The ROC curve area and its confidence interval resulting from any binary-response model or classifier can be computed using the ROC statement in PROC LOGISTIC. Several examples are shown in this note. As can be seen in the note, the computations depend only on the predicted and actual classifications from the model/classifier. The method used is a nonparametric method based on U statistic theory as discussed in "Receiver Operating Characteristic Curves" in the Details section of the PROC LOGISTIC documentation. As such, I believe that if you use the predicted classifications from a proper analysis of your survey data, as could be done using PROC SURVEYLOGISTIC, then the area and confidence interval can be obtained using the ROC statement in PROC LOGISTIC. You can further investigate by seeing the DeLong et al. paper cited in the above documentation section which details the method including the variance computation.
