title: Nonlinear Random Effects Mixture Models: Maximum Likelihood Estimation via the EM Algorithm.
abstract: Nonlinear random effects models with finite mixture structures are used to identify polymorphism in pharmacokinetic/pharmacodynamic phenotypes. An EM algorithm for maximum likelihood estimation approach is developed and uses sampling-based methods to implement the expectation step, that results in an analytically tractable maximization step. A benefit of the approach is that no model linearization is performed and the estimation precision can be arbitrarily controlled by the sampling process. A detailed simulation study illustrates the feasibility of the estimation approach and evaluates its performance. Applications of the proposed nonlinear random effects mixture model approach to other population pharmacokinetic/pharmacodynamic problems will be of interest for future investigation.
authors.affiliation: Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, 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