I am not going to address the first question, leaving it for Robin or Dale. I will suggest that if you have multiple visits of the same patient through time, the CS error structure is unlikely to provide the best fit. Look into the time series (i.e., AR, ARH) and spatial power (i.e., SP(POW)) error constructs.

As for the second question, look at the SLICE option or the ESTIMATE/CONTRAST statement. Both should allow you to conduct the test you are interested in.

Warren Schlechte

-----Original Message----- From: mkhah@JSSRESEARCH.COM [mailto:mkhah@JSSRESEARCH.COM] Sent: Monday, February 16, 2009 12:26 PM Subject: proc mixed model

For my first experience in proc mixed , I want t o run mixed model for change in serum creatinin in different visit time, I consider it as repeated measure case . I have some variable such as pt_id sex renal_stratification visit treatment_type period (difference between visit date and baseline date) serum_creatinine (baseline) serum_creatinine( in each visit) and dif_serum_creatinine(change of serum creatinine at each visit after baseline). I have to show the actual value of serum_creatinine in each visit, change of serume creatinine compare to baseline and percentage of serum creatinine change in two treatment group (TX group and placebo).

My questions:

1. whether the statement that I am using is correct.

PROC MIXED DATA=data file METHOD=reml; CLASS pt_id sex renal_stratification visit treatment_type period; MODEL dif_seume_creatinine=seume_creatinine_baseline sex renal_stratification visit treatment_type period treatment_type*period; REPEATED period/sub=pt_id R RCORR TYPE=CS; RUN;

2. How can I find whether there is statistically significant difference between seume_creatinine change compare with baseline in particular visit, let say I have 6 equal visit points after baseline?

Thank you for your help, Mo