I have a large xls file with data on multiple sheets. The data is written in chunks with reoccurring column names: id var1 var2 1  10 a 1 12 b 2 9 c 2 12 d 3 8 e 3 11 f id var1 var2 4 10 t 4 9 u 4 8 v 5 13 w 5 11 x 6 9 y 6 18 u 6 16 s I have to account for the fact that the data has multiple column headers, spaces between column headers, and occurs on multiple sheets. The data also isn't evenly spaced, so there can be a different number of rows between column headers.    Does anyone have any idea how to import this into SAS?   ... View more

I have a dataset that looks like this: out time intervention control random_var 0 1 0 1 1 1 2 0 0 2 0 3 1 1 3 1 4 1 0 4 0 5 0 1 5 1 6 0 0 6 0 1 1 1 7 1 2 1 0 8 0 3 1 0 9 1 4 0 1 10 0 5 1 1 11 1 6 0 1 12  I would like to obtain the predicted probabilities and 95% CIs for dummy values which I appended to the end of the dataset. These dummy values are intended to show how the probability of the outcome changes over time within each intervention at the reference level for the control variable and random effect: out time intervention control random_var 0 1 0 1 1 1 2 0 0 2 0 3 1 1 3 1 4 1 0 4 0 5 0 1 5 1 6 0 0 6 0 1 1 1 7 1 2 1 0 8 0 3 1 0 9 1 4 0 1 10 0 5 1 1 11 1 6 0 1 12 . 1 0 0 1 . 2 0 0 1 . 3 0 0 1 . 4 0 0 1 . 5 0 0 1 . 6 0 0 1 . 1 1 0 1 . 2 1 0 1 . 3 1 0 1 . 4 1 0 1 . 5 1 0 1 . 6 1 0 1 Obviously they don't have an outcome, and aren't used to estimate the coefficients. Here is the model: proc glimmix method=laplace; class random intervention (ref='0') control (ref='0'); model out (event='1')=intervention time control intervention*time intervention*control time*control/solution link=logit dist=binary; random random_var; output out=predprob predicted(blup ilink)=pp; run;   With proc logistic, this isn't a problem. But it seems that proc glimmix won't output the predicted probabilities unless there is an outcome variable, for some reason. Is there a way to work around this?    ... View more

I am trying to run a mixed effect logistic model using proc glimmix with two random intercepts. Some background on the data, doctors and clinics were randomized to receive an intervention, so both doctors, clinics and patients have multiple visits. The goal is to determine whether doctors prescribing decreases over time, rather than patient level effects.   This original model execution by a different analyst in R derived two random intercepts for doctor and clinic, but I can’t find a straightforward way to do this in SAS. I am having trouble distinguishing between ‘random’, ‘repeat’ and ‘subject’ in proc glimmix. I’ve tried a few things, but the only model that worked was:   proc glimmix data=mydata method=quad; class doctor clinic intervention (ref=‘0’) control (ref=‘0’); model prescription (event =‘1’)=intervention time control intervention*time control*time intervention*control/solution link=logit dist=binary; random intercept/subject=doctor(clinic) type=un; run;   This gives me only one intercept for doctor(clinic), whereas the same model in R provides a random intercept for both doctor and clinic. The coefficients are also different. Both model variables have the exact same counts and structure. Below is the code in R:   Model <- glmer(prescription ~ intervention + time + control + intervention:time + control:time + intervention:control + (1|doctor) + (1|provid), data=mydata, family=binomial(link=logit), nAGQ=1, control=glmerControl(calc.derivs=FALSE))   Is there a way to get the random intercepts for both doctor and clinic in SAS?  Is there a reason why the results between SAS and R are inconsistent? I also tried the code below, but received an error that ‘Estimation by quadrature is available only if the data can be processed by subjects’:   proc glimmix data=mydata method=quad; class doctor clinic intervention (ref=‘0’) control (ref=‘0’); model prescription (event =‘1’)=intervention time control intervention*time control*time intervention*control/solution link=logit dist=binary; random intercept/subject=doctor type=un; random intercept/subject=clinic type=un; run; ... View more