SAS Essential Training: 2 Regression Analysis for Healthcare Research

Course details

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.

Learning objectives
Preparing for linear regression
Creating plots for testing assumptions
Linear regression modeling
Interpreting the linear regression model
Logistic regression modeling
Presenting linear and logistic regression models
Issues in regression

Concepts

0. Introduction

01 - Introduction to the course
02 - What you should know

1. Preparing for Linear Regression

03 - Linear regression and hypothesis review
04 - Plots for testing assumptions
05 - Stepwise linear regression modeling
06 - Basic PROC GLM code
07 - Reading PROC GLM output

2. Linear Regression Modeling

08 - Linear regression model presentation
09 - Linear regression - Early models
10 - Linear regression - Round 1
11 - Linear regression - The final model
12 - Linear regression model metadata
13 - Linear regression model fit
14 - Interpreting linear regression model

3. Preparing for Logistic Regression

15 - Hypothesis and odds ratio review
16 - Outcome distribution
17 - Basic PROC LOGISTIC code
18 - Basic PROC LOGISTIC output
19 - Stepwise logistic regression modeling

4. Logistic Regression Modeling

20 - Logistic regression - Early models
21 - Logistic regression - Round 1
22 - Logistic regression - The final model
23 - Logistic regression model metadata
24 - AIC and AUC for model fit
25 - Interpreting the logistic regression model
26 - Challenge - Fit a logistic regression model
27 - Solution - Fit a logistic regression model

5. Model Presentation

28 - Presenting linear regression models
29 - Excel for linear regression models
30 - Presenting logistic regression models
31 - Excel for logistic regression models
32 - Challenge - Add interaction to a logistic regression model
33 - Solution - Add interaction to a logistic regression model

6. Issues in Regression

34 - Collinearity in stepwise regression
35 - Interaction review
36 - Interactions in linear regression
37 - Interactions in logistic regression
38 - Interactions - Stratum-specific estimates
39 - -2 log likelihood for model fit

7. Regression Tips

40 - Categorizing continuous outcomes
41 - Categorizing continuous covariates
42 - Flags for ordinal value levels
43 - Strategically collapsing categories
44 - Choosing reference groups
45 - Describe your regression analysis

Conclusion

46 - Review of the process
47 - Next steps