About zihdonv19

zihdonv19 · ‎10-04-2024

Thank you. It works!

zihdonv19 · ‎10-03-2024

Hi! The ultimate aim of this data cleaning is to find out treatment pattern of this population (as you said, medication change is one the important things that we want to capture). In this thread, I want to ask how to merge the medication records/corresponding prescription time for the same medications (e.g., OAD), so the dataset won't look so redundant. In my data, the start_med and next_med are without missing values. Hope it clarifies a bit.

zihdonv19 · ‎10-03-2024

Hi, sorry for the typo. I mean "start_med=next_med=next_med in next row".

zihdonv19 · ‎10-03-2024

I have a dataset like this: data medication; infile datalines dsd truncover; input id $ start : ddmmyy10. next : ddmmyy10. start_med $ next_med $; format start next yymmdd10.; /* Change format to yymmdd10. */ datalines; 1,10/10/2006,09/09/2007,insulin,OAD 1,09/09/2007,10/10/2007,OAD,OAD 1,10/10/2007,16/01/2008,OAD,OAD 1,16/01/2008,06/08/2009,OAD,insulin 1,06/08/2009,30/08/2009,insulin,insulin 2,10/10/2006,09/09/2007,insulin,OAD 2,09/09/2007,10/10/2007,OAD,OAD 2,10/10/2007,16/01/2008,OAD,OAD 2,16/01/2008,19/01/2008,OAD,OAD 2,19/01/2008,06/08/2009,OAD,insulin 2,06/08/2009,30/08/2009,insulin,insulin ; run; For each id, I want to merge their date variables if start_med=end_med=end_med in next row. In this example, the desired dataset should be: data medication1; infile datalines dsd truncover; input id $ start : ddmmyy10. next : ddmmyy10. start_med $ next_med $; format start next yymmdd10.; /* Change format to yymmdd10. */ datalines; 1,10/10/2006,09/09/2007,insulin,OAD 1,09/09/2007,16/01/2008,OAD,OAD 1,16/01/2008,06/08/2009,OAD,insulin 1,06/08/2009,30/08/2009,insulin,insulin 2,10/10/2006,09/09/2007,insulin,OAD 2,09/09/2007,19/01/2008,OAD,OAD 2,19/01/2008,06/08/2009,OAD,insulin 2,06/08/2009,30/08/2009,insulin,insulin ; run;

zihdonv19 · ‎05-03-2024

I tried ("cou" is 1,2,3,4,5.....; "var" is continuous variable with decimal points): proc sql; create table mt as select cou, pctl(75, var) as var_p75 from check_1 group by cou; quit; But it returns me with multiple rows per cou.

zihdonv19 · ‎04-23-2024

I have several datasets that I want to use proc sql to join. but here to simplify, I use 3 datasets as example: data dat1; input id x1; cards; 1 1 2 0 3 0 ; run; data dat2; input id x2; cards; 2 1 3 1 5 0 ; run; data dat3; input id x3; cards; 3 0 4 1 6 0 ; run; I want to merge them based one id, with one id per row, the output should be something like this: id x1 x2 x3 1 1 . . 2 0 1 . 3 0 1 0 4 . . 1 5 . 0 . 6 . . 0

zihdonv19 · ‎03-15-2024

I want to fit a model below, where edu is a 3-level variable, cou is a binary variable: proc genmod data=cohort descending; class id edu (ref='1'); model cou=edu female / dist=poisson link=log; repeated subject=id/type=ind; estimate 'edu 2 vs. edu 1' edu 0 1 0; estimate 'edu 3 vs. edu 1' edu 0 0 1; ods output Estimates=outtab; run; But the results viewer showed "Non-est" in the contrast estimate results table. I found people always refer to this note (https://support.sas.com/kb/24/447.html) for relevant questions, but I felt so lost after reading this. I just want to know: edu 3 vs. edu 1, RR=....95%CI=... edu 2vs. edu 1, RR=....95%CI=...

zihdonv19 · ‎03-15-2024

What I want to estimate is: When sex=male, medication group vs. non-medication group, RR=... When sex=female, medication group vs. non-mediaction group, RR=...

