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08-19-2013 02:14 PM

Hi,

In a dataset with repeated measurements from each subject, I matched the exposure group to those who are unexposed to a certain factor. I have the data structure as follows:

Obs id Y X1 X2 matching

1 1 80 100 25 1

2 1 70 90 20 2

3 2 100 120 25 3

4 2 125 80 40 1

5 3 108 86 92 2

6 3 110 90 84 3

...

PROC MIXED DATA = aa METHOD = ML;

CLASS id;

MODEL Y = X1 X2;

REPEATED INT / TYPE = cs SUBJECT = id;

RUN;

In a matched study, the matching pairs (i.e., variable "matching" here) should be adjusted for to obtain unbias estimates. How can I adjust for matching? Treat matching as a binary variable and put it after X2, or in the REPEATED statement or allow it a RANDOM effect?

Thanks a lot?

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08-20-2013 03:01 PM

Just so I get this straight, observations with the same value of matching form the pair, correct? I don't follow from the example data, where it looks like subject 1 is matched with subject 2 (obs 1 and 4) and subject 1 is also matched with subject 3 (obs 2 and 5). So pairings are not exclusive per subject, which strikes me as a bit odd. If we can get my head wrapped around the actual matching procedure, then I think the code will follow quickly, as the responses will be repeated measures on the pair.

Steve Denham

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Posted in reply to SteveDenham

08-20-2013 05:43 PM

Yes. You are right. Pairs are not exclusive per subjects.

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08-21-2013 03:14 PM

Ick.

I guess you could consider pair as a random effect, rather than repeated and try something like the following, where matching captures all of the pairs:

PROC MIXED DATA = aa METHOD = ML;

CLASS id matching;;

MODEL Y = X1 X2;

random matching;

REPEATED INT / TYPE = cs SUBJECT = id;

RUN;

Steve Denham

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Posted in reply to SteveDenham

08-21-2013 07:29 PM

Thanks. I agree with you.