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Optimal sampling times in population pharmacokinetic studies

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  • Jonathan R. Stroud
  • Peter Müller
  • Gary L. Rosner

Abstract

We propose a simulation‐based approach to decision theoretic Bayesian optimal design. The underlying probability model is a population pharmacokinetic model which allows for correlated responses (drug concentrations) and patient‐to‐patient heterogeneity. We consider the problem of choosing sampling times for the anticancer agent paclitaxel, using criteria related to the total area under the curve, the time above a critical threshold and the sampling cost.

Suggested Citation

  • Jonathan R. Stroud & Peter Müller & Gary L. Rosner, 2001. "Optimal sampling times in population pharmacokinetic studies," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(3), pages 345-359.
    • Handle: RePEc:bla:jorssc:v:50:y:2001:i:3:p:345-359
    DOI: 10.1111/1467-9876.00239
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    Cited by:

    1. Ryan, Elizabeth G. & Drovandi, Christopher C. & Pettitt, Anthony N., 2015. "Simulation-based fully Bayesian experimental design for mixed effects models," Computational Statistics & Data Analysis, Elsevier, vol. 92(C), pages 26-39.
    2. Cong Han & Kathryn Chaloner, 2004. "Bayesian Experimental Design for Nonlinear Mixed-Effects Models with Application to HIV Dynamics," Biometrics, The International Biometric Society, vol. 60(1), pages 25-33, March.
    3. Ryan, Elizabeth G. & Drovandi, Christopher C. & Thompson, M. Helen & Pettitt, Anthony N., 2014. "Towards Bayesian experimental design for nonlinear models that require a large number of sampling times," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 45-60.
    4. Dehideniya, Mahasen B. & Drovandi, Christopher C. & McGree, James M., 2018. "Optimal Bayesian design for discriminating between models with intractable likelihoods in epidemiology," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 277-297.
    5. Elizabeth G. Ryan & Christopher C. Drovandi & James M. McGree & Anthony N. Pettitt, 2016. "A Review of Modern Computational Algorithms for Bayesian Optimal Design," International Statistical Review, International Statistical Institute, vol. 84(1), pages 128-154, April.
    6. McGree, J.M., 2017. "Developments of the total entropy utility function for the dual purpose of model discrimination and parameter estimation in Bayesian design," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 207-225.
    7. Meihua Wu & Ana Diez†Roux & Trivellore E. Raghunathan & Brisa N. Sánchez, 2018. "FPCA†based method to select optimal sampling schedules that capture between†subject variability in longitudinal studies," Biometrics, The International Biometric Society, vol. 74(1), pages 229-238, March.

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