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 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 Formats available Duration
: 6 half day sessions

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 course. You should also be familiar with the MIXED procedure. You can gain this experience by completing the 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

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