Random Effects Models for Longitudinal Data

By Geert Verbeke, Geert Molenberghs, Dimitris Rizopoulos. 2010

939 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/55301c5097d67833b9c6ba55ab14c94c613f7b69

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Citations in the corpus

A review on longitudinal data analysis with random forest (2022, methodology): “For model fitting an expectationmaximization (EM) algorithm [32] is used, which iterates between estimation of the fixed and random effects components.”
Intraseasonal climate and habitat‐specific variability controls the flowering phenology of high alpine plant species (2010, methodology): “Linear mixed-effects models (Laird & Ware 1982) were developed with the following parameters (unless explicitly specified): we assumed a gaussian error distribution, a maximum likelihood algorithm was chosen to approximate the log-likelihood criterion of parameter estimation, within-group errors…”
VARIABLE SELECTION AND PREDICTION WITH INCOMPLETE HIGH-DIMENSIONAL DATA (2016, methodology): “(2008), Garcia, Ibrahim and Zhu (2010a), Chen and Wang (2013) (CW), and RRstep under Rubin’s rule (e.g., stepwise regression with p-value computed based on Rubin’s rule standard error). We follow the same simulation settings reported in Johnson et al. (2008) and Garcia et al.”
RANDOMIZED CLINICAL TRIAL COMPARING FAMILY-BASED TREATMENT TO ADOLESCENT FOCUSED INDIVIDUAL THERAPY FOR ADOLESCENTS WITH ANOREXIA NERVOSA (2010, neutral): “6) Previous hospitalizationsf 7 (24) 6 (19) 22 (71) 19 (66) 29 (48) 25 (41) BMI percentile for age and sex 5.”
Nonlinear mixed-effects modeling of variable-exponent taper equations for lodgepole pine in Alberta, Canada (2009, background): “The REML estimates, on the other hand, are not biased (Laird and Ware 1982; Littell et al. 2006).”
Overview and Findings from the Rush Memory and Aging Project (2012, methodology): “Mixed models are used to examine the relation of risk factors to change in cognitive function over time [94].”
Drivers of changes in agricultural intensity in Europe (2016, background): “Furthermore, random effects models allow for an explicit analysis of between- and within-variations among observations (Laird and Ware, 1982).”
Generalized fiducial inference for normal linear mixed models (2012, background): “There are numerous applications of normal linear mixed models related to topics such as animal breeding studies [Burch and Iyer (1997), E, Hannig and Iyer (2008)], multilevel studies [O’Connell and McCoach (2008)] and longitudinal studies [Laird and Ware (1982)].”
Semi-Parametric Estimation for Conditional Independence Multivariate Finite Mixture Models (2015, methodology): “A common method for modeling this situation is the so-called “conditional-independence model” [29], in which each multivariate observation, say Xi for 1 ≤ i ≤ n, consists of a subject-specific effect plus random noise.”
Sparse Variational Analysis of Linear Mixed Models for Large Data Sets. (2011, methodology): “For concreteness we focus on the linear mixed effects (LME) model (Laird and Ware, 1982), though the proposed methods can be applied directly in many other hierarchical © 2011 Elsevier B.V.”
Prodromal Alzheimer's disease: Successive emergence of the clinical symptoms (2008, methodology): “Longitudinal analyses of the different measures were done using a semiparametric extension(30) of the mixedeffects linear model.(31) This model has demonstrated utility in the description of the evolution of psychometric measures without doing restrictive assumptions on the shape of the curve.”
Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis (2017, methodology): “As a universal notation that encompasses both situations, we can use a standard linear mixed model [38]:”
Robust linear mixed models with skew-normal independent distributions from a Bayesian perspective (2009, positive): “Introduction Linear mixed model (LMM; Laird and Ware, 1982) has become the most frequently used analytic tool for longitudinal data analysis with continuous repeated measures. A linear mixed model consists of a fixed effects and random effects. The random effects account for the between-subject vari”