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A sequential Monte Carlo approach to derive sampling times and windows for population pharmacokinetic studies

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Abstract

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.

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References

  1. Amzal B, Bois FY, Parent E, Robert CP (2006) Bayesian-optimal design via interacting particle systems. J Am Stat Assoc 101:773–785

    Article  CAS  Google Scholar 

  2. Bogacka B, Johnson P, Jones B, Volkov O (2008) D-efficient window experimental designs. J Stat Plan Inference 138:160–168

    Article  Google Scholar 

  3. Cavagnaro DR, Myung JI, Pitt MA, Kujala JV (2010) Adaptive design optimization: a mutual information-based approach to model discrimination in cognitive science. Neural Comput 22:887–905

    Article  PubMed  Google Scholar 

  4. Chib S, Greenberg E (1995) Understanding the metropolis-hastings algorithm. Am Stat 49:327–335

    Google Scholar 

  5. Corana A, Marchesi M, Martini C, Ridella S (1987) Minimizing multimodal functions of continuous variables with the ‘simulated annealing’ algorithm. ACM Trans Math Softw 13:262–280

    Article  Google Scholar 

  6. Dartois C, Lemenuel-Diot A, Laveille C, Tranchand B, Tod M, Girard P (2007) Evaluation of uncertainty parameters estimated by different population PK software and methods. J Pharmacokinet Pharmacodyn 34:289–312

    Article  PubMed  Google Scholar 

  7. Del Moral P, Doucet A, Jasra A (2006) Sequential Monte Carlo samplers. J R Stat Soc 68:411–436

    Article  Google Scholar 

  8. Demidenko E (2004) Mixed models—theory and application. Wiley series in probability and statistics, 1st edn. Wiley, Hoboken

    Google 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 publication

  11. Graham G, Aarons L (2006) Optimum blood sampling time windows for parameter estimation in population pharmacokinetic experiments. Stat Med 25:4004–4019

    Article  PubMed  Google Scholar 

  12. Green B, Duffull S (2003) Prospective evaluation of a D-optimal designed population pharmacokinetic study. J Pharmacokinet Pharmacodyn 30:145–161

    Article  PubMed  CAS  Google Scholar 

  13. Johansen AM, Doucet A, Davy M (2008) Particle methods for maximum likelihood estimation in latent variable models. Stat Comput 18:47–57

    Article  Google Scholar 

  14. Kitagawa G (1996) Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. J Comput Graph Stat 5:1–25

    Google Scholar 

  15. Liu J, Chen R, Wong W (1998) Rejection control and sequential importance sampling. J Am Stat Assoc 93:1022–1031

    Article  Google Scholar 

  16. McGree JM, Drovandi CC, Thompson MH, Eccleston JA, Duffull SB, Mengersen K, Pettitt AN, Goggin T (2012) Adaptive Bayesian compound designs for dose finding studies. J Stat Plan Inference 142:1480–1492

    Article  Google Scholar 

  17. McGree JM, Duffull SB, Eccleston JA (2009) Simultaneous vs. sequential design for nested multiple response models with FO and FOCE considerations. J Pharmacokinet Pharmacodyn 36:101–123

    Article  PubMed  CAS  Google Scholar 

  18. Mentré F, Mallet A, Baccar D (1997) Optimal design in random-effects regression models. Biometrika 84:429–442

    Article  Google Scholar 

  19. Meyer RK, Nachtsheim CJ (1995) The coordinate-exchange algorithm for constructing exact optimal experimental designs. Technometrics 37:60–69

    Article  Google Scholar 

  20. Müller P (1999) Simulation-based optimal design. Bayesian Stat 6:459–474

    Google Scholar 

  21. Müller P, Sanso B, De Iorio M (2004) Optimal Bayesian design by inhomogeneous Markov chain simulation. J Am Stat Assoc 99:788–798

    Article  Google Scholar 

  22. Ogungbenro K, Aarons L (2008) Optimization of sampling windows design for population pharmacokinetic experiments. J Pharmacokinet Pharmacodyn 35:465–482

    Article  PubMed  Google Scholar 

  23. Ogungbenro K, Aarons L (2009) An effective approach for obtaining sampling windows for population pharmacokinetic experiments. J Biopharm Stat 19:174–189

    Article  PubMed  Google Scholar 

  24. Pronzato L (2002) Information matrices with random regressors. Application to experimental design. J Stat Plan Inference 108:189–200

    Article  Google Scholar 

  25. Retout S, Mentré F (2003) Further developments of the Fisher information matrix in nonlinear mixed effects models with evaluation in population pharmacokinetics. J Biopharm Stat 13:209–227

    Article  PubMed  Google Scholar 

  26. Woods DC, Lewis SM, Eccleston JA, Russell KG (2006) Designs for generalized linear models with several variables and model uncertainty. Technometrics 48:284–292

    Article  Google Scholar 

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Acknowledgments

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.

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Authors and Affiliations

  1. Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, 4001, Australia

    J. M. McGree, C. C. Drovandi & A. N. Pettitt

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  1. J. M. McGree
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  2. C. C. Drovandi
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  3. A. N. Pettitt
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Correspondence to J. M. McGree.

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McGree, J.M., Drovandi, C.C. & Pettitt, A.N. A sequential Monte Carlo approach to derive sampling times and windows for population pharmacokinetic studies. J Pharmacokinet Pharmacodyn 39, 519–526 (2012). https://doi.org/10.1007/s10928-012-9265-1

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  • DOI: https://doi.org/10.1007/s10928-012-9265-1

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