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Population Pharmacokinetics of Gentamicin in Neonates Using a Nonlinear, Mixed‐Effects Model
Pharmacotherapy, 1992The population pharmacokinetics of gentamicin in neonates was determined using a nonlinear, mixed‐effects model (NONMEM). The final regression equations derived to estimate clearance (Cl) and volume of distribution (Vd) were Cl = 0.120 * (WT/2.4)1,36 L ...
P. Jensen, B. Edgren, R. Brundage
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Goodness-of-Fit in Generalized Nonlinear Mixed-Effects Models
Biometrics, 1996In recent years, generalized linear and nonlinear mixed-effects models have proved to be powerful tools for the analysis of unbalanced longitudinal data. To date, much of the work has focused on various methods for estimating and comparing the parameters of mixed-effects models. Very little work has been done in the area of model selection and goodness-
Vonesh, Edward F. +2 more
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Canadian Journal of Forest Research, 2021
Tree height (H) is one of the most important tree variables and is widely used in growth and yield models, and its measurement is often time-consuming and costly.
L. Xie +4 more
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Tree height (H) is one of the most important tree variables and is widely used in growth and yield models, and its measurement is often time-consuming and costly.
L. Xie +4 more
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Longitudinal functional nonlinear marginal mixed effect models
Communications in Statistics - Simulation and Computation, 2019AbstractIn this article, a longitudinal functional model including both fixed and random effects which depend on measurement periods is considered.
R. Nasirzadeh +2 more
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Nonlinear Mixed Effects Models, Growth Curves, and Autoregressive Linear Mixed Effects Models
2018In the previous chapters, we discussed autoregressive linear mixed effects models. In this section, we discuss the relationships between the autoregressive linear mixed effects models and nonlinear mixed effects models, growth curves, and differential equations.
Ikuko Funatogawa, Takashi Funatogawa
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Nonlinear Mixed Effects Models: Theory
2011This chapter introduces the theory behind nonlinear mixed effects models through the concept of a structural model or covariate model coupled to both fixed and random effects in a nonlinear manner. Modeling and estimation of model parameters in the face of different sources of variability (between-subject, inter-occasion, inter-study, and residual or ...
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Nonlinear mixed‐effect models with nonignorably missing covariates
Canadian Journal of Statistics, 2004AbstractNonlinear mixed‐effect models are often used in the analysis of longitudinal data. However, it sometimes happens that missing values for some of the model covariates are not purely random. Motivated by an application to HTV viral dynamics, where this situation occurs, the author considers likelihood inference for this type of problem.
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Influence analyses of nonlinear mixed-effects models
Computational Statistics & Data Analysis, 2004zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lee, Sik-Yum, Xu, Liang
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Nonlinear Mixed-Effects Modeling Programs in R
Structural Equation Modeling: A Multidisciplinary Journal, 2017In this software review, we provide a brief overview of four R functions to estimate nonlinear mixed-effects programs: nlme (linear and nonlinear mixed-effects model), nlmer (from the lme4 package, linear mixed-effects models using Eigen and S4), saemix (stochastic approximation expectation maximization), and brms (Bayesian regression models using Stan)
Gabriela Stegmann +3 more
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