A sequential Monte Carlo approach to design for population pharmacokinetics studies
Data(s) |
2012
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Resumo |
Here we present a sequential Monte Carlo approach that can be used to find optimal designs. Our focus is on the design of phase III clinical trials 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 catalogue 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 phase III clinical trial. |
Formato |
application/pdf application/pdf |
Identificador | |
Publicador |
Springer |
Relação |
http://eprints.qut.edu.au/46540/1/annealed_utility_SMC.pdf http://eprints.qut.edu.au/46540/4/46540.pdf DOI:10.1007/s10928-012-9265-1 McGree, J.M., Drovandi, C.C., & Pettitt, A.N. (2012) A sequential Monte Carlo approach to design for population pharmacokinetics studies. Journal of Pharmacokinetics and Pharmacodynamics, 39(5), pp. 519-526. |
Direitos |
Copyright 2012 Springer The original publication is available at SpringerLink http://www.springerlink.com |
Fonte |
Faculty of Science and Technology |
Palavras-Chave | #010400 STATISTICS #Optimal design #Particle filter #Sampling windows #Sequential Monte Carlo #Utility |
Tipo |
Journal Article |