zihdonv19 · ‎03-15-2024

I'm trying to do a stratified analysis (by sex) using proc genmod. But it always gives error message, no matter I used "by" or make male/female in separate datasets: WARNING: The generalized Hessian matrix is not positive definite. Iteration will be terminated. ERROR: Error in parameter estimate covariance computation. ERROR: Error in estimation routine. WARNING: Output 'Estimates' was not created. Make sure that the output object name, label, or path is spelled correctly. Also, verify that the appropriate procedure options are used to produce the requested output object. For example, verify that the NOPRINT option is not used. The code I use: proc genmod data=temp descending; by sex; class id; model qtsmo=agecat byear medication/ dist=poisson link=log; repeated subject=id/type=ind; estimate 'RR medication vs. non-medication' medication 1 /exp; ods output Estimates=outtab; run; Is it possible to do stratified analysis using proc genmod? What might be potential solution in my case? Thank you.

zihdonv19 · ‎03-01-2024

Thank you everyone @Reeza @Tom @ballardw @yabwon @PaigeMiller , I generated the following to achieve my goal. data exdata; input sex drug $; datalines; 0 A 1 B 1 C 0 A 1 B 0 C ; run; proc contents data=exdata; run; proc format; value sex 0='Male' 1='Female'; run; proc format; value $drug 'A'='Drug A' 'B'='Drug B' 'C'='Drug C'; run; data exdata_label; set exdata; format sex sex. drug $drug.; label sex = 'gender' drug = 'Type of Medication'; run; proc sql; create table part1 as select name, label as label_var, type, format from sashelp.vcolumn where libname='WORK' and memname='EXDATA_LABEL'; quit; proc format cntlout = format_data (keep=fmtname start label); select sex $drug; run; data value; set format_data; name = lowcase(fmtname); drop fmtname; run; proc sql; create table var_info as select a.name, b.label_var, a.start, a.label, b.type, b.format from value as a left join part1 as b on a.name = b.name order by name, start ; quit;

zihdonv19 · ‎03-01-2024

Thanks for your suggestion. I was just surprised SAS does not have this kind of coding... But do you think making the following table is feasible in SAS? That is: I want to display values for all the character variables with their labels of values? CHAR_VAR_NAME OPTION_VALUE LABEL SEX 1 Male SEX 2 Female DRUG 1 A DRUG 2 B DRUG 3 C

zihdonv19 · ‎02-29-2024

I expect to get this kind of table (as a separate SAS dataset rather than just display in the "results viewer"): VARIABLE NAME DESCRIPTION/LABEL TYPE/ FORMAT VALUE DESCRIPTION SEX Sex of study participant Char 1 = Male 2 = Female DATE Date of diagnosis Num YYMMDD10. ... ... ...

