EPID 766: Analysis of Longitudinal Data from ... - NCSU Statistics

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CHAPTER 2 Epid 766, D. Zhang 2 Linear mixed models for normal longitudinal data • What is a linear mixed model? 1. Random intercept model 2. Random intercept and slope model 3. Other error structures 4. General mixed models • Estimation and inference • Choose a variance matrix of the data • Analyze Framingham data using linear mixed models • GEE for mixed models, missing data issue Graduate Summer Session in Epidemiology Slide 48
CHAPTER 2 Epid 766, D. Zhang 2.1 What is a linear mixed (effects) model? A linear mixed model is an extension of a linear regression model to model longitudinal (correlated) data. It contains fixed effects and random effects where random effects are subject-specific and used to model between-subject variation and the correlation induced by this variation. What are fixed effects? Fixed effects are the covariate effects that are fixed across subjects in the study sample. These effects are the ones of our particular interest. E.g., the regression coefficients in usual regression models are fixed effects: y = α + xβ + ǫ. What are random effects? Random effects are the covariate effects that vary among subjects. So these effects are subject-specific and hence are random (unobservable) since each subject is a random subject drawn from a population. Graduate Summer Session in Epidemiology Slide 49
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