Bivariate probit model with random effects

[niam]

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?

[SteveDenham]

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