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 resu...

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Bibliographic Details
Published in:Computational statistics & data analysis Vol. 51; no. 12; pp. 6614 - 6623
Main Authors: Wang, Xiaoning, Schumitzky, Alan, D’Argenio, David Z.
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 15-08-2007
Elsevier Science
Elsevier
Series:Computational Statistics & Data Analysis
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Summary: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.
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ISSN:0167-9473
1872-7352
DOI:10.1016/j.csda.2007.03.008