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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Published in: | Computational statistics & data analysis Vol. 51; no. 12; pp. 6614 - 6623 |
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Main Authors: | , , |
Format: | Journal Article |
Language: | English |
Published: |
Amsterdam
Elsevier B.V
15-08-2007
Elsevier Science Elsevier |
Series: | Computational Statistics & Data Analysis |
Subjects: | |
Online Access: | Get full text |
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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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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0167-9473 1872-7352 |
DOI: | 10.1016/j.csda.2007.03.008 |