Nonlinear random effects mixture models: Maximum likelihood estimation via the EM algorithm

Nonlinear random effects models with finite mixture structures are used to identify polymorphism in pharmacokinetic/ pharmacodynamic (PK/PD) 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 PK/PD problems will be of interest for future investigation. © 2007 Elsevier B.V. All rights reserved.

• Type: Journal
• Year: 2007
• Where: CSDA
• Authors: Xiaoning Wang, Alan Schumitzky, David Z. D'Argenio
• Keywords: CSDA 2007, Estimation Approach, Nonlinear Random Effects, Random Effects Models