Purpose. It is often difficult to estimate parameters from individual clinical data because of noisy or incomplete measurements. Nonlinear mixed-effects (NLME) modeling provides a statistical framework for analyzing population parameters and the associated variations, even when individual data sets are incomplete. The authors demonstrate the application of NLME by analyzing data from the MNREAD, a continuous-text reading-acuity chart. Methods. The authors analyzed MNREAD data (measurements of reading speed vs. print size) for two groups: 42 adult observers with normal vision and 14 patients with age-related macular degeneration (AMD). Truncated sets of MNREAD data were generated from the individual observers with normal vision. The MNREAD d...
Analyses of longitudinal data with non-linear mixed-effects models (NLMEM) are typically associated ...
International audienceThis article focuses on parameter estimation of multilevel nonlinear mixed-eff...
Precise modeling of disease progression in neurodegenerative disorders may enable early intervention...
PURPOSE. It is often difficult to estimate parameters from indi-vidual clinical data because of nois...
The linear model (LM) is typically used to analyze the relationship between imaging data and demogra...
International audienceThis article represents the first in a series of tutorials on model evaluation...
Linear mixed effects models for RNFL-G thickness, RNFL-T thickness, and macular volume with group, a...
International audienceData below the quantification limit (BQL data) are a common challenge in data ...
Background: Studies of cell-to-cell variation have in recent years grown in interest, due to improve...
This article represents the first in a series of tutorials on model evaluation in nonlinear mixed ef...
Multivariate nonlinear mixed-effects models (MNLMM) have received increasing use due to their flexib...
Background: Studies of cell-to-cell variation have in recent years grown in interest, due to improve...
Background: Studies of cell-to-cell variation have in recent years grown in interest, due to improve...
This article represents the first in a series of tutorials on model evaluation in nonlinear mixed ef...
Semiparametric nonlinear mixed-effects (NLME) models are flexible for modelling complex longitudinal...
Analyses of longitudinal data with non-linear mixed-effects models (NLMEM) are typically associated ...
International audienceThis article focuses on parameter estimation of multilevel nonlinear mixed-eff...
Precise modeling of disease progression in neurodegenerative disorders may enable early intervention...
PURPOSE. It is often difficult to estimate parameters from indi-vidual clinical data because of nois...
The linear model (LM) is typically used to analyze the relationship between imaging data and demogra...
International audienceThis article represents the first in a series of tutorials on model evaluation...
Linear mixed effects models for RNFL-G thickness, RNFL-T thickness, and macular volume with group, a...
International audienceData below the quantification limit (BQL data) are a common challenge in data ...
Background: Studies of cell-to-cell variation have in recent years grown in interest, due to improve...
This article represents the first in a series of tutorials on model evaluation in nonlinear mixed ef...
Multivariate nonlinear mixed-effects models (MNLMM) have received increasing use due to their flexib...
Background: Studies of cell-to-cell variation have in recent years grown in interest, due to improve...
Background: Studies of cell-to-cell variation have in recent years grown in interest, due to improve...
This article represents the first in a series of tutorials on model evaluation in nonlinear mixed ef...
Semiparametric nonlinear mixed-effects (NLME) models are flexible for modelling complex longitudinal...
Analyses of longitudinal data with non-linear mixed-effects models (NLMEM) are typically associated ...
International audienceThis article focuses on parameter estimation of multilevel nonlinear mixed-eff...
Precise modeling of disease progression in neurodegenerative disorders may enable early intervention...