Most SAS data analysts know that you can fit a logistic model in PROC LOGISTIC and create an ROC curve for that model, but did you know that PROC LOGISTIC enables you to create and compare ROC curves for ANY vector of predicted probabilities regardless of where the predictions came from?
For example, you can fit a random-intercept model by using PROC GLIMMIX or use survey weights in PROC SURVEYLOGISTIC, then use the predicted values from those models to produce an ROC curve for the comparisons. You can even overlay multiple ROC curves in one graph.
If Pred1 and Pred2 are two vectors of predicted probabilities, the following syntax creates and overlays the ROC curves:
ods graphics on;
proc logistic data=Have;
model Y = Pred1 Pred2 / nofit;
roc 'Model1' pred=Pred1;
roc 'Model2' pred=Pred2;
roccontrast reference('Model1') / estimate e; /* optional: compare curves */
run;
For more information, see "Create and compare ROC curves for any predictive model."
