I have some one group pre-post data and I am looking at differences between a group variable (binary) and the pre post change in the score outcome.
My code for the analysis is below:
proc glm data=data;
class group;
model pre post = group / nouni;
repeated time 2 (1 2) / printe;
lsmeans group / stderr;
run;
I would try PROC MIXED instead of PROC GLM.
Check GLM documenation to see how to transform Repeated Measure from GLM into MIXED.
change your data structure as
data have;
set x;
sub+1;
time=1;value=pre;output;
time=2;value=post;output;
run;
proc mixed data=have;
class group time;
model value=group;
repeated time/type=un solution subject=sub;
lsmean group/pdiff cl;
run;
but does this still not make the same assumptions? my outcome is skewed.
If you are trying to do Repeated Measure, I suggest you use PROC MIXED , not GLM . GLM can't do the real mixed model .
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