I need to reproduce the identical parameter estimates with a clustered or robust standard errors.  I have not been able to reproduce the results.  The first model is the model I am trying to reproduce by utilizing other PROCS in order to calculate the clustered variance.  Any recommendations out there?!? Model need to reproduce with clustered or robust standard errors proc logistic data = regdatas;   title 'Measure 5';   class quarter /param = ref ref = first;   model meas_5_num (event = '1')=  treated female nonwhite age_at_discharge quarter score_community;   strata prov_name;   where (prov_ace_crd = 1 or prov_tru_crd = 1) & quarter ne '' & prov_name ne '' & meas_5_denom = 1 &   Procedure_group in ('CARDIAC DEFIBRILLATOR IMPLANT','CARDIAC PACEMAKER IMPLANT OR REVISION','CARDIAC VALVE AND OTHER MAJOR CARDIOTHORACIC',   'CORONARY ARTERY BYPASS GRAFT','PERCUTANEOUS CORONARY INTERVENTION');   by procedure_group; run; I have tried - proc mixed data = regdatas method=REML empirical;   title 'Measure 5';   class quarter prov_name;   model meas_5_num =  treated female nonwhite age_at_discharge quarter score_community / solution influence;   random int / sub=prov_name g gcorr;   where (prov_ace_crd = 1 or prov_tru_crd = 1) & quarter ne '' & prov_name ne '' & meas_5_denom = 1 &   Procedure_group in ('CARDIAC DEFIBRILLATOR IMPLANT','CARDIAC PACEMAKER IMPLANT OR REVISION','CARDIAC VALVE AND OTHER MAJOR CARDIOTHORACIC',   'CORONARY ARTERY BYPASS GRAFT','PERCUTANEOUS CORONARY INTERVENTION');   by procedure_group; run; proc genmod data = x descending;   class quarter (param=ref ref=first) prov_name;   model meas_5_num = treated female nonwhite age_at_discharge quarter score_community / dist=binomial link=logit noint;   repeated subject=prov_name / type=cs corrw;   by procedure_group; run; proc glimmix data = x empirical=classical;   class quarter prov_name;   model meas_5_num = treated female nonwhite age_at_discharge quarter score_community / dist=binomial solution;   random intercept / subject=prov_name;   by procedure_group; run; proc surveylogistic data = x;   title 'Measure 5';   class quarter / param=ref ref=first;   model meas_5_num (event='1') = treated female nonwhite age_at_discharge quarter score_community / noint;   strata prov_name;   by procedure_group;   cluster prov_name; run; ... View more

I need to reproduce identical parameter estimates with clustered or robust standard errors.  I have not been able to reproduce the results.  The logistic procedure is the model I am trying to reproduce by utilizing other PROCS in order to calculate the clustered variance.  Based on the literature that I have viewed, I have not been able to find a way to produce clustered or robust standard deviations using the logistic procedure.  I have been attempting this for some time and I need a fresh set of eyes.  Any recommendations out there?!? Model need to reproduce with clustered or robust standard errors - proc logistic data = regdatas;   title 'Measure 5';   class quarter /param = ref ref = first;   model meas_5_num (event = '1')=  treated female nonwhite age_at_discharge quarter score_community;   strata prov_name;   where (prov_ace_crd = 1 or prov_tru_crd = 1) & quarter ne '' & prov_name ne '' & meas_5_denom = 1 &   Procedure_group in ('CARDIAC DEFIBRILLATOR IMPLANT','CARDIAC PACEMAKER IMPLANT OR REVISION','CARDIAC VALVE AND OTHER MAJOR CARDIOTHORACIC',   'CORONARY ARTERY BYPASS GRAFT','PERCUTANEOUS CORONARY INTERVENTION');   by procedure_group; run; I have tried - proc mixed data = regdatas method=REML empirical;   title 'Measure 5';   class quarter prov_name;   model meas_5_num =  treated female nonwhite age_at_discharge quarter score_community / solution influence;   random int / sub=prov_name g gcorr;   where (prov_ace_crd = 1 or prov_tru_crd = 1) & quarter ne '' & prov_name ne '' & meas_5_denom = 1 &   Procedure_group in ('CARDIAC DEFIBRILLATOR IMPLANT','CARDIAC PACEMAKER IMPLANT OR REVISION','CARDIAC VALVE AND OTHER MAJOR CARDIOTHORACIC',   'CORONARY ARTERY BYPASS GRAFT','PERCUTANEOUS CORONARY INTERVENTION');   by procedure_group; run; proc genmod data = x descending;   class quarter (param=ref ref=first) prov_name;   model meas_5_num = treated female nonwhite age_at_discharge quarter score_community / dist=binomial link=logit noint;   repeated subject=prov_name / type=cs corrw;   where (prov_ace_crd = 1 or prov_tru_crd = 1) & quarter ne '' & prov_name ne '' & meas_5_denom = 1 &   Procedure_group in ('CARDIAC DEFIBRILLATOR IMPLANT','CARDIAC PACEMAKER IMPLANT OR REVISION','CARDIAC VALVE AND OTHER MAJOR CARDIOTHORACIC',   'CORONARY ARTERY BYPASS GRAFT','PERCUTANEOUS CORONARY INTERVENTION');   by procedure_group; run; proc glimmix data = x empirical=classical;   class quarter prov_name;   model meas_5_num = treated female nonwhite age_at_discharge quarter score_community / dist=binomial solution;   random intercept / subject=prov_name;   where (prov_ace_crd = 1 or prov_tru_crd = 1) & quarter ne '' & prov_name ne '' & meas_5_denom = 1 &    Procedure_group in ('CARDIAC DEFIBRILLATOR IMPLANT','CARDIAC PACEMAKER IMPLANT OR REVISION','CARDIAC VALVE AND OTHER MAJOR CARDIOTHORACIC',   'CORONARY ARTERY BYPASS GRAFT','PERCUTANEOUS CORONARY INTERVENTION');   by procedure_group; run; ... View more

I have the following pseduo-code in which I need to calculate robust sandwich standard error estimates.  I do not believe you can perform this using PROC LOGISTIC, correct?  If not, which PROC would you recommend?  Which options to specify to substitute for the strata statement?  Thanks! proc logistic data = data;   class x5 /param = ref ref = first;   model y (event = '1')=  x1 x2 x3 x4 x5 x6;   strata x7;   where (x9 = 1 or x10 = 1) & x5 ne '' & x7 ne '' & y_denom = 1 &   x8 in ('name1','name2','name3','name4','name5');   by x8; run; ... View more