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LinkedIn Learning

SAS Essential Training: 2 Regression Analysis for Healthcare Research

via LinkedIn Learning


Deepen your SAS knowledge by learning how to conduct a regression analysis of a health survey data center using this popular data analytics platform.

SAS is a venerable data analytics platform that boasts millions of users worldwide and a slew of useful features. In this course, instructor Monika Wahi helps you deepen your SAS knowledge by showing how to use the platform to conduct a regression analysis of a health survey data center. Throughout the course, Monika demonstrates how to conduct regression analyses and present your model results in tables. She shows how to develop and present a linear regression model using PROC GLM as part of a hypothesis-driven analysis; how to do a logistic regression model in both PROC GENMOD and PROC LOGISTIC; and how to present and interpret your linear and logistic regression models. To wrap up, she goes over issues in regression and provides a few helpful tips.


  • Introduction to the course
  • What you should know
1. Preparing for Linear Regression
  • Linear regression and hypothesis review
  • Plots for testing assumptions
  • Stepwise linear regression modeling
  • Basic PROC GLM code
  • Reading PROC GLM output
2. Linear Regression Modeling
  • Linear regression model presentation
  • Linear regression: Early models
  • Linear regression: Round 1
  • Linear regression: The final model
  • Linear regression model metadata
  • Linear regression model fit
  • Interpreting linear regression model
3. Preparing for Logistic Regression
  • Hypothesis and odds ratio review
  • Outcome distribution
  • Basic PROC LOGISTIC code
  • Basic PROC LOGISTIC output
  • Stepwise logistic regression modeling
4. Logistic Regression Modeling
  • Logistic regression: Early models
  • Logistic regression: Round 1
  • Logistic regression: The final model
  • Logistic regression model metadata
  • AIC and AUC for model fit
  • Interpreting the logistic regression model
5. Model Presentation
  • Presenting linear regression models
  • Excel for linear regression models
  • Presenting logistic regression models
  • Excel for logistic regression models
6. Issues in Regression
  • Collinearity in stepwise regression
  • Interaction review
  • Interactions in linear regression
  • Interactions in logistic regression
  • Interactions: Stratum-specific estimates
  • -2 log likelihood for model fit
7. Regression Tips
  • Categorizing continuous outcomes
  • Categorizing continuous covariates
  • Flags for ordinal value levels
  • Strategically collapsing categories
  • Choosing reference groups
  • Describe your regression analysis
  • Review of the process
  • Next steps

Taught by

Monika Wahi

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