Hi:   I am working on a study. It has been planned to do multiple imputation for missing values related to the primary endpoint by an agency's requirements. I have read many materials online and have some ideas. But, I am still not sure.    Study information: The study's indication is Epilepsy. Each patient is given a diary and they are supposed to record how many seizures they expeirenced each day during the study period (DB period is ~ 85 days).  As you can image, some patients may forget to record their seizures on some days and some patients may discontinue from the study before the Day 85. So there are missing values for seizure counts on some days for some subjects.     My questions: 1. Our missing type is considered as 'missing as random' and so this procedure(Proc MI) can be used, correct? 2. Since our missing data is only in one variable, i.e., seizure count, so I think I should NOT use the methods in SAS documentation (Imputation Methods, Table 5) with 'monotone', correct? 3. Since our data is 'seizure count', which should follow poission distribution (correct?), not normal, I should NOT use methods with 'MCMC' since MCMC method is based on the assumption of multivariate normal distribution (MVN) for variables, correct? 4. Then I thought I should use FCS,  fcs reg, or fcs regpmm. I read SAS documentation, it has "The predictive mean matching method ensures that imputed values are plausible; it might be more appropriate than the regression method if the normality assumption is violated (Horton and Lipsitz 2001, p. 246)." So I thought I should use 'fcs regpmm'. I also tried 'fcs reg', the imputed values gives non-integer, a number with decimal. It seems it does not fit my case. Our seizure is an count; so it should be an interger. If I use 'fcs regpmm', the imputed values are integers.   5. If using 'fcs regpmm' is correct for my case, what number of 'k' (SAS option with 'fcs regpmm' option) should I pick?    Here is the code I use.    proc mi data = post nimpute = 25 out = post_mi  seed = 54321 noprint;  by subjid; var qsdy count;' fcs regpmm (/k = 5); run;   Note: 'qsdy' is the study Day variable; it is from Day 1 till Day 85. 'count' is seizure count for each day. There are missings in this variable.  Note: since the imputation is by subjid, so covariates such as age, treatment, etc, are not needed (no change for an individual), correct?   If any detailed information is needed for this discussion, please ask me.   Thanks a lot in advance.    Xiaoshu   ... View more

when use proc logistic procedure to set up default model in bank risk analysis, for the current customer, there are 4 kinds of results after application for the loan, accept, refuse , cancel or nouse. when set up model, to analysis if new customer will default or could approve their application of loan, we will use the current customer loan history. refused=1, accept=0 but how to deal with cancel and nouse, i think nouse looked as accepted, but for cancel, how to deal with it?  ... View more

I tried to edit my post at https://communities.sas.com/t5/New-SAS-User/PROC-GLMSELECT-testing-incomplete/m-p/808149#M33667 a few minutes later, and was unable to do so. The "Edit" action is no longer available from the dropdown menu. Screen capture showing I am logged in (top right) and drop down menu that doesn't allow editing.   ... View more

    refer to an old post below. i searched , but can't find any sample program. can anyone post a specific link for sample program? thanks.   Solved: Nonmem Program - SAS Support Communities ... View more

Why can I create any names of variables in JMP but I can't do that with SAS?     ... View more