About carmong

carmong · ‎03-20-2024

I am attempting to create a table with only the row percentages. So far I have written code that has removed the column numbers and column percent's which has given me the table below: proc freq data=pairs; table admissiondate*def1 /NOCOL NOpercent out=freqmonth&today. outpct; format admissiondate MONYY.; run; admissiondate def1 Associated Associated Total illness non illness 20-Jan 1 0 1 100 0 20-Feb 5 1 6 83.33 16.67 20-Mar 780 63 843 92.53 7.47 20-Apr 2770 276 3046 90.94 9.06 20-May 2128 380 2508 How can I write a code which will provide me a table with only the row percent's like the one below? admissiondate def1 Associated Associated illness non illness 20-Jan 100 0 20-Feb 83.33 16.67 20-Mar 92.53 7.47 20-Apr 90.94 9.06 20-May 84.85 15.15

carmong · ‎05-23-2023

The previous datasets will only need to be combined into a master file once, what my plan is, is to save this as a SAS dataset so that I don't have the issues that exist with excel. Then each week new data will be added to the master file (but this data will be created wholly within SAS).

carmong · ‎05-23-2023

The source is excel, here is the code my former colleague wrote to import them in: /*import data from excel that the team has reviewed to SAS dataset*/ %MACRO CheckExcel(ex=, tab=); PROC IMPORT OUT=WORK.&tab /*DATAFILE= "\\hospital patient data\Hosp_Death_Report\ team notes\&ex..xlsx"*/ DATAFILE= "\\hospital patient data\Hosp_Match\QA\Data\ team notes\&ex..xlsx" DBMS=EXCEL REPLACE; RANGE="&tab$"; GETNAMES=YES; MIXED=NO; SCANTEXT=YES; USEDATE=YES; SCANTIME=YES; RUN; %MEND CheckExcel; %CheckExcel(ex=QA_ICU_Intub_X2_09SEP2021, tab=QA_IcuInt_09SEP2021); %CheckExcel(ex=QA_ICU_Intub_X3_15SEP2021, tab=QA_IcuInt_15SEP2021); %CheckExcel(ex=QA_ICU_Intub_Xx1_22SEP2021,tab=QA__IcuInt_Other_22SEP2021);

