Hi All,   I am used the automated variable selection (stepwise method) with a SLE of 0.2 and a SLS of 0.05. I am getting variables in my final model that are >0.20 in my univariate analysis. I am wondering why that is, is it because those variables are significant in the presence of other variables and they get entered in one of the middle steps? ... View more

I have longitudinal data similar to below:   ID   GROUP   DATE          FLAG 1     A              1/1/2020       1 1     A              1/2/2020       1 1     B              1/3/2020 2     A               1/1/2020        2     A                1/8/2020 2     B               1/4/2020       1 2     B                1/1/2020       1   I want to flag and and further remove observations for the same ID and GROUP where the dates are within 6 days of each other (plus or minus). How may I do this? ... View more

Hello, I am running a stacked hbar and my data is structured longitudinally: ID GR TIME  COMP 1    TX    2     X 1     P     3     X 2     TX   6 2     P     3   Thus each bar in my figure represents each subject's total duration on study stacked by treatment group. What I would like to do is: 1) Add a symbol to the end of the bar to indicate those in compliance only. I created a new variable COMP that was X if compliance is met and blank if not. I was planning to run DATALABEL=COMP to have that "symbol" show up but it is not working for some reason.. I just ran DATALABEL and it did work, so I am not sure why the = does not. I am using SAS 9.4. 2) Add another "SEGLABEL" to indicate the % of the total time that the subject was taking TX. So for ID 1, it should be 66.7%. How may I do this?   proc sgplot data=dat;      hbar id / response = time group = gr groupdisplay=stack stat=sum datalabel=comp; run;   Thanks to all who help! ... View more

I am running a negative binomial regression on various predictors of a count outcome and want to be able to make a conclusion on which one is the "best" at predicting the counts. Are there any metrics that can be compared between models to make this conclusion? I was thinking psuedo r-squared, but understand that that can't be interpreted the same as a regular r-square. Any tips? ... View more

I have clustered data (right and left hand - not all patients contribute both) and I am trying to run a multinomial model in GLIMMIX. My categorical outcome is actually ordinal in nature (5 categories self rating, about 400 observations total), but I am also okay with it being treated as nominal depending on better model fit. My questions are: 1) How do I check the proportional odds assumption using GLIMMIX? 2) I am running the dist=multinomial with both the clogit and glogit links but I cannot run a model without some sort of a error or note in the log. I either get "NOTE: At least one element of the (projected) gradient is greater than 1e-3." or "NOTE: Estimated G matrix is not positive definite." singly or both together when a tweak the model using various options such as "method=laplace, nloptions MAXITER=5000 gconv=0." I was hoping someone could provide some general advice on what else I can do to fix the errors?    Thank you.  ... View more

I have a continuous outcome measure. The measurement was taken under 3 different conditions, in 2 locations on the body for each patient in the study. We are looking at the measurement also in 2 different ways: and average across all the "readers" and then also 1 "skilled" reader. Our research question is, is the average measurement taken from all readers as good as distinguishing between the 3 different conditions as the 1 skilled reader? Also, are the measurements taken from the 2 locations significantly different? I am not able to figure out how to write my mixed model for my random effects and fixed effects to answer these questions and would be very appreciative of any suggestions.    I suppose the data looks something like this:   ID  Y  Condition   Location   Reader 1        A                A                1 1        B                A                1 1        C                A                1 1        A                B                1 1        B                B                1 1        C                B                1 1        A                A                2 1        B                A                2 1        C                A                2 1        A                B                2 1        B                B                2 1        C                B                2 ... View more

I have a really large dataset that looks like this:   ID   A B C -- ZZ A_new B_new C-new -- ZZ_new 1     0 1 1 --   1    1          1         0              0 .. ..     I want to perform a test of agreement between all variables of the same name + the suffix   i.e.   proc freq data=dat;      table A*A_new / agree;      table B*B_new / agree; .. run;   I dont think we can use an array to do a SAS procedure - but is there another way to do this without have to write down every variable name?         ... View more