Hi all, I posted this question under 'SAS Text and Content Analytics' but suggested it more relevant under here. I have multiple time varying covariates and unfortunately their time intervals (start, stop) are different, so I split time (start, stop) of one variable into interval (T1, T2) that can be matched/merged with other variables. I run the analyses using data before (table 1) and after splitting time (table 2) and hope to get the same results but I don't. *Before time split - table 1; HR=0.587(0.074-4.673) proc phreg data=TABLE1;class treatment (ref=first); model (start, stop)*outcome(0)=treatment / TIES = EFRON RL;run; *After time split - table 2; HR=0.378(0.089-1.60) proc phreg data=TABLE2;class treatment (ref=first); model (start, stop)*outcome(0)=treatment/ TIES = EFRON RL;run;   I really appreciate if any could have explanations and/or how to adjust it? Thank you!   Table 1 ID start stop treatment outcome 1 0 0.0001 0 0 1 0.0001 2.218 1 0 2 0 0.0001 0 0 3 0.0001 2.779 1 0 3 0 2.105 0 0 4 0 5.092 0 0 4 5.092 6.47 1 0 5 0 3.532 0 0 5 3.532 6.494 1 0 6 0 3.603 0 0 6 3.603 6.47 1 0 7 0 2.382 0 1 8 0 0.969 0 1 9 0 0.0001 0 0 9 0.0001 1.18 1 0 10 0 2.642 0 0 11 0 1.254 0 0 12 0 5.07 0 0 13 0 6.494 0 0 14 0 2.661 0 0 15 0 4.279 0 0 16 0 3.671 0 1 17 0 4.66 0 0 17 4.66 6.48 1 0 18 0 6.489 0 0 19 0 2.669 0 0 20 0 4.728 0 0 20 4.728 6.486 1 0 21 0 6.47 0 0 22 0 2.45 0 0 23 0 6.494 0 0 24 0 0.052 0 0 24 0.052 1.41 1 0 25 0 0.0001 0 0 25 0.0001 1.725 1 0 26 0 6.018 0 0 27 0 3.617 0 0 27 3.617 6.453 1 0 28 0 0.0001 0 0 28 0.0001 3.907 1 0 29 0 3.047 0 0 30 0 6.497 0 0 31 0 1.982 0 0 32 0 0.0001 0 0 32 0.0001 1.399 1 0 33 0 0.621 0 0 34 0 2.579 0 0 35 0 6.448 0 0 36 0 5.24 0 0 37 0 4.227 0 0 38 0 1.114 0 1 39 0 4.285 0 1 40 0 1.61 0 1 41 0 3.844 0 1 42 0 2.522 0 1 43 0 1.769 0 1 44 0 3.48 0 1 45 0 0.0001 0 0 45 0.0001 1.667 1 1 46 0 0.0001 0 0 46 0.0001 1.895 1 0 Table 2 ID start stop outcome treatment T1 T2 1 0 0.0001 0 0 0 1 1 0.0001 1 0 1 0 1 1 1 2 0 1 1 2 1 2 2.218 0 1 2 2.218 2 0 0.0001 0 0 0 1 2 0.0001 1 0 1 0 1 2 1 2 0 1 1 2 2 2 2.779 0 1 2 2.779 3 0 1 0 0 0 1 3 1 2 0 0 1 2 3 2 2.105 0 0 2 2.105 4 0 1 0 0 0 1 4 1 2 0 0 1 2 4 2 3 0 0 2 3 4 3 4 0 0 3 4 4 4 5 0 0 4 5 4 5 5.092 0 0 5 6 4 5.092 6 0 1 5 6 4 6 6.47 0 1 6 6.47 5 0 1 0 0 0 1 5 1 2 0 0 1 2 5 2 3 0 0 2 3 5 3 3.532 0 0 3 4 5 3.532 4 0 1 3 4 5 4 5 0 1 4 5 5 5 6 0 1 5 6 5 6 6.494 0 1 6 6.494 6 0 1 0 0 0 1 6 1 2 0 0 1 2 6 2 3 0 0 2 3 6 3 3.603 0 0 3 4 6 3.603 4 0 1 3 4 6 4 5 0 1 4 5 6 5 6 0 1 5 6 6 6 6.47 0 1 6 6.47 7 0 1 1 0 0 1 7 1 2 1 0 1 2 7 2 2.382 1 0 2 2.382 8 0 0.969 1 0 0 0.969 9 0 0.0001 0 0 0 1 9 0.0001 1 0 1 0 1 9 1 1.18 0 1 