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12-21-2016 02:47 PM

Hello,

I am trying to estimate a bivariate probit model with random effects.

In my data, I have two binary outcomes (d and p) and only two continues predictors (x and y). d is a function of x and p is a function of y. I also have a variable called group that has two levels: one and two. the observations in each of the two groups are correlated.

If I ignore the group and correlation between the variables, I can use proc qlim as the following:

```
proc qlim data=test;
model d = x;
model p = y;
endogenous d p ~ discrete;
run;
```

But how can I model the random effects and consider for the fact that observations within the same groups are correlated?

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12-27-2016 02:37 PM

You may wish to consider PROC GLIMMIX, with two dependent variables. Multiple dependent variables can be handled using the dist=byobs() syntax.

If you can recast your model as something like:

```
proc glimmix data=hernio_uv;
class dist;
model response(event='1') = dist dist*age dist*OKstatus /
noint s dist=byobs(dist);
run;
```

then you will be well on your way. For more info, check the GLIMMIX documentation, and in particular the section following DISTRIBUTION=*keyword.*

Steve Denham