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A mixed model, mixed-effects model or mixed error-component model is a statistical model containing both fixed effects and random effects. [ 1 ] [ 2 ] These models are useful in a wide variety of disciplines in the physical, biological and social sciences.
Repeated measures analysis of variance (rANOVA) is a commonly used statistical approach to repeated measure designs. [3] With such designs, the repeated-measure factor (the qualitative independent variable) is the within-subjects factor, while the dependent quantitative variable on which each participant is measured is the dependent variable.
In statistics, a mixed-design analysis of variance model, also known as a split-plot ANOVA, is used to test for differences between two or more independent groups whilst subjecting participants to repeated measures. Thus, in a mixed-design ANOVA model, one factor (a fixed effects factor) is a between-subjects variable and the other (a random ...
In multilevel modeling for repeated measures data, the measurement occasions are nested within cases (e.g. individual or subject). Thus, level-1 units consist of the repeated measures for each subject, and the level-2 unit is the individual or subject. In addition to estimating overall parameter estimates, MLM allows regression equations at the ...
Multilevel models (also known as hierarchical linear models, linear mixed-effect models, mixed models, nested data models, ... (repeated measures) data
While any statistical model containing both fixed effects and random effects is an example of a nonlinear mixed-effects model, the most commonly used models are members of the class of nonlinear mixed-effects models for repeated measures [1]
In statistics, a generalized linear mixed model (GLMM) is an extension to the generalized linear model (GLM) in which the linear predictor contains random effects in addition to the usual fixed effects. [1] [2] [3] They also inherit from generalized linear models the idea of extending linear mixed models to non-normal data.
Most clinical trials are analyzed using repeated-measurements ANOVA (analysis of variance) or mixed models that include random effects. In most longitudinal studies of human subjects, patients may withdraw from the trial or become "lost to follow-up". There are statistical methods for dealing with such missing-data and "censoring" problems.