1 1.18 10 0 1 0 0 0 1 10 1 2 0 0 1 2 10 2 2.642 0 0 2 2.642 11 0 1 0 0 0 1 11 1 1.254 0 0 1 1.254 12 0 1 0 0 0 1 12 1 2 0 0 1 2 12 2 3 0 0 2 3 12 3 4 0 0 3 4 12 4 5 0 0 4 5 12 5 5.07 0 0 5 5.07 13 0 1 0 0 0 1 13 1 2 0 0 1 2 13 2 3 0 0 2 3 13 3 4 0 0 3 4 13 4 5 0 0 4 5 13 5 6 0 0 5 6 13 6 6.494 0 0 6 6.494 14 0 1 0 0 0 1 14 1 2 0 0 1 2 14 2 2.661 0 0 2 2.661 15 0 1 0 0 0 1 15 1 2 0 0 1 2 15 2 3 0 0 2 3 15 3 4 0 0 3 4 15 4 4.279 0 0 4 4.279 16 0 1 1 0 0 1 16 1 2 1 0 1 2 16 2 3 1 0 2 3 16 3 3.671 1 0 3 3.671 17 0 1 0 0 0 1 17 1 2 0 0 1 2 17 2 3 0 0 2 3 17 3 4 0 0 3 4 17 4 4.66 0 0 4 5 17 4.66 5 0 1 4 5 17 5 6 0 1 5 6 17 6 6.48 0 1 6 6.48 18 0 1 0 0 0 1 18 1 2 0 0 1 2 18 2 3 0 0 2 3 18 3 4 0 0 3 4 18 4 5 0 0 4 5 18 5 6 0 0 5 6 18 6 6.489 0 0 6 6.489 19 0 1 0 0 0 1 19 1 2 0 0 1 2 19 2 2.669 0 0 2 2.669 20 0 1 0 0 0 1 20 1 2 0 0 1 2 20 2 3 0 0 2 3 20 3 4 0 0 3 4 20 4 4.728 0 0 4 5 20 4.728 5 0 1 4 5 20 5 6 0 1 5 6 20 6 6.486 0 1 6 6.486 21 0 1 0 0 0 1 21 1 2 0 0 1 2 21 2 3 0 0 2 3 21 3 4 0 0 3 4 21 4 5 0 0 4 5 21 5 6 0 0 5 6 21 6 6.47 0 0 6 6.47 22 0 1 0 0 0 1 22 1 2 0 0 1 2 22 2 2.45 0 0 2 2.45 23 0 1 0 0 0 1 23 1 2 0 0 1 2 23 2 3 0 0 2 3 23 3 4 0 0 3 4 23 4 5 0 0 4 5 23 5 6 0 0 5 6 23 6 6.494 0 0 6 6.494 24 0 0.052 0 0 0 1 24 0.052 1 0 1 0 1 24 1 1.41 0 1 1 1.41 25 0 0.0001 0 0 0 1 25 0.0001 1 0 1 0 1 25 1 1.725 0 1 1 1.725 26 0 1 0 0 0 1 26 1 2 0 0 1 2 27 2 3 0 0 2 3 27 3 4 0 0 3 4 27 4 5 0 0 4 5 27 5 6 0 0 5 6 27 6 6.018 0 0 6 6.018 27 0 1 0 0 0 1 27 1 2 0 0 1 2 27 2 3 0 0 2 3 27 3 3.617 0 0 3 4 27 3.617 4 0 1 3 4 27 4 5 0 1 4 5 27 5 6 0 1 5 6 27 6 6.453 0 1 6 6.453 28 0 0.0001 0 0 0 1 28 0.0001 1 0 1 0 1 28 1 2 0 1 1 2 28 2 3 0 1 2 3 28 3 3.907 0 1 3 3.907 29 0 1 0 0 0 1 29 1 2 0 0 1 2 29 2 3 0 0 2 3 29 3 3.047 0 0 3 3.047 30 0 1 0 0 0 1 30 1 2 0 0 1 2 30 2 3 0 0 2 3 30 3 4 0 0 3 4 30 4 5 0 0 4 5 30 5 6 0 0 5 6 30 6 6.497 0 0 6 6.497 31 0 1 0 0 0 1 31 1 1.982 0 0 1 1.982 32 0 0.0001 0 0 0 1 32 0.0001 1 0 1 0 1 32 1 1.399 0 1 1 1.399 33 0 0.621 0 0 0 0.621 34 0 1 0 0 0 1 34 1 2 0 0 1 2 34 2 2.579 0 0 2 2.579 35 0 1 0 0 0 1 35 1 2 0 0 1 2 35 2 3 0 0 2 3 35 3 4 0 0 3 4 35 4 5 0 0 4 5 35 5 6 0 0 5 6 35 6 6.448 0 0 6 6.448 36 0 1 0 0 0 1 36 1 2 0 0 1 2 36 2 3 0 0 2 3 36 3 4 0 0 3 4 36 4 5 0 0 4 5 36 5 5.24 0 0 5 5.24 37 0 1 0 0 0 1 37 1 2 0 0 1 2 37 2 3 0 0 2 3 37 3 4 0 0 3 4 37 4 4.227 0 0 4 4.227 38 0 1 1 0 0 1 38 1 1.114 1 0 1 1.114 39 0 1 1 0 0 1 39 1 2 1 0 1 2 39 2 3 1 0 2 3 39 3 4 1 0 3 4 39 4 4.285 1 0 4 4.285 40 0 1 1 0 0 1 40 1 1.61 1 0 1 1.61 41 0 1 1 0 0 1 41 1 2 1 0 1 2 41 2 3 1 0 2 3 41 3 3.844 1 0 3 3.844 42 0 1 1 0 0 1 42 1 2 1 0 1 2 42 2 2.522 1 0 2 2.522 43 0 1 1 0 0 1 43 1 1.769 1 0 1 1.769 44 0 1 1 0 0 1 44 1 2 1 0 1 2 44 2 3 1 0 2 3 44 3 3.48 1 0 3 3.48 45 0 0.0001 0 0 0 1 45 0.0001 1 1 1 0 1 45 1 1.667 1 1 1 1.667 46 0 0.0001 0 0 0 1 46 0.0001 1 0 1 0 1 46 1 1.895 0 1 1 1.895 ... 