carmong · ‎05-23-2023

I am attempting to refine code written by a former colleague. I am attempting to combine imported datasets into one master dataset (previous data from previous data runs are needed for present and future data runs and I would like to create a large master file instead of my colleague having to import in multiple files from previous data runs). The issue is there are lots of empty cells and when this happens it seems that SAS will read them in as characters, but with the cells with data SAS reads them in as numbers, which is causing the following error messages. Is there a way to change the format in SAS I use SAS 9.4), or will I have to combine the data in excel (there are over 50 spreadsheets with 3 tabs each) Here is the code below: 1261 data allmiss_hosp_info; 1262 set 1263 1264 QA_Miss_Hosp_Info_10NOV2021 1265 QA_Miss_Hosp_Info_17NOV2021 1266 QA_Miss_Hosp_Info_23NOV2021 1267 QA_Miss_Hosp_Info_01DEC2021 1268 QA_Miss_Hosp_Info_08DEC2021 1269 QA_Miss_Hosp_Info_15DEC2021 1270 QA_Miss_Hosp_Info_Xx13 1271 QA_Miss_Hosp_Info_Xx14 1272 QA_Miss_Hosp_Info_Xx15 1273 QA_Miss_Hosp_Info_Xx16 1274 QA_Miss_Hosp_Info_Xx17 1275 QA_Miss_Hosp_Info_Xx18 1276 QA_Miss_Hosp_Info_Xx19 1277 QA_Miss_Hosp_Info_Xx20 1278 QA_Miss_Hosp_Info_Xx21 1279 QA_Miss_Hosp_Info_Xx22 1280 QA_Miss_Hosp_Info_Xx23 1281 QA_Miss_Hosp_Info_Xx24 1282 QA_Miss_Hosp_Info_Xx25 1283 QA_Miss_Hosp_Info_Xx26 1284 QA_Miss_Hosp_Info_Xx27 1285 QA_Miss_Hosp_Info_Xx28 1286 QA_Miss_Hosp_Info_Xx29 1287 QA_Miss_Hosp_Info_Xx30 1288 QA_Miss_Hosp_Info_Xx31 1289 QA_Miss_Hosp_Info_Xx32 1290 QA_Miss_Hosp_Info_Xx33 1291 QA_Miss_Hosp_Info_Xx34 1292 QA_Miss_Hosp_Info_Xx35 1293 QA_Miss_Hosp_Info_Xx36 1294 QA_Miss_Hosp_Info_Xx37 1295 QA_Miss_Hosp_Info_Xx38 1296 QA_Miss_Hosp_Info_Xx39 1297 QA_Miss_Hosp_Info_Xx40 1298 QA_Miss_Hosp_Info_Xx41 1299 QA_Miss_Hosp_Info_Xx42 1300 QA_Miss_Hosp_Info_Xx43 1301 QA_Miss_Hosp_Info_Xx44 1302 QA_Miss_Hosp_Info_Xx45 1303 QA_Miss_Hosp_Info_Xx46 1304 QA_Miss_Hosp_Info_Xx47 1305 QA_Miss_Hosp_Info_Xx48 1306 QA_Miss_Hosp_Info_Xx49 1307 QA_Miss_Hosp_Info_Xx50 1308 QA_Miss_Hosp_Info_Xx51 1309 QA_Miss_Hosp_Info_Xx52 1310 QA_Miss_Hosp_Info_Xx53 1311 QA_Miss_Hosp_Info_Xx54 1312 QA_Miss_Hosp_Info_Xx55 1313 QA_Miss_Hosp_Info_Xx56 1314 QA_Miss_Hosp_Info_Xx57 1315 QA_Miss_Hosp_Info_Xx58 1316 QA_Miss_Hosp_Info_Xx59 1317 QA_Miss_Hosp_Info_Xx60 1318 QA_Miss_Hosp_Info_Xx61 1319 QA_Miss_Hosp_Info_Xx62 1320 QA_Miss_Hosp_Info_Xx63 1321 QA_Miss_Hosp_Info_Xx64 1322 QA_Miss_Hosp_Info_Xx65 1323 QA_Miss_Hosp_Info_Xx66 1324 QA_Miss_Hosp_Info_Xx67 1325 QA_Miss_Hosp_Info_Xx68 1326 QA_Miss_Hosp_Info_Xx69 1327 QA_Miss_Hosp_Info_Xx70 1328 QA_Miss_Hosp_Info_Xx71 1329 QA_Miss_Hosp_Info_Xx72 1330 QA_Miss_Hosp_Info_Xx73 1331 QA_Miss_Hosp_Info_Xx74 1332 QA_Miss_Hosp_Info_Xx75 1333 QA_Miss_Hosp_Info_Xx76 1334 QA_Miss_Hosp_Info_Xx77 1335 QA_Miss_Hosp_Info_Xx78 1336 QA_Miss_Hosp_Info_Xx79 1337 QA_Miss_Hosp_Info_Xx80 1338 QA_Miss_Hosp_Info_Xx81; ERROR: Variable IntubationDate has been defined as both character and numeric. ERROR: Variable ExtubationDate has been defined as both character and numeric. ERROR: Variable HospDtlICUAdmitDt has been defined as both character and numeric. ERROR: Variable HospDtlICUDischDt has been defined as both character and numeric. ERROR: Variable HospDtlIntub1StartDt has been defined as both character and numeric. ERROR: Variable HospDtlIntub1EndDt has been defined as both character and numeric. ERROR: Variable repeat has been defined as both character and numeric. ERROR: Variable repeat has been defined as both character and numeric. ERROR: Variable NoAdmitDate has been defined as both character and numeric.

carmong · ‎01-19-2023

For some reason the data was truncated when changing the admissions from character to numeric (can't explain why), but when I changed the character to numeric before running the rest of the code, the sas data was not affected in any way and the code gave me all of my data 🙂

carmong · ‎01-19-2023

The spreadsheet is uploaded by another department so they may have changed the format. What I will have to do is save it to my own folder instead of importing directly from their folder

carmong · ‎01-19-2023

carmong · ‎01-19-2023

the code works but my output is truncated. I am using an excel sheet that has 1,020 rows with dates from March 29th 2020 going to January 13th 2023 but the summary data is only outputting up until 3/12/2022. What might be causing that to happen?

