Hi! I am running statistical procedures (proc mixed/glimmix) on repeated measurements (Time = 1 - 5) of blood metabolites in calves, I have three treatment groups (Treatment = A, B, C). Repeated measurements for "C" are only available for Time 3 to Time 5 (and unavailable for Time 1 and 2). I am assessing differences between treatment groups for every time-point, and am also assessing changes over time within each treatment group, Interactions of treatment*time do not converge correctly due to missing values for "Treatment C" at Time 1 and Time 2. Therefore I have so far had to separate the analyses and assess for differences between treatment groups for every timepoint separately. My current model is: proc mixed data=BLOOD plots=none; by Time; class calf treatment breed sex origin ; model Glucose = treatment age breed sex origin/ ddfm=kr residual outp=predresid; lsmeans treatment/ adjust=Tukey; run; I am dealing with a base of covariates, in particular age (continuous), breed (1, 2), origin (1, 2), and sex (M, F). The problem is that differences in covariates breed, origin, and sex are confined to Treatments A and B, whereas all calves are of similar breed, origin, and sex in Treatment C. Due to the limited repeated measures in treatment C, and non-applicable covariates, it has been suggested to me that I need to split up this analysis. Is it possible to account for covariates in treatments A and B by running an initial model (y = breed + origin + sex), and using estimated values from the ods output in a second model where all treatment groups are included? Example model 1: proc mixed data=BLOOD plots=none; by time; where treatment < 3; class calf origin breed sex; model Glucose = origin breed sex / ddfm=kr residual outp=predresid; random calf; run; To then run the following model including all treatment groups: proc mixed data=BLOOD plots=none; by sample; class calf treatment ; model Glucose = treatment age / ddfm=kr residual outp=predresid; lsmeans treat/ adjust=Tukey; run;   I’m not sure if this method accounts for the covariance correctly, as the main treatment effect and age are not included in the initial model. But I also don’t know if the very first model will run correctly if three of the covariates are restricted to two of the three treatment groups. Would anyone have any advice on how to account for these unbalanced covariates? ... View more