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Population pharmacokinetic/pharmacodynamic mixture models via maximum a posteriori estimation

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  • Wang, Xiaoning
  • Schumitzky, Alan
  • D'Argenio, David Z.

Abstract

Pharmacokinetic/pharmacodynamic phenotypes are identified using nonlinear random effect models with finite mixture structures. A maximum a posteriori probability estimation approach is presented using an EM algorithm with importance sampling. Parameters for the conjugate prior densities can be based on prior studies or set to represent vague knowledge about the model parameters. A detailed simulation study illustrates the feasibility of the approach and evaluates its performance, including selecting the number of mixture components and proper subject classification.

Suggested Citation

  • Wang, Xiaoning & Schumitzky, Alan & D'Argenio, David Z., 2009. "Population pharmacokinetic/pharmacodynamic mixture models via maximum a posteriori estimation," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 3907-3915, October.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:12:p:3907-3915
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    References listed on IDEAS

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    1. repec:cup:cbooks:9780521252805 is not listed on IDEAS
    2. De la Cruz-Mesia, Rolando & Quintana, Fernando A. & Marshall, Guillermo, 2008. "Model-based clustering for longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1441-1457, January.
    3. Donna K. Pauler & Nan M. Laird, 2000. "A Mixture Model for Longitudinal Data with Application to Assessment of Noncompliance," Biometrics, The International Biometric Society, vol. 56(2), pages 464-472, June.
    4. A. Philippou & G. Roussas, 1975. "Asymptotic normality of the maximum likelihood estimate in the independent not identically distributed case," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 27(1), pages 45-55, December.
    5. Wang, Xiaoning & Schumitzky, Alan & D'Argenio, David Z., 2007. "Nonlinear random effects mixture models: Maximum likelihood estimation via the EM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6614-6623, August.
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