zihdonv19 · ‎02-07-2024

Example dataset: DATA OPPOSITN; INPUT ID O1-O7 T1-T7 SCOLMER SCOLPER; CARDS; 1000 2 0 1 0 0 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 10 15 1001 2 1 2 0 5 1 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 9 6 1003 4 2 0 0 0 0 1 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 14 12 1004 4 0 1 0 . . 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 12 13 1005 5 9 3 4 4 3 3 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 9 9 1007 8 10 2 0 1 2 2 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 7 16 1008 1 2 0 0 0 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 12 7 1009 1 0 1 0 0 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 16 20 1011 3 1 0 0 0 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 9 20 1012 4 2 2 3 6 . 6 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 9 12 1013 4 2 0 0 . 2 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 7 7 1014 5 7 10 4 4 6 2 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 11 12 1016 4 2 0 0 0 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 12 12 1017 7 4 3 6 3 4 5 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 6 5 1018 5 2 1 6 2 . . -0.6 -0.2 -0.1 0 0.1 0.2 0.3 14 11 1020 5 2 6 0 0 . . -0.6 -0.2 -0.1 0 0.1 0.2 0.3 9 10 1021 7 1 1 . 0 4 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 13 17 1022 7 3 2 1 1 0 1 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 12 14 1023 5 1 2 1 4 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 5 7 1024 5 4 4 0 . 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 13 21 1026 3 0 0 0 0 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 17 18 1027 6 5 9 3 2 2 1 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 12 8 1100 0 10 2 5 2 3 4 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 3 9 1101 5 2 0 6 0 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 10 11 1102 0 1 4 4 2 . 3 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 12 12 1103 2 . 3 0 1 . 1 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 11 11 1104 0 2 2 3 1 0 . -0.6 -0.2 -0.1 0 0.1 0.2 0.3 10 7 1106 2 3 6 3 1 0 4 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 14 12 1107 2 0 2 0 2 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 13 12 1110 0 2 2 6 5 0 . -0.6 -0.2 -0.1 0 0.1 0.2 0.3 14 14 1113 0 0 0 0 5 1 1 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 10 11 1115 0 6 6 5 2 . . -0.6 -0.2 -0.1 0 0.1 0.2 0.3 12 6 1116 1 2 6 3 3 2 . -0.6 -0.2 -0.1 0 0.1 0.2 0.3 11 8 1117 0 2 3 1 1 3 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 11 11 1119 0 0 0 6 2 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 9 6 1120 0 0 0 1 3 . 3 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 11 11 1121 0 4 7 6 3 6 5 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 9 6 1122 0 2 5 1 0 0 1 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 11 9 1123 2 8 8 7 8 8 8 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 10 8 1124 0 1 1 2 3 0 2 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 3 1 1125 0 9 5 2 7 0 . -0.6 -0.2 -0.1 0 0.1 0.2 0.3 3 11 1126 0 0 0 3 5 0 . -0.6 -0.2 -0.1 0 0.1 0.2 0.3 7 11 1127 0 . 2 7 2 2 1 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 12 10 1200 1 3 4 0 0 2 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 11 9 1201 0 0 1 0 0 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 12 16 1202 2 6 5 0 4 1 1 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 10 12 1204 0 0 3 0 0 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 7 12 1205 2 4 3 6 2 5 3 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 11 12 1206 0 0 2 0 1 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 9 12 1207 0 0 0 4 0 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 14 12 1208 4 0 1 0 0 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 11 10 1209 3 0 2 2 0 1 1 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 10 12 1210 1 1 6 5 0 1 8 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 6 6 1211 0 0 3 1 1 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 10 10 1212 0 5 5 1 2 0 1 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 8 11 1213 1 6 3 4 1 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 7 12 1214 0 0 4 3 . 3 2 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 7 7 1216 0 1 3 0 0 1 1 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 12 12 1217 2 6 7 2 1 1 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 12 11 1219 0 0 0 2 1 0 . -0.6 -0.2 -0.1 0 0.1 0.2 0.3 10 10 1221 1 2 3 0 . . . -0.6 -0.2 -0.1 0 0.1 0.2 0.3 8 3 1222 0 0 2 0 0 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 12 10 1223 0 1 0 0 0 0 . -0.6 -0.2 -0.1 0 0.1 0.2 0.3 11 12 1225 4 2 5 2 3 1 4 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 7 9 1226 1 5 0 0 . 5 2 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 8 10 1227 0 1 4 4 1 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 7 12 1300 9 8 5 8 7 4 . -0.6 -0.2 -0.1 0 0.1 0.2 0.3 5 12 1302 7 6 7 7 4 2 6 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 9 8 1400 0 1 0 0 0 1 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 15 15 1401 1 1 0 0 0 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 15 15 1402 0 0 0 0 0 0 1 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 12 15 1404 3 0 1 1 0 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 12 10 1405 2 0 0 0 0 0 . -0.6 -0.2 -0.1 0 0.1 0.2 0.3 9 10 1406 3 0 2 3 0 0 . -0.6 -0.2 -0.1 0 0.1 0.2 0.3 11 7 1407 1 2 3 1 0 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 13 6 1408 1 1 1 1 0 0 1 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 11 9 1409 2 1 2 0 0 0 1 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 8 10 1410 3 0 1 1 0 0 2 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 6 2 1500 0 5 3 1 2 0 2 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 11 10 1501 1 0 0 0 0 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 11 10 1502 2 0 1 0 1 0 5 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 17 15 1504 4 . . 