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Generalized quasi-linear mixed-effects model
Statistical Methods in Medical Research, 2022The generalized linear mixed model (GLMM) is one of the most common method in the analysis of longitudinal and clustered data in biological sciences. However, issues of model complexity and misspecification can occur when applying the GLMM. To address these issues, we extend the standard GLMM to a nonlinear mixed-effects model based on quasi-linear ...
Yusuke Saigusa +2 more
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, 2021 Linear Mixed Effect Models for Rehabilitation Research
American Journal of Physical Medicine & Rehabilitation, 2021Abstract The growing emphasis on evidence-based methods in rehabilitation medicine calls for increase in the sophistication of study design and analytic methods across the discipline. To properly evaluate new treatment options, a physiatrist needs to be able to separate treatment effects from parallel changes that occur over time and ...
J W, Hamner, Can Ozan, Tan
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2007
Statistical models provide a framework in which to describe the biological process giving rise to the data of interest. The construction of this model requires balancing adequate representation of the process with simplicity. Experiments involving multiple (correlated) observations per subject do not satisfy the assumption of independence required for ...
Ann L, Oberg, Douglas W, Mahoney
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Statistical models provide a framework in which to describe the biological process giving rise to the data of interest. The construction of this model requires balancing adequate representation of the process with simplicity. Experiments involving multiple (correlated) observations per subject do not satisfy the assumption of independence required for ...
Ann L, Oberg, Douglas W, Mahoney
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2012
In Chap.10, we presented linear models (LMs) models with fixed effects for correlated data. They are examples of population-averaged models, because their mean-structure parameters can be interpreted as effects of covariates on the mean value of the dependent variable in the entire population. The association between the observations in a dataset was a
Andrzej Gałecki, Tomasz Burzykowski
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In Chap.10, we presented linear models (LMs) models with fixed effects for correlated data. They are examples of population-averaged models, because their mean-structure parameters can be interpreted as effects of covariates on the mean value of the dependent variable in the entire population. The association between the observations in a dataset was a
Andrzej Gałecki, Tomasz Burzykowski
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Linear Transformations of Linear Mixed-Effects Models
The American Statistician, 1997Abstract A number of articles have discussed the way lower order polynomial and interaction terms should be handled in linear regression models. Only if all lower order terms are included in the model will the regression model be invariant with respect to coding transformations of the variables.
Christopher H. Morrell +2 more
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Influence analysis for linear mixed‐effects models
Statistics in Medicine, 2004AbstractIn this paper, we extend several regression diagnostic techniques commonly used in linear regression, such as leverage, infinitesimal influence, case deletion diagnostics, Cook's distance, and local influence to the linear mixed‐effects model.
Eugene, Demidenko, Therese A, Stukel
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Random Effects Selection in Linear Mixed Models
Biometrics, 2003Summary. We address the important practical problem of how to select the random effects component in a linear mixed model. A hierarchical Bayesian model is used to identify any random effect with zero variance. The proposed approach reparameterizes the mixed model so that functions of the covariance parameters of the random effects distribution are ...
Zhen, Chen, David B, Dunson
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2011
This chapter expands on linear models through the introduction of random effects, where the independent variables in the model include those variables that are fixed and those that vary across subjects. The general linear mixed effects model (LMEM) is introduced as methods for parameter estimation – maximum likelihood and restricted maximum likelihood.
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This chapter expands on linear models through the introduction of random effects, where the independent variables in the model include those variables that are fixed and those that vary across subjects. The general linear mixed effects model (LMEM) is introduced as methods for parameter estimation – maximum likelihood and restricted maximum likelihood.
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A Linear Mixed‐Effects Model for Multivariate Censored Data
Biometrics, 2000Summary.We apply a linear mixed‐effects model to multivariate failure time data. Computation of the regression parameters involves the Buckley‐James method in an iterated Monte Carlo expectation‐maximization algorithm, wherein the Monte Carlo E‐step is implemented using the Metropolis‐Hastings algorithm.
Pan, Wei, Louis, Thomas A.
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