It sounds like what you want is confidence limits for the proportion of success for each of the three groups: Male, Female, and PNA. You could also ask for statistical tests, such as "is there a significant difference for the proportions of success between the groups?"
If you only care whether some group is different (but you don't care which one), you can use PROC FREQ and a chi-square test:
data Awards;
length Gender $6 Success $3;
input Gender Success Count;
datalines;
Male Yes 330
Male No 570
Female Yes 570
Female No 1230
PNA Yes 100
PNA No 200
;
proc freq data=Awards;
weight Count;
tables Gender*Success / chisq;
run;
The output shows that the observed proportion in some group is different than expected.
If you want a more sophisticated model that enables you to quantitatively compare the proportions and the effect of gender, then you can use the event-trial syntax in PROC LOGISTIC. If you want information about the predicted proportion, you can use the LSMEANS statement to get that information.
data EventTrial;
length Gender $6;
input Gender Applied Success;
datalines;
Male 900 330
Female 1800 570
PNA 300 100
;
proc logistic data=EventTrial;
class Gender(ref='Male') / param=GLM; /* do you want to use Male as the ref group? */
model Success / Applied = Gender;
lsmeans Gender / ilink diff=anom adjust=bon CL;
run;
The odds ratio table shows that the 95% CI for the OR of Males vs Females does NOT contain 1, so you can conclude that the effect is different. However, the CI for the OR of Males vs PNA is not different from 1.
The LSMEANS table shows the estimated proportions are Female=0.3167, PNA=0.3333, and Male=0.3667. The CI for Males does not include 1/3, but the other CIs do.
