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Longitudinal Data Analysis Using Discrete and Continuous Responses

Course Details
Code: LONG42
Tuition (USD): $2,400.00 • Classroom (3 days)
$2,400.00 • Virtual (3 days)

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 the SAS(R) System course instead.

Skills Gained

  • 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 Can Benefit

  • Epidemiologists, social scientists, physical scientists, and business analysts

Prerequisites

  • 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.

Course Details

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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