Dear mkeintz,   You understood my problem so well. Categorical variable is my desired outcome. Thank you so much for your explanation in depth as well as the given optimal solution.   Best, Trang     ... View more

Thank you very much, RW9, for your reply. 2 variables quintiles of Na and K are ordinal variables. They were created from Proc rank with the continuous variables of Na, K.   Sorry I don't have SAS on the current PC so I can't post test data.  I just looked for hints.  I think I'll try your suggested solution with input statement.   ... View more

Quintile of Na Quintile of K NaKgroup 1 1 11 2 2 12 3 3 13 4 4 14 5 5 15     21     22     23     24     25 Dear all,   My data looks as the table above.  I have 2 variables which are quintiles of Na and K (so each of them has 5 levels: 1st, 2nd,...5th quintile). Now I want to create new var as NaKgroup which combines those 2 quintiles variables. This new variable will have 25 categories.   ... View more

Dear Astouding, Thanks a lot for your help. It's so simple! Best, Trang ... View more

Dear Doc_Duke, Thank you very much for your reply. I agree with you. Can I ask you some more questions?   If I use Cox model with time-dependent covariates (for instance,  Sodium is my main interest as a predictor for hypertension, and it varies over time so I want to use the most recent measure of sodium, i.e measure at the time of censor or incident hypertension, in my model), is there any differences in the interpretation of this model compared to the standard Cox model (using baseline value of covariates)? Another problem for my Cox model is that when I treat Sodium as a continuous variable (in grams), the results is highly significant.But when it's put into the model as quartiles, the results become no more significant across categories of quartiles, with lowest quartile as a references (Q2 vs Q1, Q3 vs Q1, Q4 vs Q1). How can we explain for this issue?   Thank you very much for your support. Trang ... View more

Dear Xia, I'm really surprised by your code! It's so short and it creates the same results which I expected. I've more experience now. Thank you very much.  Best,   ... View more

Dear Kurt, I worked it out, thanks to some hint from your code (array and loop). My code is like this: *hbp1-hbp5: hypertension from visit 1 to 5; *hp: hypertension after 5 year follow-up; *year1: last visit with hbp=0 if hp=0; *incvisit: visit with incidence hypertension; *lastzero: last visit with hbp=0 before incvisit; *personyear: person-years; data hbp; input id hbp1 hbp2 hbp3 hbp4 hbp5 hp; datalines; 1 0 0 0 0 1 1 2 0 0 0 . . 0 3 0 . . 0 . 0 4 0 0 1 1 1 1 5 0 . 0 . 1 1 ; run; data want; set hbp; array hbp {*} hbp1-hbp5; i = 1; do while (hp=0 and i<=dim(hbp)); if missing(hbp{i})=0 then year1=i; i+1; end; do while (hp=1 and i<=dim(hbp) and incvisit=.); if hbp{i}=1 then incvisit=i; i+1; end; drop i; run; data want1; set want; array hbp {*} hbp1-hbp5; i = 1; do while (hp=1 and i<=incvisit); if hbp{i}=0 then lastzero=i; i+1; end; drop i; run; data want2; set want1; if hp=0 then personyear=year1; else personyear=lastzero+(incvisit-lastzero)/2; run; proc print; run;   The result with variable "personyear":   Obs id hbp1 hbp2 hbp3 hbp4 hbp5 hp year1 incvisit lastzero personyear 1 1 0 0 0 0 1 1 . 5 4 4.5 2 2 0 0 0 . . 0 3 . . 3.0 3 3 0 . . 0 . 0 4 . . 4.0 4 4 0 0 1 1 1 1 . 3 2 2.5 5 5 0 . 0 . 1 1 . 5 3 4.0 ... View more