Hi everyone I have a question about the "Number of observations not used"-information when using proc mixed. I'm investigation the the effect of a drug on Diabetes in a randomized clinical trial. I have five measurements pr patient - week 4, 8, 12, 16 and 1 year and blood sugar as my endpoint. Whem I'm running the proc mixed, it says that 130 out of 400 observations are NOT used. And even though I have 103 patients in my study, it seems to only use data from 75 patiens. I do have some missing variables in my dataset. For instance, I'm adjusting for how long each patient has had the diabetes diagnosis (in a covariate, I think), and when the patients dosn't know, I have entered ND. When I remove that covariate from the proc mixed, SAS seems to "notice" even more of my patients, as the 75 patients increase to 95.   What does this mean? Is SAS only analyzing the blood sugar-numbers from the 75 patients? Or is it informing me that 75 of 103 patients have a complete dataset? Can I count on/belive the statistics?   This is what I have typed in SAS (where "treatment" is treatment with other types of medication that might influence bloodsugar and "years" is number of years with the diabetes diagnosis):   proc mixed data=sasuser.data; where week>0; class patient week group(ref='B') gender treatment years; model bloodsugar= week group Group*week baseline_bloodsugar baseline_bloodsugar*week gender age baseline_bmi treatment years /solution cl ddfm=kr; lsmeans group*week /cl; repeated week / subject=patient type=un r rcorr; run;     I really hope that you can help! Thank you in advance. ... View more

In a placebo controlled randomized study invesigating the effect of a drug on weight, blood pressure ect, I found that there is difference in the baseline weight between the two groups (placebo group and active group). The study has repeated measurements ,and therefore I am using mixed model. To adjust for baseline difference in my outcome weight, I have build this model:   proc mixed data=sasuser.data; WHERE week > 0; class id week treat (ref='2'); model weight = week treat treat*week weight0*treat               /solution CL DDFM=KR; repeated week / subject=id type=un r rcorr; run;   where weight0 is mean weight at baseline for both groups. As far as I understand, I put it as a covariate (ANCOVA).   However, I want to adjust for other baseline differences, such as age and gender. My question is therefore: How do I do that in the model above? Can I have more than one covariate at the same time?   THANK YOU!    ... View more