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.

The self-study e-learning includes:

• Annotatable course notes in PDF format.
• Virtual lab time to practice.

• Create individual and group profile plots and sample variograms.
• Use the MIXED procedure 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 the GLIMMIX procedure.
• Fit an ordinal generalized linear mixed model and a model with spline effects in PROC GLIMMIX.
• Fit a binary GEE model in the GENMOD procedure.

Who should attend

Formats available	Standard Duration (duration can vary, see event schedule for details)
Classroom:	3 days
Live Web Classroom:	6 half-day session(s)
e-Learning:	21 hours/180 day license

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 2: Data Manipulation Techniques course. You should also be familiar with the MIXED procedure. You can gain this experience by completing the Statistics 2: ANOVA and Regression course.

This course addresses SAS/STAT software.

• Understanding the merits and analytical problems associated with longitudinal data analysis.

• Graphing individual and group profiles.
• Identifying cross-sectional and longitudinal patterns.

• 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.

• Creating a sample variogram that illustrates the error components in your model.
• Plotting information criteria for models with selected covariance structures.

• 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.

• Fitting a binary generalized linear mixed model in PROC GLIMMIX.

• Fitting an ordinal generalized linear mixed model in PROC GLIMMIX.
• Fitting a generalized linear mixed model with splines in PROC GLIMMIX.

• Fitting a binary GEE model in PROC GENMOD.