There is a new version of this course. Please see Longitudinal Data Analysis Using Discrete and Continuous Responses.
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
Formats available | Standard duration | | |
Classroom: |
3.0 days | | |
|
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
