Mixed Models: Using the General Linear Mixed Model to Analyse Unbalanced Repeated Measures and Longitudinal Data

2005 ◽  
pp. 127-158
Author(s):  
Avital Cnaan ◽  
Nan M. Laird ◽  
Peter Slasor
Keyword(s):  
Longitudinal Data ◽  
Mixed Models ◽  
Repeated Measures ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
General Linear ◽  
General Linear Mixed Model

A Comparison of the General Linear Mixed Model and Repeated Measures ANOVA Using a Dataset with Multiple Missing Data Points

2004 ◽  
Vol 6 (2) ◽  
pp. 151-157 ◽  
Author(s):  
Charlene Krueger ◽  
Lili Tian
Keyword(s):  
Missing Data ◽  
Repeated Measures ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Individual Characteristics ◽  
General Linear ◽  
Repeated Measures Anova ◽  
Longitudinal Methods ◽  
Data Points ◽  
General Linear Mixed Model

Longitudinal methods are the methods of choice for researchers who view their phenomena of interest as dynamic. Although statistical methods have remained largely fixed in a linear view of biology and behavior, more recent methods, such as the general linear mixed model (mixed model), can be used to analyze dynamic phenomena that are often of interest to nurses. Two strengths of the mixed model are (1) the ability to accommodate missing data points often encountered in longitudinal datasets and (2) the ability to model nonlinear, individual characteristics. The purpose of this article is to demonstrate the advantages of using the mixed model for analyzing nonlinear, longitudinal datasets with multiple missing data points by comparing the mixed model to the widely used repeated measures ANOVA using an experimental set of data. The decision-making steps in analyzing the data using both the mixed model and the repeated measures ANOVA are described.

General Linear Mixed Model for Analysing Longitudinal Data in Developmental Research

2010 ◽  
Vol 110 (2) ◽  
pp. 547-566 ◽  
Author(s):  
Jaume Arnau ◽  
Roser Bono ◽  
Nekane Balluerka ◽  
Arantxa Gorostiaga
Keyword(s):  
Longitudinal Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
General Linear ◽  
Developmental Research ◽  
General Linear Mixed Model

Linear mixed model better than repeated measures analysis

2019 ◽  
Vol 30 (6) ◽  
pp. NP1-NP2 ◽  
Author(s):  
Işıl Kutluturk Karagoz ◽  
Berhan Keskin ◽  
Flora Özkalaycı ◽  
Ali Karagöz
Keyword(s):  
Mixed Models ◽  
Repeated Measures ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Linear Time ◽  
Case Series ◽  
Model Fit ◽  
Repeated Measures Analysis ◽  
Technical Issues ◽  
Better Than

We have some criticism regarding some technical issues. Mixed models have begun to play a pivotal role in statistical analyses and offer many advantages over more conventional analyses regarding repeated variance analyses. First, they allow to avoid conducting multiple t-tests; second, they can accommodate for within-patient correlation; third, they allow to incorporate not only a random coefficient, but also a random slope, typically ‘linear’ time in longitudinal case series when there are enough data and patients’ trajectories vary a lot and improving model fit.

Linear Equality Constraints in the General Linear Mixed Model

Biometrics ◽  
2001 ◽  
Vol 57 (4) ◽  
pp. 1185-1190 ◽  
Author(s):  
Lloyd J. Edwards ◽  
Paul W. Stewart ◽  
Keith E. Muller ◽  
Ronald W. Helms
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Equality Constraints ◽  
General Linear ◽  
Linear Equality Constraints ◽  
Linear Equality ◽  
General Linear Mixed Model
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