This course is for scientists and analysts who want to analyze observational data collected over time. It is not for SAS users who have collected data in a complicated experimental design; they should take the Mixed Models Analyses Using SAS course instead.
Learn how to
- create individual and group profile plots and sample variograms
- use PROC MIXED to fit a general linear mixed model and a random coefficient model
- plot information criteria for models with selected covariance structures
- generate diagnostic plots in PROC MIXED
- fit a binary generalized linear mixed model in PROC GLIMMIX
- fit an ordinal generalized linear mixed model and a model with spline effects in PROC GLIMMIX
- fit a binary GEE model in PROC GENMOD.
Who should attend
Epidemiologists, social scientists, physical scientists, and business analysts
Before attending this course, you should be able to
- execute SAS programs and create SAS data sets
- fit models using the GLM and REG procedures in SAS/STAT software.
You can gain the programming experience by completing the SAS Programming 1: Essentials course. You can gain the modeling experience by completing the Statistics 2: ANOVA and Regression course.
This course addresses SAS/STAT software.
Longitudinal Data Analysis Concepts- understanding the merits and analytical problems associated with longitudinal data analysis
Exploratory Data Analysis- graphing individual and group profiles
- identifying cross-sectional and longitudinal patterns
General Linear Mixed Model- understanding the concepts behind the linear mixed model
- examining the different covariance structures available in PROC MIXED
- fitting a general linear mixed model in PROC MIXED
Evaluating Covariance Structures- creating a sample variogram that illustrates the error components in your model
- plotting information criteria for models with selected covariance structures
Model Development, Interpretation, and Assessment- learning the model building strategies in PROC MIXED
- creating interaction plots
- specifying heterogeneity in the covariance structure
- computing predictions using EBLUPs
- fitting a random coefficient model in PROC MIXED
- generating diagnostic plots in PROC MIXED using ODS Graphics
Generalized Linear Mixed Models- fitting a binary Generalized Linear Mixed Model in PROC GLIMMIX
Applications Using PROC GLIMMIX- fitting an ordinal generalized linear mixed model in PROC GLIMMIX
- fitting a generalized linear mixed model with splines in PROC GLIMMIX
GEE Regression Models- fit a binary GEE model in PROC GENMOD
