Modeling Categorical Predictors in SAS

Trying to figure out how to model a categorical predictor in your regression? 

Done this code a million times but can never remember the syntax for the class statement? 

Want to generate exponentiated estimates and confidence intervals?

We'll give examples for binary, 3 levels, 4 levels, and stratified.

Binary Predictor

For all of these code snippets, define &data, &outcome, and &var with your data set, outcome variable, and predictor variable.

&let data=<dataset>;
&let outcome=<outcome>;
​&let var=<var>;

Here we've used proc genmod, and called on logistic regression using the logit link and binomial distribution.

The estimate statement puts a value of 1 in for the predictor variable. The "exp" option has SAS exponentiate the OR and 95% CI for us. You'll want to go to the row labeled Exp(&VAR), over to the estimate and 95% CI column.

​proc genmod data=&data descending;
      model &outcome = &var / link=logit dist=binomial type3;
      estimate "&var" &var 1 / exp;
      run;

Multi-Level Categorical Predictor

This example is for an age category variable, which can easily have more than 3 or 4 levels. Here, &var is "agecat", and the reference value is the youngest age group, 18-29. You'd need to replace the estimate "OR LABEL" &var... statement with your own labels.

The tricky part here is how many zeros? You can see for each subsequent level, we add a zero, extending the 1 out. You'll see later even when we make the reference value a different level, it's similar.

 proc genmod data=&data descending;
      class &var (ref='1') / param=ref;
      model &outcome = &var / link=logit dist=binomial type3;
      estimate "OR 30-39" &var 1 / exp;
      estimate "OR 40-49" &var 0 1 / exp;
      estimate "OR 50-59" &var 0 0 1 / exp;
      estimate "OR 60-69" &var 0 0 0 1 / exp;
      estimate "OR 70-79" &var 0 0 0 0 1 / exp;
      estimate "OR 80+" &var  0 0 0 0 0 1 / exp;
      run;

Different Reference Value

This variable has 3 levels. We're choosing level 2 (private insurance), to be the reference. You can see that when we do our estimate statement, we don't skip over 0 1 for level 2, but instead go sequentially like in the last example. 

If instead we made the reference level 3, we would adjust our labels to reflect that, and use the same 1 and 0 1 designation in the estimate statements.

proc genmod data=out.cohort6 descending;
      class &var (ref='2') / param=ref;
      model &outcome = &var / link=logit dist=binomial;
      estimate "OR Public" &var 1 / exp;
      estimate "OR Other" &var 0 1 / exp;
      run;

Stratified Logistic Regression with Categorical Predictors

What if you want to stratify? First, sort your data set by the strata variable. Then run your analyses as above, but add a -by- statement.

Your output will be for strata=0 and strata=1 (or more if you have more levels). You won't get an overall output, so you'll need to run that separately.

proc sort data=&data out=sort;
      by &strata;
      run;
 
proc genmod data=sort descending;
      by &strata;
      class &var  (ref='1') / param=ref;
      model &outcome = &var / link=logit dist=binomial type3;
      estimate "OR q2" &var  1 / exp;
      estimate "OR q3" &var 0 1/ exp;
      estimate "OR q4" &var  0 0 1 / exp;
      run;

Enjoy! Feel free to bookmark. 

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About the Author

Bailey DeBarmore is a doctoral student at the University of North Carolina at Chapel Hill studying epidemiology. Find her on Twitter@BaileyDeBarmore and blogging for the American Heart Association on the Early Career Voice blog. ​