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08-15-2007 06:04 AM

Hi guys... so glad I found this board.

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);

MEANS tmc;

RUN;

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:

**data binomial;**

set descriptive;

if TMC=0 then status=2;

if TMC=1 then status=1;

if TMC=1 then censor=1;

if TMC=0 then censor=0;

run;

proc phreg data=binomial;

model status*censor(0)= fever /ties=discrete risklimits;

strata match;

run;

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.

__HELP! Can someone help me with what to do? __

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:

CLASS match tmc ;

MODEL daysadmit = tmc match(tmc);

MEANS tmc;

RUN;

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:

set descriptive;

if TMC=0 then status=2;

if TMC=1 then status=1;

if TMC=1 then censor=1;

if TMC=0 then censor=0;

run;

proc phreg data=binomial;

model status*censor(0)= fever /ties=discrete risklimits;

strata match;

run;

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.