Population pharmacokinetic/pharmacodynamic mixture models via maximum a posteriori estimation

• Published in: Computational statistics & data analysis Vol. 53; no. 12; pp. 3907 - 3915
• Main Authors: Wang, Xiaoning, Schumitzky, Alan, D’Argenio, David Z.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.10.2009; Elsevier
• Series: Computational Statistics & Data Analysis
• Subjects: Exact sciences and technology; General topics; Mathematics; Multivariate analysis; Numerical analysis; Numerical analysis. Scientific computation; Numerical methods in probability and statistics; Parametric inference; Probability and statistics; Sciences and techniques of general use; Statistics; Mixed distribution; Density estimation; Discriminant analysis; Data analysis; Prior distribution; Probability; Probability distribution; Multivariate analysis; Statistical simulation; Hypothesis test; Statistical test; Statistical computation; Non linear model; A posteriori estimation; Random effect; Pharmacokinetics; EM algorithm; Importance sampling
• ISSN: 0167-9473; 1872-7352
• DOI: 10.1016/j.csda.2009.04.017

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.