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Hi All, I don't know a right topic for my question. Hope it reaches many of you. I have been in the forum asking on counting process survival analysis. I have made some progress on it. Thanks for the help! I posted one question on merging variables which have different time interval (T1,T2). I guess it is tricky for everyone. So I split time (start, stop) of one variable into interval (T1, T2) that can be matched with other variables. Below in table1 is the original one with treatment, start and stop, and outcome. And I split start and stop time in table 2 (T1,T2) so I can merge it with other variables. I hope that the result will be similar, but it is not: HR using table 1 is slightly larger than that from table 2.  I really appreciate if any could have explanations and how to adjust it? Thank you! Hao     Table 1 ID start stop outcome treatment 1 0 0.0001 0 0 1 0.0001 2.218 0 1 2 0 0.0001 0 0 2 0.0001 2.779 0 1 3 0 2.105 0 0 4 0 5.092 0 0 4 5.092 6.47 0 1 5 0 3.532 0 0 5 3.532 6.494 0 1 6 0 3.603 0 0 Table 2 ID start stop outcome treatment T1 T2 1 0 0.0001 0 0 0 1 1 0.0001 1 0 1 0 1 1 1 2 0 1 1 2 1 2 2.218 0 1 2 2.218 2 0 0.0001 0 0 0 1 2 0.0001 1 0 1 0 1 2 1 2 0 1 1 2 2 2 2.779 0 1 2 2.779 3 0 1 0 0 0 1 3 1 2 0 0 1 2 3 2 2.105 0 0 2 2.105 4 0 1 0 0 0 1 4 1 2 0 0 1 2 4 2 3 0 0 2 3 4 3 4 0 0 3 4 4 4 5 0 0 4 5 4 5 5.092 0 0 5 6 4 5.092 6 0 1 5 6 4 6 6.47 0 1 6 6.47 5 0 1 0 0 0 1 5 1 2 0 0 1 2 5 2 3 0 0 2 3 5 3 3.532 0 0 3 4 5 3.532 4 0 1 3 4 5 4 5 0 1 4 5 5 5 6 0 1 5 6 5 6 6.494 0 1 6 6.494 6 0 1 0 0 0 1 6 1 2 0 0 1 2 6 2 3 0 0 2 3 6 3 3.603 0 0 3 4 6 3.603 4 0 1 3 4 6 4 5 0 1 4 5 6 5 6 0 1 5 6 6 6 6.47 0 1 6 6.47 ... View more

Hi All, Have you ever run the PROC PHREG in which you include more than one time varying covariate using counting process? I understand that in counting process approach, for each variable (x, y), we need to restructure data into: event, start, stop, and var (x,y)/covariate value. How can we run a multivariate model including x and y with 2 different data set, and more complicated if we have many more variables? I really appreciate any help on this. Thanks so much! Hao FU_TIME Event X1 … X5 Y1 … Y5   ... View more

Hi all, I have a very basic question. I have long line of codes and would like to make it in multiple lines to see them easily. I remember that I used // before but it does not work. Thanks!   ... View more