Like @Rick_SAS pointed out, the estimates in the Parameter Estimates table have interpretations that might not be what you think. You did not capture the estimate for Intercept in the Parameter Estimates table, which is important. Please refer to the following usage note for more explanations -- https://support.sas.com/kb/38/384.html   Your ESTIMATE statement estimates the response for the first level, which is level 0. If you want to estimate the response for the reference level (which is also the estimate for Intercept in the Parameter Estimates table), you should use -- estimate 'rs1 1' intercept 1 rs1 0 0 1;   Hope this helps. Jill ... View more

You would use PROC LOGISTIC to fit your model with similar ESTIMATE statements to get what you wanted. The results are in the logit scale. ... View more

You can also consider using the ESTIMATE statement in PROC GLM to estimate what you wanted. For example, if the model is -- model y=x1 x2 x3;   then estimate 'change in y for 10 units change in x1' x1 10; estimate ' change in y for sd=3.4 units change in x1' x1 3.4;   might get what you wanted.     ... View more

What exactly do you want to estimate for A?  Depends on what you want to estimate, you might use the S option in the MODEL statement to get the slope estimate for A, or use the ESTIMATE statement. For example, proc glimmix data = dataset; class hospid month; model B = A ; random month / residual subject = hospid type=ar(1); estimate 'response when A=5' int 1 A 5; run; ... View more

A negative value of UN(2,1) indicates that the covariance is negative. A negative covariance is okay. ... View more

proc glimmix data = dataset; class hospid month; model B = A ; random month / residual subject = hospid type=ar(1); run; ... View more

You have two levels of correlations, family and subject. Your program above does not model the correlations at the family level, and over-specified the correlations at the subject level. Random intercept refers to a model such as random int / subject=A. If you must fit a random intercept model, and also must have a UN structure somewhere in there, then you might consider a model similar to the following -- PROC MIXED DATA = PHENOTYPE_PGS_STD; CLASS DBID FAMILY TIME GENDER; MODEL Z_IMM_MEM = PGS_APP_DOLD AGEATVISIT GENDER VISNO / CHISQ S DDFM = BW; RANDOM INTERCEPT VISNO / TYPE = UN SUBJECT = FAMILY G; RANDOM INTERCEPT VISNO / TYPE = UN SUBJECT = DBID(FAMILY) ; RUN; If you must fit a UN covariance structure in the REPEATED statement, then there is no way I am aware of to make it a typical random intercept model. I hope that makes sense. Jill   ... View more

zjppdozen, The within-subject correlations are modeled by the REPEATED statement. The within-family correlations are modeled (indirectly) by the RANDOM statement (plus the REPEATED statement for certain observations within a family). The random intercept for family is modeled in your RANDOM statement. The random intercept for subject is not explicitly modeled in your program. But if you used TYPE=CS in the REPEATED statement, it essentially fits the same model as random int / subject= DBID(FAMILY); therefore can be considered being modeled. You could change the REPEATED CS statement to this random intercept statement to make it explicit. If you used other types, such as type=ar(1) or type=UN, etc, then there is no random intercept being modeled. You are not modeling random intercept for subject, instead, you are using the REPEATED statement to model the correlations directly, and it is an alternative model to a random intercept model for subject. ... View more

Inversely related does not mean it is the inverse. Scale is in the denominator of the variance formula, that is why they are inversely related. But they are not inverse of each other. ... View more