* example2.sas
* xref:
* input: blex.sas
* output:
* does  - Example how to use GEE macro.
        - The data step generates data at random. Its details
          are not important. What matters is that it generates
          variables:y, id, x1-x5.
        - Notice that x2-x3 are cluster-level covariates, while
          x4-x5 are observation-level covariates, x1=intercept.
        - Cluster size 10,000.
***************************************************;
filename BLEX "blex.sas";
%include BLEX;
* options mprint;
***************************************************;
data A;         * generate a dataset;
  k = 100;      * # clusters;
  n = 10000;    * cluster size;
  beta1 = 1;    * parameter values;
  beta2 =-0.5;
  beta3 = 0;
  beta4 =-0.5;
  beta5 = 0.5;
  sigma = 0.5;
  seed = 2141994;
  retain x1 1 ;
  array x  x1-x5;
  array beta  beta1-beta5;
  do id = 1 to k;       * one cluster;
    e = rannor(seed);
    x2 = rannor(seed);
    x3 = rannor(seed);
    do j = 1 to n;      * one obs.;
      x4 = rannor(seed);
      x5 = rannor(seed);
      lp = e * sigma;   * linear predictor;
      do l=1 to dim(beta);
        lp=lp+x[l]*beta[l];
      end;
      p = 1/(1+exp(-lp));
      y = (ranuni(seed) <= p);
      output;
    end;        * one obs.;
  end;  * one cluster;
  keep id y x1 - x5 ;
  run;
***************************************************;
title2   "A minimal run";
%gee    (
  data=A,                  /* required: dataset name */
  yvar=y,                  /* required: response 0/1 */
  xvar=x1 x2 x3 x4 x5,     /* required: covariates */
  id=id );                 /* required: cluster id */
***************************************************;