The Viya explorer has nothing in it. We cannot upload files. ... View more

Yep, the PROC LIFETEST procedure produces a Kaplan-Meier curve plot. Good idea. Thanks. ... View more

@Ksharp Hello, Ksharp. Thank you for your answer. Would you mind if you tell me whether it is appropriate to use only one of the random and repeated statements?   I think either the random or repeated statement can adjust the intra-individual relationship.   Tom ... View more

@sbxkoenk Much appreciate for your additional information. I can see that you do researches about INR, haha.   Below is my codes. I don't know what is wrong. Another thing is that I switched from SAS 9.4 to SAS Viya (update: I have tested these code lines on SAS OnDemand, and no CL bands there on cumhaz graph).   proc phreg data = cox_raw plots(overlay cl) = cumhaz; class genderC(ref = '0') AF(ref = '0') Hypertension(ref = '0') CHF(ref = '0') akiC(ref = '0') ddiC(ref = '0') indicationC(ref = '0') CYP2C9code(ref = '0') VKORC1Code(ref = '0') Warfarin_History(ref = '0'); model timeToFirstInrAttain*firstInrAttain(0) = bsa ageY genderC AF Hypertension CHF serumalb akiC ddiC indicationC CYP2C9code VKORC1code Warfarin_History / rl=wald; baseline covariates = predict_cox; run;   ... View more

Update:   When plots option, overlay & cl, are used together, no CL bands. It shows CL bands when plots option has only cl. proc phreg data = cox_raw plots(overlay cl) = cumhaz;   ... View more

@sbxkoenk Hi, pal. Thank you for your reply. Oh! I thought the CL option in plots(overlay cl) was the prediction interval. Probably, I was misled by chatGPT, I think. I remember chatGPT told me CL (confidence limits) and CI (confidence intervals) were different. At the same time, my data showed a very wide band, which led me to the wrong conclusion.   Thank you for your clarification. Appreciated! ... View more

@FreelanceReinh Thanks for your further explanation. I guess if we have more than 3 time-varying variables, this approach would be very laborious. I addition, the PROC TRANSPOSE might experience difficulty when transferring a wide dataset to a narrow dataset, given there is more than one time-varying variables (e.g., A_wk1, A_wk2, ... A_wkm, and B_wk1, B_wk2, ... B_wkn). ... View more

Thanks for the notice, @FreelanceReinh    Great! And I have one more question. If the two time-varying variables change on the same day (tie), how do we count them? Only two rows with a same start and stop time? Thank you. ... View more

Hi, @FreelanceReinh    Thank you for your suggestion. Hmmm, it's a great idea to deal with multiple time-dependent variables. Please let me reproduce your idea.   First, via the counting process method. Say, we have two time-varying variables named A and B, and there are other fixed variables which is represented in union by U. If there is an individual who was followed for 14 days, for whom the A changed on day 7 from 0 to 1. In addition, the B changed on day 4 from 1 to 0. According to my understanding of your idea, there should be three rows for this individual in the overall table and they are:   row one: A=0, B=1, start=0, stop=4 row two: A=0, B=0, start=4, stop=7 row three A=1, B=0, start=7, stop=14   Therefore, every change in the time-varying variable produces an extra row for the individual, given that the time-varying variables do not change on the same day (i.e., tie).   I have not yet thought about the programming method and if I have ideas about it I would update this post. Again, thank you for your feedback.   Tom ... View more