title: Population Pharmacokinetic/Pharmacodyanamic Mixture Models via Maximum a Posteriori Estimation.
abstract: Pharmacokinetic/pharmacodynamic phenotypes are identified using nonlinear random effects 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.
authors.affiliation: Clinical Discovery, Strategic Modeling & Simulation Group, Bristol-Myers Squibb Co., Princeton, NJ 08543, USA.
authors.forename: Xiaoning
authors.initials: X
authors.lastname: Wang
authors.forename: Alan
authors.initials: A
authors.lastname: Schumitzky
authors.forename: David Z
authors.initials: DZ
authors.lastname: D'Argenio