Hi folks; I want to fit a repeated measure logistic regression with GLIMMIX and get the predicted class labels. The data was ordered by "id", the response is 0-1 coded variable "y", and there is one class variable "pwp". The code looks like this: ods html close; ods html; proc glimmix data=test;   class pwp;   model y= pwp /dist=binary link=logit ddfm=kr2 solution ;   random _residual_ / subject=id;   output out=out1 pred(ilink)=predicted;  *??; run; However I get confused about "OUTPUT keyword< (keyword-options) > <=name >" part of the code. There are four options available: 1. Linear predictor (default) 2. Marginal linear predictor (NOBLUP) 3. Predicted mean (ILINK) 4. Marginal mean (NOBLUP ILINK), and I don't know which of them should be used to calculate predicted class labels. Any useful hints would be highly appreciated. Issac ... View more

Hi folks; Have been doing on a project aims to predict physician workload (in terms of Primary Care Relative Value Unit) based on some patient demographic attributes and, more importantly, illness types. One problem, or I though to be a problem, is that for each row (patient), there is a set of 1s and 0s across disease columns (1= the patient had the illness during the past year and had got visited for it) but there is only one cell for PCRVU accounted for all physician time spent on total visits over the past year. So we cannot say how much of PCRVU is related to, say Diabetes, and how much of it is related to, say Vascular problem, and so on. Now we think the job can be done in two levels, first is how to split PCRVU among the disease types and second how to analyze divided PCRVUs with multivariate GLIMMIX. The reason for selecting GLIMMIX lies in the fact that there is no guarantee that the PCRVU follows approximately normal and also we do have to include such random effects like administrative region  (VISN) for in order to account for potential heteroscedasticity and possible correlations within clusters of patients. Now I 'm asking whether there is a way to address this problem in SAS 9.3. Any helpful comments would be highly appreciative. P.S. a sample excel file is attached. ACC001 through ACC030 are the disease types. Thanks! Issac ... View more

Hi folks; I've run gam procedure to a data set and got the following output table. I wonder how I can calculate a pseudo R-Squared with the help of deviance. Number of Observations 49988 Number of Missing Observations 12 Distribution Gaussian Link Function Identity Final Number of Backfitting Iterations 4 Final Backfitting Criterion 1.6618101E-9 The Deviance of the Final Estimate 240768.87317    The backfitting algorithm converged. Thanks! Issac ... View more

Hi folks! Have been working on a VA data analysis project that aims to predict the workload needed for a future inpatient based on his/her demographic, DRG, and health care characteristics. The response variable is PCRVU (Primary-Care relative value unit for all primary care visits during the year) which is continous, and we have a cross-sectional data set pulled out of, up to now, 6 different VA facilities. The are a number of independent variables, that can be grouped as 1.health related (such as Inpatient Days (LOS), CanScore (severity of the patient illness), assigned provider, etc.) 2. patient demographic attributes (such as zip code, gender, insurance status, etc.) and 3. war-related columns (such as radiation status, agent orange status, etc.). Previous attempts were using SAS E-Miner for OLS regression and CART that could not yield a reasonable R-Square. I'm thinking to use GLMM or GAM procedures but not pretty sure the way to approach the problem. Any helpful/professional comment would be appreciative. Thanks! Issac ... View more