How can I change this macro to create all the variables in one data set? I want to do this for multiple varibles using the macro but it keeps overwriting itself and keeping the last flag that was created by the macro. Thank you!   %MACRO FLAG(VAR1,VAR2,FLAGNAME); DATA FLAG; SET RULE1; &FLAGNAME=0; DO _N_=1 to countw(&VAR1,',') while(&FLAGNAME=0); word=scan(&VAR1,_N_,','); if word=' ' then continue; if indexw(trim(&VAR2),trim(word),',') then &FLAGNAME=1; end; drop word; run; %MEND FLAG; %FLAG(Addresses1,Addresses2,First_ADD); %FLAG(Addresses1,Addresses3,Second_ADD); ... View more

Hi, I am using this code for pulling the birthdate >17years FLOOR(intck('MONTH',datepart(pt.BIRTH_DATE), date())/12)> 17   But the person who has DOB of 06/29/2006 is considered as 18years and pulling that person. i tried not to use FLOOR and it still gives me the same 18years.   Can I fix this. ... View more

Hello,   With this syntax: proc sgplot data=plot ; vbox value / category=DV group=genre ; run; SGPLOT produces a VBOX with different colors for the two groups defined in GROUP=   See graphs below.    Instead, I would like the two levels of groups (liteary and popular) to have different patterns (for instance, small dots for one and small x for the other).  I have been looking into various options with ATTRMAP, but I do not seem to be able to get it.   Suggestions?     ... View more

I want to find the relative risk and risk difference and their corresponding 95% CIs to compare adverse events in a treatment group compared to a placebo group.  What 95% CI can handle zero counts using PROC FREQ (or a different procedure)? I was going to use Wald and Agresti-Caffo, respectively, but it looks like Wald can't handle zero counts and since we have a small sample size (n<10), perhaps that's not the best option in general... ... View more

I have data which has already been aggregated like this:   Category Groups People A 23 235 B 45 754 C 12 89   And I want something like this (prefer Proc Report for the bells and whistles) Category Groups Groups Percent People People Percent A 23 28.8% 235 21.8% B 45 56.3% 754 69.9% C 12 15.0% 89 8.3% Total 80 100.0% 1078 100.0%   All the examples I can find online seem to use dis-aggregated data. ... View more