Thanks for replying. To clarify: Variable selection was done in the development set using the AIC (stepwise selection, SLENTRY=1 SLSTAY=1). I realize that Harrell and others recommend using bootstrapping for variable selection, but I'm sticking with the stepwise AIC approach. Using the mean or median of the coefficients obtained in the bootstrapped samples is referred to as "bootstrap aggregating" or "bagging" of coefficients. It's not that I want to avoid hard scoring. The way I've used Score in the past is as such, which allows me to get the ROC graphs and c statistic for the scored dataset. PROC LOGISTIC DATA=WORK.BRAIST_SMS; CLASS THORACIC (REF='0' PARAM=REF) SMSC2 (REF='1.2' PARAM=REF); MODEL VTERM (EVENT='1') = SMSC2 THORACIC COBBMAX; SCORE DATA=WORK.VALID_SMS OUT=VALIDP OUTROC=VROC; ROC; ROCCONTRAST; Is there a way to take the coefficients from the bootstraps and create something that would function like the "outmodel" does below? proc logistic data = hsb2 outmodel=pout; model honcomp = read math; run; proc logistic inmodel=pout; score clm data = toscore out=pred ; run; Ideas? Thanks

... View more