@michellemabelle wrote: Ok, I'll avoid stepwise then. Then my issue becomes doing variable selection with multiple imputation. Looking elsewhere on the boards, I've found someone suggest to perform variable selection on a single imputation, and then run that model on all the other imputations using mi analyze to pool coeffients and odds ratios, but I am confused as to how to implement this, or if this is statistically sound. Thank you very much! I think doing variable selection on a single imputation and then using that model thereafter on the other imputations is questionable. That's my opinion. I note that I don't have any experience with that method.   But whatever you come up with, I'm probably not going to like if it involves variable selection; so you might as well come up with something that YOU like and YOU can feel good about.   By the way, how are you doing the imputation in PROC MI? If you are using the EM method, that would make everything feel better to me, as then the different imputations ought to produce somewhat similar models, although the horrors of stepwise could still cause trouble. You see, the EM method ought to (across multiple imputations) result in data that has roughly the same covariance matrix, and thus ought to produce roughly the same Logistic regression models, but no guarantees either. If you're not using EM here, then I don't like it. ... View more

You can do this by arranging the output data set of parameter estimates from PROC MIANALYZE for use as an INEST= data set for PROC LOGISTIC. You can then run PROC LOGISTIC on your training data set to get the ROC curve and predictions for your validation data. Use the INEST= option to bring in the pooled parameter estimates and the MAXITER=0 to prevent PROC LOGISTIC from changing them. Use the PLOTS=ROC option to request the training data ROC curve and the SCORE statement to score the validation data.   The following uses the fish example from the MIANALYZE documentation. The original data is used as both the training and validation data, but you would use your validation data set in the last LOGISTIC run.   data Fish2; input Species $ Length Width @@; datalines; Parkki 16.5 2.3265 Parkki 17.4 2.3142 . 19.8 . Parkki 21.3 2.9181 Parkki 22.4 3.2928 . 23.2 3.2944 Parkki 23.2 3.4104 Parkki 24.1 3.1571 . 25.8 3.6636 Parkki 28.0 4.1440 Parkki 29.0 4.2340 Perch 8.8 1.4080 . 14.7 1.9992 Perch 16.0 2.4320 Perch 17.2 2.6316 Perch 18.5 2.9415 Perch 19.2 3.3216 . 19.4 . Perch 20.2 3.0502 Perch 20.8 3.0368 Perch 21.0 2.7720 Perch 22.5 3.5550 Perch 22.5 3.3075 . 22.5 . Perch 22.8 3.5340 . 23.5 . Perch 23.5 3.5250 Perch 23.5 3.5250 Perch 23.5 3.5250 Perch 23.5 3.9950 . 24.0 . Perch 24.0 3.6240 Perch 24.2 3.6300 Perch 24.5 3.6260 Perch 25.0 3.7250 . 25.5 3.7230 Perch 25.5 3.8250 Perch 26.2 4.1658 Perch 26.5 3.6835 . 27.0 4.2390 Perch 28.0 4.1440 Perch 28.7 5.1373 . 28.9 4.3350 . 28.9 . . 28.9 4.5662 Perch 29.4 4.2042 Perch 30.1 4.6354 Perch 31.6 4.7716 Perch 34.0 6.0180 . 36.5 6.3875 . 37.3 7.7957 . 39.0 . . 38.3 . Perch 39.4 6.2646 Perch 39.3 6.3666 Perch 41.4 7.4934 Perch 41.4 6.0030 Perch 41.3 7.3514 . 42.3 . Perch 42.5 7.2250 Perch 42.4 7.4624 Perch 42.5 6.6300 Perch 44.6 6.8684 Perch 45.2 7.2772 Perch 45.5 7.4165 Perch 46.0 8.1420 Perch 46.6 7.5958 ; proc mi data=Fish2 seed=1305417 out=outfish2; class Species; monotone logistic( Species= Length Width); var Length Width Species; run; ods select none; proc logistic data=outfish2; class Species; model Species= Length Width / covb; by _Imputation_; ods output ParameterEstimates=lgsparms; run; ods select all; proc mianalyze parms=lgsparms; modeleffects Intercept Length Width; ods output parameterestimates=pe; run; proc transpose data=pe out=tpe; var estimate; id parm; run; data train; set fish2; run; data valid; set fish2; run; proc logistic data=train inest=tpe plots(only)=roc; model Species= Length Width / maxiter=0; score data=valid out=valpred2; run;   ... View more

I think your supervisor want you to do some description statistic , like : max min , outlier  and if it conform to Normal distribution ? ... View more