As I mentioned, PROC LOGISTIC can produce an ROC plot with labeled points. You are not limited to just using observation numbers or computed statistics in the label. If you specify the PLOTS(ONLY)=ROC(ID=ID) option and include an ID statement in your PROC LOGISTIC step, then you can use any variable (or variables) you like from your data set to label the points. But keep in mind that each point on the ROC curve is determined by all of the predictor values in the model and not just the value of any one predictor if your model has multiple predictors. One consequence of this is that more than one point on the ROC curve could have the same value of any one particular predictor.    To produce a single plot with ROC curves from several models, use the OUTROC= option in each model fit to produce ROC data sets with unique names. Then concatenate them together using a DATA step and add a variable identifying the model each one was from. You can then use SGPLOT to produce an overlaid ROC plot like what PROC LOGISTIC makes with code like the following: proc sgplot data=allmodels aspect=1; xaxis values=(0 to 1 by 0.25) grid offsetmin=.05 offsetmax=.05; yaxis values=(0 to 1 by 0.25) grid offsetmin=.05 offsetmax=.05; lineparm x=0 y=0 slope=1 / transparency=.7; series x=_1mspec_ y=_sensit_ / group=model_ID; run; ... View more