1 2 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 12 11 1505 3 6 9 4 0 5 9 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 12 5 1506 1 1 . 0 1 1 3 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 11 14 1600 7 3 3 2 5 0 3 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 11 11 1601 2 . 4 0 0 1 3 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 12 10 1602 0 1 0 0 0 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 6 7 1604 0 0 0 0 0 1 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 14 19 1605 2 2 4 5 3 2 3 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 12 12 1606 5 6 2 4 1 8 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 14 12 1607 8 5 6 6 1 0 1 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 12 12 1700 1 2 2 5 1 0 3 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 11 11 1702 8 4 1 6 0 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 11 11 1704 10 1 2 3 . 1 . -0.6 -0.2 -0.1 0 0.1 0.2 0.3 10 12 1705 0 0 0 0 0 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 8 12 1706 0 3 0 0 0 1 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 12 12 1708 0 0 0 0 1 0 . -0.6 -0.2 -0.1 0 0.1 0.2 0.3 7 11 1709 0 0 0 0 0 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 10 9 1711 1 2 3 2 8 3 7 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 12 10 1714 0 0 0 0 0 0 . -0.6 -0.2 -0.1 0 0.1 0.2 0.3 12 7 1715 1 0 1 0 0 3 . -0.6 -0.2 -0.1 0 0.1 0.2 0.3 10 12 1716 1 2 5 0 0 0 . -0.6 -0.2 -0.1 0 0.1 0.2 0.3 13 15 1717 1 1 6 6 2 1 . -0.6 -0.2 -0.1 0 0.1 0.2 0.3 15 12 1719 2 1 . 1 3 2 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 10 11 1720 5 3 2 0 2 2 . -0.6 -0.2 -0.1 0 0.1 0.2 0.3 6 7 1723 1 8 2 4 . 3 8 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 3 7 1724 0 0 0 1 0 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 7 10 1725 5 1 0 0 0 0 2 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 14 18 1726 7 9 3 6 . . . -0.6 -0.2 -0.1 0 0.1 0.2 0.3 12 14 1727 1 . 3 2 0 0 1 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 5 7 1728 2 0 0 0 4 1 1 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 12 10 1729 3 4 1 0 0 . . -0.6 -0.2 -0.1 0 0.1 0.2 0.3 13 11 1731 4 3 1 0 0 2 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 11 17 1732 0 2 2 1 0 2 2 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 10 11 1733 3 0 2 2 3 6 4 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 5 9 1734 1 0 0 0 0 0 . -0.6 -0.2 -0.1 0 0.1 0.2 0.3 10 11 1735 3 1 3 3 3 0 . -0.6 -0.2 -0.1 0 0.1 0.2 0.3 9 9 1737 5 0 0 2 2 3 . -0.6 -0.2 -0.1 0 0.1 0.2 0.3 9 10 1739 2 3 0 0 5 1 . -0.6 -0.2 -0.1 0 0.1 0.2 0.3 9 9 2000 2 1 3 1 1 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 12 11 2001 1 3 1 0 . . 2 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 11 12 2003 1 0 0 0 0 0 1 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 14 11 2005 0 2 2 . 0 3 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 13 11 2006 0 0 0 0 . 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 11 8 2007 7 7 8 5 3 1 4 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 12 10 2008 1 0 1 0 0 4 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 7 8 2009 2 2 3 0 0 2 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 9 10 2010 2 0 9 1 3 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 12 8 2011 0 1 0 0 0 6 3 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 11 12 2012 2 1 0 1 0 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 11 16 2013 1 0 0 0 0 1 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 12 15 2014 0 1 5 1 0 0 3 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 9 7 2015 2 2 3 3 0 1 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 12 11 2016 0 0 . 0 1 0 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 13 11 2017 4 6 6 0 0 1 4 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 8 10 2018 4 2 7 6 . . . -0.6 -0.2 -0.1 0 0.1 0.2 0.3 13 10 2019 0 8 2 1 0 3 0 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 11 11 3000 3 1 5 1 6 . 1 -0.6 -0.2 -0.1 0 0.1 0.2 0.3 7 12 RUN; Code for trajectory model and plot: PROC TRAJ DATA=OPPOSITN OUTPLOT=OP OUTSTAT=OS OUT=OF OUTEST=OE ITDETAIL; ID ID; VAR O1-O7; INDEP T1-T7; MODEL CNORM; MAX 10; NGROUPS 3; ORDER 3 3 3; RUN; %TRAJPLOT(OP,OS,'Opposition vs. Scaled Age','Cnorm Model','Opposition','Scaled Age') Does anyone know how to show trajectory line one at a time in the plot (in the above example, I should get 3 plots, one line in each plot)? Thanks!

zihdonv19 · ‎02-05-2024

Thanks for your additional comments. but it gives error "ERROR: The ID value "YEAR0" occurs twice in the same BY group." I guess it's because for some person, they have multiple records in the same year?

zihdonv19 · ‎02-05-2024

Hi! Thank you for your reply. The reason that I want a wide format is that I need to use proc traj to do trajectory analysis which requires this kind of dataset. In my real data, I only have data for 5 years, so even using wide format my desired dataset won't look too messy..i guess

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