present a sequential Monte Carlo approach that can be used to find optimal designs. Our focus is on the design of population pharmacokinetic studies where the derivation of sampling windows is required, along with the optimal sampling schedule. The search is conducted via a particle filter which traverses a sequence of target distributions artificially constructed via an annealed utility. The algorithm derives a catalog of highly efficient designs which, not only contain the optimal, but can also be used to derive sampling windows. We demonstrate our approach by designing a hypothetical population pharmacokinetic study, and compare our results with those obtained via a simulation method from the literature.
KeywordsOptimal design Particle filter Sampling windows Sequential Monte Carlo Utility
We would like to thank both referees for their helpful comments throughout the review process. The authors thank E.G. Ryan for proofreading the paper.
- 4.Chib S, Greenberg E (1995) Understanding the metropolis-hastings algorithm. Am Stat 49:327–335Google Scholar
- 8.Demidenko E (2004) Mixed models—theory and application. Wiley series in probability and statistics, 1st edn. Wiley, HobokenGoogle Scholar
- 9.Duffull SB, Eccleston JA, Kimko HC, Denmanm N (2009) WinPOPT: optimization for population PKPD study design. http://www.winpopt.com. Accessed 1 Aug 2011
- 10.Foo LK, McGree JM, Duffull SB (2011) A general method to determine sampling windows for nonlinear mixed effects models with an application to population pharmacokinetic. Pharm Stat. Submitted for publicationGoogle Scholar
- 14.Kitagawa G (1996) Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. J Comput Graph Stat 5:1–25Google Scholar
- 20.Müller P (1999) Simulation-based optimal design. Bayesian Stat 6:459–474Google Scholar