If your final goal is to find an optimal cutoff, then note that there are statistics (like Youden's index and others) that are often used for that. These can be obtained using the ROCPLOT macro (or in PROC LOGISTIC if you have a recent version of SAS Viya). However, note that the unique predicted probabilities, which are the cutoffs used for the ROC curve, are computed using ALL of the predictor values. So, it is not possible to talk about cutoffs on just your LAB predictor with your model. Each cutoff is determined by both LAB and VISIT using your model. If you remove VISIT from the MODEL statement then you can add the OUTROC= option in the MODEL statement in your PROC LOGISTIC step and then merge that data set together with your FITDAT data set.
proc sort data=fitdat out=fitdat2(rename=(predprob=_PROB_)); by predprob; run;
proc sort data=or out=or2; by _prob_; run;
data or3; merge fitdat2 or2; by _prob_; run;
This allows you to have a data set (OR3) that shows the LAB value corresponding to each cutoff. That data set also has the cell counts of the 2x2 table associated with each cutpoint and the sensitivity and 1-specificity statistics. Using those, you can easily compute the other statistics you want as shown in this note on computing various 2x2 table statistics.
... View more
