There could be several approaches or models to address your questions and the choice of the most appropriate one for your data and goals has to be yours. That said, one approach is to fit a suitable Generalized Estimating Equations (GEE) model to accommodate the repeated measurements on each subject. I assume that the two cardiac groups consist of two independent sets of subjects. If you assume that your ordinal response is at least roughly normally distributed in each group at each time and that any trend over time is approximately linear, then the model could simply allow for an intercept and a slope for each of the two cardiac groups. The slopes measure the trend in each group.
The basic idea is similar to what is discussed in this note on intervention analysis, but doesn't involve an intervention. The situation described above is like the right-hand (post intervention) side of the data plot in the "Treated and Control Groups, Continuous Response" section of the note. The following generates the same example data but keeps only the observations for the two independent groups over months 4-6.
data a;
sd=3;
keep id exposed month iv expiv y;
do exposed=1,0;
do month=1 to 6;
iv=(month>3);
expiv=cats(exposed,'_',iv);
input mean @;
do ID=1 to 10;
Y=rannor(23425)*sd+mean;
if iv then output;
end;
end; end;
datalines;
26 30 32 45 52 63
27 31 33 29 32 36
;
The PROC GEE step fits and plots a model that allows for separate intercepts (EXPOSED) and slopes or trends (EXPOSED*MONTH) for the two exposure groups (like your cardiac groups). Because the model contains a single, common slope estimate (MONTH), the test for the MONTH*EXPOSED interaction is a test of whether the slopes for the two exposed groups are the same. The highly significant test for the interaction indicates that the group trends differ. Whether the subjects are all observed at that same set of time points doesn't affect this particular analysis.
proc gee data=a;
class id exposed;
model y = exposed month exposed*month;
repeated subject=id;
effectplot slicefit(x=month sliceby=exposed) / extend=data clm;
run;
You could then fit a form of the model that directly shows the estimated intercepts (EXPOSED) and slopes (EXPOSED*MONTH) for the two groups.
proc gee data=a;
class id exposed;
model y = exposed exposed*month / noint;
repeated subject=id;
effectplot slicefit(x=month sliceby=exposed) / extend=data clm;
run;
