A sequential Monte Carlo approach to design for population pharmacokinetics studies


Autoria(s): McGree, J.M.; Drovandi, C.C.; Pettitt, A.N.
Data(s)

2012

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

http://eprints.qut.edu.au/46540/

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