SAS Essential Training: 2 Regression Analysis for Healthcare Research

Introduction

• 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

Conclusion

• Review of the process
• Next steps