Hello SAS community,
I have a dataset that contains 175 families, with up to 5 members per family. There are up to 2 sexually-linked couples in each household (for example, one household might have grandma-grandpa, mom-dad, and a kid. The next household might have mom-dad and two kids. the next household might just have a dingle person. etc.). I have a household ID, and individual ID, and an ID ('pairs') that is the same for sexually-linked couples within a given household (1-1 or 2-2). All of the people in the household that are not sexually linked to other people in the household have a 3, 4, or 5 in the 'pairs' column.
Here's an example of some of my dataset:
household | individual ID | pairs | disease |
1 | 10010 | 3 | 1 |
1 | 10012 | 4 | 0 |
1 | 10013 | 5 | 1 |
2 | 10020 | 1 | 1 |
2 | 10021 | 1 | 1 |
3 | 10030 | 3 | |
4 | 10040 | 1 | 1 |
4 | 10041 | 1 | 1 |
5 | 10050 | 3 | 1 |
5 | 10052 | 2 | 1 |
5 | 10053 | 2 | 0 |
6 | 10060 | 3 | 1 |
6 | 10062 | 2 | 1 |
6 | 10063 | 2 | 1 |
Our research question is: do the sexually linked people in the household have higher concordance of disease status (binary outcome) compared to all of the non-sexually linked dyads in the house? We want to determine if sexual relationship with a person with disease is a risk factor for disease transmission.
I am using a Glimmix model (SAS version 9.4 (TS1M1):
proc glimmix data = mydatset ASYCORR ASYCOV;
First you should create a new binary variable, sex_partners. The 1-1 and 2-2 cases should receive one code (e.g. 1, "Y"), and the 3, 4, 5 should receive the alternate code (e.g. 0, "N"). This is your independent variable. The other variables are nesting variables.
Then I would try:
proc glimmix data = mydatset ASYCORR ASYCOV;
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