Hi there,   I've been asked to calculate white standard errors for a logistic regression model for a work project. Here are some specifics about the data set I'm using: 1.  RCT data collected across 2 separate healthcare sites  2. One observation per row (eg subjectid, age, race, cci, etc) 3. This is not survey data  4. Need to cluster by the site variable    The original model was created by the head statistician using Stata. I need to replicate his model using SAS. The survey logistic procedure produces p-values that are relatively close to those of the head statistician's model (magnitude of the ORs don't change). Have also tried using GENMOD and GLM but am unable to execute the procedures due to multiple error messages.    Considering this, I would like to find the SAS implementation that most closely resembles Stata output for fitting a Logistic Regression model with Heteroskedastic Corrected Standard Errors (Huber White).   My Logistic Regression code:    proc logistic data=x; class gender (ref='1') race1 (ref='2') medicare (ref='0') medicaid (ref='0') priv_ins (ref='0') inc_ind (ref='1')  hospitalizations2 (ref='0') diabetes(ref='0') cpd(ref='0') chf (ref='0') site (ref='1') health_new (ref='1') receive_care (ref='1')/param=ref; model alert (event='1') = agebaseline gender race1 medicare medicaid priv_ins inc_ind traveltime  hospitalizations2 edvisits update_cci cpd chf diabetes pt_days site health_new receive_care; run;   Any help would be greatly appreciated!   Kind regards, M ... View more