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.
Learn how to
- 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
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 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.
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- Fitting a binary GEE model in PROC GENMOD.
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