Date: Wed, 23 May 2007 11:16:15 -0500 Reply-To: "Swank, Paul R" Sender: "SAS(r) Discussion" From: "Swank, Paul R" Subject: Re: GLIMMIX and 3 random effects (nested?) Comments: To: Martina Pavlicova In-Reply-To: <200705230418.l4MIorTH030185@malibu.cc.uga.edu> Content-Type: text/plain; charset="us-ascii" The least squares means you get from GLIMMIX need to be back-transformed. Given that, they will likely be smaller than the raw means because of the skew. They will be more like geometric means. Second issue. Using an AR(1) for of the variance covariance matrix is not helpful with two time points since it assumes that as measures get firther apart in time, the correaltion between measures will be reduced. But with two time points, there is only one time difference. The question is whether the variances at the two time points are the same. If so, then compund symmetry should work. If not then UN (unstructured) might be better. Paul R. Swank, Ph.D. Professor Director of Reseach Children's Learning Institute University of Texas Health Science Center-Houston -----Original Message----- From: SAS(r) Discussion [mailto:SAS-L@LISTSERV.UGA.EDU] On Behalf Of Martina Pavlicova Sent: Tuesday, May 22, 2007 11:18 PM To: SAS-L@LISTSERV.UGA.EDU Subject: GLIMMIX and 3 random effects (nested?) Hi, I am quite new to SAS and modeling and appreciate any help. The goal of my analysis is to detected difference between two treatments on 500 patients (equally split between treatment A and treatment B). The outcome is a number of certain occasions per month (many has 0 but some has very high number of occasions). I assume that the outcome has Poisson distribution and use following model PROC GLIMMIX data=workingdata ORDER=INTERNAL; CLASS TREATMENT TIME SITE_id COHORT PATIENT_id; MODEL Outcome = TREATMENT TIME TREATMENT*TIME / dist=poisson solution; However, I do not know how to model random effect. The patients were recruited at 12 SITES (S1, S2, ...S12), and were split for the treatment into cohorts. In each site, there were 9 to 11 cohort and each cohort about 5 patients. Each patient was observed at 2 time points (TIME=1,2). Thus that means that I have 3 random factors: SITE, COHORT and PATIENT. Obviously, the COHORT is nested within SITE. Since the subject was observed twice in time, I thought about modeling it as AR(1). SITE_id ... are unique ids of sites PATIENT_id ... are unique ids of patients COHORT ... are number of cohort, but not unique. COHORT=1 is first cohort but is nested within the site. Here is my attempt: RANDOM SITE_id COHORT(SITE_id); RANDOM PATIENT_id / type = AR(1); I tried to check my model by comparing the observed means by TIME and TREATMENT with predicted means using statement LSMEANS TREATMENT*TIME / pdiff; but the results I am getting are very much lower than the observed means. I am also unsure about a statement RANDOM _residual_; as it seems needed in the model. I really appreciate any comments. Thank you very much, Martina Pavlicova

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