My survey data has group variables such as A1-A10, B1-B2, C1-C5, D1-D15...so forth so on. And I'd like to exclude 😧 in following data step. 😧 has missing (.) . I don't need to shift 😧 by +1 to the right.  I'm using SAS 9.4.     data have1(drop=i); set have; array shift[*] _numeric_; do i=1 to dim(shift); if shift(i) not in ('D:') then shift(i)=shift(i)+1; end; run; data have1(drop=i); set have; array shift[*] _numeric_; do i=1 to dim(shift); shift(i)=shift(i)+1; end; where _numeric_ not in ('D:'); run; The codes above didn't work.  Any suggestions please?  Thanks for your time.  ... View more

 I have to select patients who has specific combinations of conditions as following:     1. patients with both P2 and the Pa;          OR 2. patients with both the P3 and the Pa;   Patients must have P2 and P3 with the Pa to be eligible. Simply put: P2+Pa, P3+Pa, P2+P3+Pa combinations are acceptable.      data combinations; set have; array diagnosis[*] condition othercondition_1-othercondition_24; do i = 1 to dim(diagnosis); if diagnosis[i] in ('P2','P3') then x=1; if diagnosis[i] in ('Pa') then y=1; if x=1 and y=1 then output; end; drop i; run; However, resulting data "combinations" has an information for single subject (row) repeated 20 times. Very strange. Any idea why that happened?    LOG:  There were 95560 observations read from the data set WORK.HAVE. NOTE: The data set WORK.COMBINATIONS has 20 observations and 66 variables.   Using SAS 9.4.  ... View more

Could you please help understand why Time*Group interactions are not quantified? in the proc mixed model (See SAS output in Fig 2 below)? What am I missing here?   Let me give you a background of this: I'm working with randomized clinical trial with wait-list control where wait-listed subjects eventually received identical treatment as controls had. Fig 1 shows trial scheme. Please note that this is a small pilot study with unbalanced, missing, loss in follow up and potential that wait-listed subjects were not happy to be wait-listed..et.c issues. However, for the benefit of learning, I wanted to use the data and thought mixed model would help. See code below and "sas_forum_data.csv" is attached. In Fig 1, "time" variable is coded after number of weeks passed until each groups receive treatment (David Howell, Reference at the end). "group" variable is coded 1 for wait-listed subjects vs 2 for treatment group. SAS version here is 9.4.   proc mixed data = sas_forum_data; class group1 time1 subj; model pcs = group1 time1 group1*time1/solution; random subj(group1); repeated time1/ type = AR(1) subject = subj(group1) rcorr; run;   Fig 1: Fig2:   Reference: http://www.uvm.edu/~dhowell/StatPages/Mixed-Models-Repeated/Mixed-Models-for-Repeated-Measures1.html   ... View more

Anyone could help understand and make sense of a standard code below?   I have a survey data that has 8 items (series of questions). I chose one of them (PF=physical functionality) for demo purpose and attached for sample data. It's consisted of 10 questions (PF01-PF10) and has 3 response options.       0-No, not limited at all       1-Yes, limited a little       2-Yes, limited a lot   I'd like to score the responses and below is the standard code recommended.      ARRAY PFI(10) PF01-PF10; PFNUM = N(OF PF01-PF10); IF PFNUM GE 5 THEN RAWPF = SUM(OF PF01-PF10); PF = ((RAWPF - 10)/(30-10)) * 100; LABEL PF = 'PHYSICAL FUNCTIONING (0-100)' RAWPF = 'RAW PHYSICAL FUNCTIONING';   i'm fine with this part to treat the subject's response for the item missing unless responded to mor ethan 5 out of 10 questions. IF PFNUM GE 5 THEN   However, the code part below doesn't make sense to me. PF = ((RAWPF - 10)/(30-10)) * 100; I keep asking myself: Overall, how part: PF = ((RAWPF - 10)/(30-10)) * 100; make sense, conceptually? Why would you subtract levels of series of question (10) from the total amount of response values (RAWPF) ? Why would you subtract levels of series of question (10) from the total number of possible responses (10*3=30)?   Anyone experienced in scoring survey data? and let me know if this approach makes sense to you? if yes, do you mind explaining how? if not, any better approach you'd recommend? I did a lit review with no success yet.   Once all responses are scored I will Z-transform them so that standardized response scores can be summed across different items. ... View more