Getting Robust Standard Errors for Clustered Data | SAS Code Fragments

/************************************************************
  Finite-sample Adjustment for standard error estimates
  for ordinary least square regression

  data: the input data set
  cluster: cluster variable
  dep : outcome variable
  indvars : variable list of all the independent variables

  Example:
  %reg_cluster(auto, rep78, dep=price, indvars=mpg weight)
************************************************************/

%macro reg_cluster(data, cluster, dep =, indvars = );
  data &data._1;
    set &data;
	array all(*) &dep &indvars;
	do _i = 1 to dim(all);
	  if all(_i) = . then delete;
	end;
    drop _i;
  run;

  proc genmod data = &data._1;
    class &cluster;
  model &dep = &indvars /dist=normal;
  repeated subject = &cluster /type = ind covb;
  ods output geercov = gcov;
  ods output GEEEmpPEst = parms;
run;
quit;
proc sql;
  select count(&cluster),count(distinct &cluster)  into :n, :m
  from &data._1;
 quit;
proc sql;
  select count(prm1) into :k
  from gcov;
quit;
data gcov_ad;
  set gcov;
  array all(*) _numeric_;
  do i = 1 to dim(all);
  all(i) = all(i)*((&n-1)/(&n-&k))*(&m/(&m-1));
  if i = _n_ then std_ad = sqrt(all(i));
  end;
drop i;
keep std_ad;
run;
data all;
  merge parms gcov_ad;
run;
proc print data = all noobs;
run;
%mend;