Population pharmacokinetic/pharmacodynamic mixture models via maximum a posteriori estimation

Authors:
Xiaoning Wang;Alan Schumitzky;David Z. D'Argenio
Affiliations:
Clinical Discovery, Strategic Modeling and Simulation Group, Bristol-Myers Squibb Co., Princeton, NJ 08543, USA;Department of Mathematics, University of Southern California, Los Angeles, CA 90089, USA;Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA
Venue:
Computational Statistics & Data Analysis
Year:
2009

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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.