Good day, I am recent SAS user and kind of overwhelmed by various options. Please, suggest right approach for (probably) standard modelling task below. 1. In PROC LOGISTIC,  is there way to specify AUC (or c-statistics?) as criteria for attribute removal/inclusion in forward/backward/step-wise model optimization? 2. In PROC LOGISTIC, is there way to specify internal k-fold self-split cross-validation in dataset 3. If these options are not available directly in PROC LOGISTIC, are there frameworks / macros / code examples for the following (e.g. backward) attribute selection process: -*- for given dataset -*-*- loop until no improvement to AUC -*-*-*- loop all "current_attribute_set" -*-*-*-*- remove attribute -*-*-*-*- loop (e.g.) 10 times with stratified 10-fold train-test split -*-*-*-*-*- run LOGISTIC(no_selection) in train set and Score test set -*-*-*-*- combine all 10 test sets and get AUC (or c-statistics?) -*-*-*- select best AUC and adjust "current_attribute_set" (IF there is an improvement!) I found several relevant articles, but not a direct example: SAS Programming for Data Mining: AUC calculation using Wilcoxon Rank Sum Test 41364 - ROC analysis for binary response models fit in the GLIMMIX, NLMIXED, GAM or other procedures 39724 - ROC analysis using validation data and crossvalidation http://www2.sas.com/proceedings/sugi27/p248-27.pdf Thanks in advance, Alex. ... View more