carmong · ‎01-18-2023

Posted below is an example of two weeks of data Today's Date Hospital admissions 5/31/2020 383 6/1/2020 369 6/2/2020 430 6/3/2020 441 6/4/2020 437 6/5/2020 411 6/6/2020 372 6/7/2020 373 6/8/2020 424 6/9/2020 426 6/10/2020 397 6/11/2020 413 6/12/2020 429 6/13/2020 381

carmong · ‎01-18-2023

The picture was used as an example of how my data is set up, but that is not my data. My data has dates for seven days of the week along with number of admissions per day.

carmong · ‎01-17-2023

This is a two part question. Part 1: Is there a way to write code that will sum every 7 rows for a variable. I wrote code for this in excel (=SUM(OFFSET($D$2,(ROW()-ROW($D$2))*7,0,7,1)) but I will now have to do this for a much larger dataset. Is there a way to write code in sas that will do the same? Part 2: The code posted below outputs data in a "by month" format. How can this be done to output data in a "per week" format (meaning the data is displayed with the last date of a 7 day period) /* monthly analysis */ proc freq data= admit_adults; table def*admissiondate / out=freqmonth&today. outpct; format admissiondate MONYY.; run;

carmong · ‎10-06-2022

Here are the other two tables: age Least Squares Means age Estimate Standard Error z Value Pr > |z| Alpha Lower Upper Exponentiated Exponentiated Lower Exponentiated Upper 30-49 -6.3712 0.2085 -30.56 <.0001 0.05 -6.7799 -5.9626 0.001710 0.001136 0.002573 50-64 -4.9659 0.1644 -30.21 <.0001 0.05 -5.2881 -4.6436 0.006972 0.005051 0.009623 65-79 -3.2641 0.1140 -28.64 <.0001 0.05 -3.4874 -3.0407 0.03823 0.03058 0.04780 80+ -2.1911 0.1170 -18.72 <.0001 0.05 -2.4205 -1.9617 0.1118 0.08888 0.1406 18-29 -8.1435 0.5774 -14.10 <.0001 0.05 -9.2751 -7.0119 0.000291 0.000094 0.000901 Differences of age Least Squares Means age _age Estimate Standard Error z Value Pr > |z| Alpha Lower Upper Exponentiated Exponentiated Lower Exponentiated Upper 30-49 18-29 1.7723 0.6138 2.89 0.0039 0.05 0.5692 2.9754 5.8842 1.7668 19.5975 50-64 18-29 3.1777 0.6003 5.29 <.0001 0.05 2.0011 4.3542 23.9904 7.3971 77.8061 65-79 18-29 4.8794 0.5885 8.29 <.0001 0.05 3.7260 6.0329 131.56 41.5138 416.91 80+ 18-29 5.9524 0.5891 10.10 <.0001 0.05 4.7978 7.1070 384.68 121.24 1220.48

carmong · ‎10-06-2022

I tried running it again and got a different result. Perhaps its the spacing with the data section that made a difference? I noticed that there was spacing in your code, for the datalines, but when I typed mine in, I didn't space. Analysis Of Maximum Likelihood Parameter Estimates Parameter DF Estimate Standard Error Wald 95% Confidence Limits Wald Chi-Square Pr > ChiSq Intercept 1 -8.1435 0.5774 -9.2751 -7.0119 198.95 <.0001 age 30-49 1 1.7723 0.6138 0.5692 2.9754 8.34 0.0039 age 50-64 1 3.1777 0.6003 2.0011 4.3542 28.02 <.0001 age 65-79 1 4.8794 0.5885 3.7260 6.0329 68.75 <.0001 age 80+ 1 5.9524 0.5891 4.7978 7.1070 102.10 <.0001 age 18-29 0 0.0000 0.0000 0.0000 0.0000 . . Scale 0 1.0000 0.0000 1.0000 1.0000

