Nonlinear mixed-effects models
Nonlinear mixed-effects models for pharmacokinetic data analysis: assessment of the random-effects distribution
... liner mixed models only and in the presence of a single random ...random- effects distribution in nonlinear mixed-effects models is more challenging, especially when ...
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Strong Consistency of MLE in Nonlinear Mixed-effects Models with Large Cluster Size
... linear mixed effects models is difficult due to the fact that, in general, the likelihood can only be expressed as an integral over the random ...in nonlinear mixed-effects ...
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Development and identification of hierarchical nonlinear mixed effects models for the analysis of dynamic systems: identification and application of hierarchical nonlinear mixed effects models for the determination of steady-state and dynamic torque responses of an SI engine
... Hierarchical Models Researchers in various fields have applied the multi-level modelling ...of nonlinear mixed effects models facilitate inter-subject variation analysis of the ...
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Design evaluation and optimisation in crossover pharmacokinetic studies analysed by nonlinear mixed effects models
... by nonlinear mixed effects models as an alternative to noncompartmental ...in nonlinear mixed effects models to design crossover pharmacokinetic trials,using a ...
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Comparison of nonlinear mixed effects models and non-compartmental approaches in detecting pharmacogenetic covariates: Approaches to detect pharmacogenetic covariates
... involve NonLinear Mixed Effects Models (NLMEM) (6), which jointly analyse data obtained on a set of individuals to determine the typical model parameters (fixed effects), and the ...
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Comparison of nonlinear mixed effects models and non-compartmental approaches in detecting pharmacogenetic covariates: Approaches to detect pharmacogenetic covariates
... involve NonLinear Mixed Effects Models (NLMEM) (6), which jointly analyse data obtained on a set of individuals to determine the typical model parameters (fixed effects), and the ...
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An Optimization Approach for the Parameter Estimation of the Nonlinear Mixed Effects Models
... mented in pro nlmixed. Sine eah of the optimizers requires dierent deriv atives, some omputational eÆienies an be gained. Eah optimization method employs one or more onvergene riteria that determine when it has onverged. ...
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Some alternatives to asymptotic tests for the analysis of pharmacogenetic data using nonlinear mixed effects models.
... use a permutation test. However, as far as we know, such an evaluation has not yet been published in the specific context of NLMEM. Both simulation and permutation based-approaches are relatively time consuming with ...
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Linear and nonlinear mixed-effects models with censored response using the multivariate normal and Student-t distributions
... Mixed models are commonly used to represent longitudinal or repeated measures ...and nonlinear mixed effects models with censored response (NLMEC/LMEC) based on the multivariate ...
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Contributions to nonlinear mixed-effects modeling
... is nonlinear mixed-effects models. A nonlinear mixed-effects model is a hierarchical regression model used to analyze measurements from several individuals simultaneously, ...
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Introduction to Population Pharmacokinetic / Pharmacodynamic. Mixed Effects Models
... statistical models to address diverse questions in the pharmaceutical ...results. Nonlinear mixed effects models have been the primary analysis framework for population-based ...
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Nonlinear mixed-effects scalar-on-function models and variable selection
... We extend the valriable selection algorithm for functinal linear regression proposed by Gertheiss et al. (2013) to the case where both scalar and functional are in the regression. This extended version of the algorithm ...
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Nonlinear mixed-effects scalar-on-function models and variable selection
... and mixed scalar and func- tional covariates, such as Ramsay and Silverman ( 2006 ), but mostly focused on single or small number of functional vari- ...regression models or functional generalized linear ...
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Extension of the SAEM algorithm for nonlinear mixed models with 2 levels of random effects.
... stantially reducing the computation time. Kuhn and Lavielle (16) propose to combine the SAEM algorithm with a Monte-Carlo Markov Chain (MCMC) procedure adapted to the NLMEMs, and prove that the resulting estimates are ...
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Nonlinear mixed-effects HIV dynamic models with considering left-censored measurements
... the mixed-effects modeling framework, the random effects can be treated as missing data so that the EM algorithm is capable of estimating fixed effects after averaging random effects in ...
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Structural identifiability and indistinguishability in mixed effects models
... Chapter 8 Conclusions 8.1 Introduction As has been discussed previously, structural identifiability is an important concept when modelling dynamical systems. In a biological system the majority of the dif- ferent parts ...
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Bayesian inference for diffusion-driven, mixed-effects models
... driven mixed-effects models allow the quantification of both between and within individual ...such models using discrete-time data that may be incomplete and subject to measurement error is a ...
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Bayesian inference for stochastic differential mixed-effects models
... Designing diffusion bridge constructs that can be applied in general multivariate set- tings is a challenging problem and has received much attention in recent literature. The simplest approach (see, for example, ...
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Bayesian Inference for Diffusion-Driven Mixed-Effects Models
... Diffusion-Driven Mixed-Effects Models Gavin ...driven mixed-effects models allow the quantification of both between and within individual ...such models using discrete-time data that may be ...
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Nonlinear mixed effects modeling of gametocyte carriage in patients with uncomplicated malaria
... For example, the gametocyte elimination half-life is the time it takes for the density of gametocytes to halve. The main problem with the data was the high preva- lence of zero measurements and their associated uncer- ...
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