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Nonlinear random effects mixture models: Maximum likelihood estimation via the EM algorithm
- Source :
-
Computational Statistics & Data Analysis . Aug2007, Vol. 51 Issue 12, p6614-6623. 10p. - Publication Year :
- 2007
-
Abstract
- Abstract: 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. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 01679473
- Volume :
- 51
- Issue :
- 12
- Database :
- Academic Search Index
- Journal :
- Computational Statistics & Data Analysis
- Publication Type :
- Periodical
- Accession number :
- 26036004
- Full Text :
- https://doi.org/10.1016/j.csda.2007.03.008