carmong · ‎10-06-2022

Here is what I typed into SAS, where did I go wrong? data a; input age $ Hosp Nhosp; off=log (Nhosp); datalines; 18-29 3 10323 0.000291 0.03 30-49 23 13450 0.00171 0.2 5.9 50-64 37 5307 0.006972 0.7 24.0 65-79 77 2014 0.038232 3.8 131.6 80+ 73 653 0.111792 11.2 384.7 ; proc genmod; class age(ref="18-29"); model Nhosp=age / dist=poisson offset=off; lsmeans age / diff=control("18-29") exp cl plots=none; run; Here is the log: 1 data a; 2 input age $ Hosp Nhosp; 3 off=log (Nhosp); 4 datalines; NOTE: SAS went to a new line when INPUT statement reached past the end of a line. NOTE: The data set WORK.A has 5 observations and 4 variables. NOTE: DATA statement used (Total process time): real time 0.47 seconds cpu time 0.04 seconds 11 ; NOTE: Writing HTML Body file: sashtml.htm 12 proc genmod; 13 class age(ref="18-29"); 14 model Nhosp=age / dist=poisson offset=off; 15 lsmeans age / diff=control("18-29") exp cl plots=none; 16 run; NOTE: Fitting saturated model. Scale will not be estimated. NOTE: Algorithm converged. NOTE: The scale parameter was held fixed. NOTE: The structure of the LSMeans table has changed from an earlier release of SAS. NOTE: The structure of the Diffs table has changed from an earlier release of SAS. NOTE: PROCEDURE GENMOD used (Total process time): real time 2.31 seconds cpu time 0.29 seconds

carmong · ‎10-05-2022

Here is what I got when I tried the code: Is there supposed to be a "mean" in the age Least Squares Means table? Analysis Of Maximum Likelihood Parameter Estimates Parameter DF Estimate Standard Error Wald 95% Confidence Limits Wald Chi-Square Pr > ChiSq Intercept 1 0.0000 0.0098 -0.0193 0.0193 0.00 1.0000 age 30-49 1 0.0000 0.0131 -0.0256 0.0256 0.00 1.0000 age 50-64 1 0.0000 0.0169 -0.0331 0.0331 0.00 1.0000 age 65-79 1 0.0000 0.0244 -0.0477 0.0477 0.00 1.0000 age 80+ 1 0.0000 0.0404 -0.0791 0.0791 0.00 1.0000 age 18-29 0 0.0000 0.0000 0.0000 0.0000 . . Scale 0 1.0000 0.0000 1.0000 1.0000 age Least Squares Means age Estimate Standard Error z Value Pr > |z| Alpha Lower Upper Exponentiated Exponentiated Lower Exponentiated Upper 30-49 0 0.008623 0.00 1.0000 0.05 -0.01690 0.01690 1.0000 0.9832 1.0170 50-64 0 0.01373 0.00 1.0000 0.05 -0.02690 0.02690 1.0000 0.9735 1.0273 65-79 0 0.02228 0.00 1.0000 0.05 -0.04367 0.04367 1.0000 0.9573 1.0446 80+ 0 0.03913 0.00 1.0000 0.05 -0.07670 0.07670 1.0000 0.9262 1.0797 18-29 0 0.009842 0.00 1.0000 0.05 -0.01929 0.01929 1.0000 0.9809 1.0195 Differences of age Least Squares Means age _age Estimate Standard Error z Value Pr > |z| Alpha Lower Upper Exponentiated Exponentiated Lower Exponentiated Upper 30-49 18-29 0 0.01309 0.00 1.0000 0.05 -0.02565 0.02565 1.0000 0.9747 1.0260 50-64 18-29 0 0.01689 0.00 1.0000 0.05 -0.03311 0.03311 1.0000 0.9674 1.0337 65-79 18-29 0 0.02436 0.00 1.0000 0.05 -0.04774 0.04774 1.0000 0.9534 1.0489 80+ 18-29 0 0.04035 0.00 1.0000 0.05 -0.07909 0.07909 1.0000 0.9240 1.0823

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