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Hi, I am trying to use PROC GENMOD to fit a multinomial logistic model accounting for repeated measures using GEE. I have a categorical exposure with 3 categories and an ordinal outcome with 3 levels (low, medium, high). However, these exposures and outcomes result from pairwise comparisons between all individuals in my dataset so my unit of analysis is a pair, not an individual. The data table structure is as follows:
pair | sample1 | sample2 | exposure | outcome |
1 | A | B | category 1 | low |
2 | A | C | category 1 | medium |
3 | A | D | category 2 | high |
4 | B | C | category 3 | low |
5 | B | D | category 3 | low |
6 | C | D | category 2 | medium |
In this simplified example, there are a total of 4 samples (A,B,C and D) so 6 pairs when comparing each sample to all others. I want to account for the fact that each of these samples occurs in multiple pairs, however, I cannot figure out how to deal with the fact I have 2 samples per pair that I need to account for. I have used the following code to incorporate just sample1 as repeated measure, but is there a way to incorporate both sample1 and sample2 as the subject of the repeated measure? I receive various errors whenever I try to do so.
proc genmod data=my_data;
class exposure sample1;
model outcome= exposure / link=cumlogit;
repeated subject = sample1;
run;
Is there a way to accomplish this task?
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Your design implies that you have 4950 pairs that are correlated (100 take 2). How many total observations do you have? If it isn't at least 49,500, GEE might not be the best tool. In fact, you may need to analyze your data in some other fashion designed for multinomial responses (FREQ, LOGISTIC, GENMOD, CATMOD) that would use the levels of pair and the levels of exposure (and their interaction, if possible) as factors.
SteveDenham
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proc genmod data=recode descending;
class sample1 sample2 outcome(ref = '1');
model outcome=exposure/ link=clogit;
repeated subject = sample1(sample2);
run;
Note: I coded my outcome as 1 (low), 2 (medium), 3 (high)
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It appears to me that there is no repeated effect in your design - there is one outcome for each of the 4950 pairs. That leads to another question - how is exposure measured? The only estimation available is the marginal effect of exposure on outcome, averaged over all the pairs (I think). What do you get if you remove the REPEATED statement?
SteveDenham