I need help with a matched case-control study. I had an outcome of interest 'TMC' and matched 1 case of 'TMC' to 2 controls by age. Now, I want to analyse with SAS to see whether variables of interest are different (2-sided) in cases vs. controls. The data is non-parametric, non-symmetrical for the most part.
'TMC' is the condition (case=1, control=0), 'daysadmit' is the continuous variable
For the continuous variables, I used the following code:
PROC GLM DATA = descriptive ;
CLASS match tmc ;
MODEL daysadmit = tmc match(tmc);
Is this correct? Do I need to look at the p-value for match(tmc), or do I need that nested variable in there at all? Or, is sufficient to have 'match' in the class statement and only have daysadmit=tmc as the model?
Also, is there any value to putting in this statment:
TEST H = tmc E = match(tmc);
For the categorical variables I used:
if TMC=0 then status=2;
if TMC=1 then status=1;
if TMC=1 then censor=1;
if TMC=0 then censor=0;
Where I made status=1 controls, status=2 cases (of toxic megacolon), censor=0 controls, censor=1 cases.
The problem here is that the numbers are strange. I want to see whether the frequency of a bunch of variables is different in the case group from the control group. The the predictor variable listed above ('fever'), 90% of the case group ('TMC') had fever while 25% of the control group had fever. Using the above program, I get a p of 0.026. With leukocytosis 'leuko' as the predictor, 80% of the case group had leukocytosis while 5 % of the control group had leukocytosis. It looks like it should be significant, but I get a p value of 0.996.