Linear Mixed Models for Longitudinal Data

By A. Avilés. Technometrics, 2001

3,249 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/8ea4aac5f2995bbf8480018ecafa0b317d70177c

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Citations in the corpus

Learning Treatment-Response Models from Multivariate Longitudinal Data (2017, methodology): “Modeling Irregularly-Sampled Multivariate Longitudinal Data: Generalized mixed-effects models (Verbeke and Molenberghs, 2009) have been widely used to model multivariate longitudinal data.”
Addition of sildenafil to long-term intravenous epoprostenol therapy in patients with pulmonary arterial hypertension: a randomized trial. (2008, methodology): “uated the primary end point, change in 6-minute walk distance from baseline to week 16, by using a linear repeated measures longitudinal analysis (26).”
Multivariate meta-analysis for non-linear and other multi-parameter associations (2012, positive): “ˇ; / for model (5) may be written as follows [27]:”
Handbook of International Large-Scale Assessment : Background, Technical Issues, and Methods of Data Analysis (2013, methodology): “However, variances can be tested by computing a likelihood ratio test statistic but compare it to a mixture of chi-square distributions (for more details see Chapter ?? or Verbeke and Molenberghs, 2000; Molenberghs and Verbeke, 2005; Self and Liang, 1987; Stram and Lee, 1994).”
LIKELIHOOD BASED INFERENCE FOR SKEW-NORMAL INDEPENDENT LINEAR MIXED MODELS (2010, background): “Classical inference on the parameter vector θ = (β>, γ>, α>, λ>, ν>)> is based on the marginal distribution for Yi (Verbeke and Molenberghs (2000)), which we present below. The proof follows directly from Corollary 2 in ArellanoValle et al. (2005a), replacing the scale matrix Ψi by u−1 i Ψi there.”
Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research (2014, methodology): “Standard longitudinal data analysis methods, such as linear mixed models (LMM [64]; also known as mixed-effect, random effects, hierarchical linear, or growth curve models) are used to address the two main effect aims.”
Bayesian quantile regression for longitudinal data models (2012, methodology): “This Bayesian quantile mixed-effects model is analogous to the linear mixed model [9].”
New Estimation and Model Selection Procedures for Semiparametric Modeling in Longitudinal Data Analysis (2004, methodology): “In this article, two new approaches are proposed for estimating the regression coefficients in a semiparametric model.”
Selecting the Best Linear Mixed Model Under REML (2006, methodology): “The comparison of mixed models with different mean structures using information criteria such as the AIC or BIC is generally seen as inappropriate under REML (Verbeke and Molenberghs 2000).”
Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time‐to‐Event Data (2011, background): “From standard mixed models theory it is known that the empirical Bayes estimates exhibit shrinkage (Verbeke and Molenberghs, 2000; Fitzmaurice, Laird, and Ware, 2004), which especially increases as the number of repeated measurements ni decreases.”
The Effectiveness of Acceptance and Commitment Therapy for Adolescent Mental Health: Swedish and Australian Pilot Outcomes (2015, methodology): “Mixed Model Repeated Measures (MMRM) was used to investigate effects for data collected at pre and post (Verbeke and Molenberghs 2000).”
Encyclopedia of Statistical Sciences (2007, background): “In addition to material closely linked to chapters in this book, the text by Verbeke and Molenberghs (2000) includes chapters on exploratory data analysis for longitudinal data, inference for random effects, and a number of topics related to missing data, including sensitivity analysis.”