Hello, I am getting this error with my code which is trying to create a wide format of my variables. Similar issue have been resolved online but I could not figure out the problem. Thanks in advance. Here is the code and log: /* STEP 5.2: TRANSFORM CARRIER LINE FILE */ data bcarline2016_2021_wide (drop=i line_1st_expns_dt line_last_expns_dt hcpcs_cd line_icd_dgns_cd line_num line_nch_pmt_amt prf_physn_npi tax_num); format line_1st_expns_dt_1-line_1st_expns_dt_13 mmddyy10. line_last_expns_dt_1-line_last_expns_dt_13 mmddyy10. line_icd_dgns_cd1- line_icd_dgns_cd13 $5. hcpcs_cd1-hcpcs_cd13 $7. line_nch_pmt_amt1-line_nch_pmt_amt13 10.2 prf_physn_npi1-prf_physn_npi13 $12. tax_num1-tax_num13 $10.; set bcarline2016_2021; by bene_id clm_id line_num; retain line_1st_expns_dt_1-line_1st_expns_dt_13 line_last_expns_dt_1-line_last_expns_dt_13 line_icd_dgns_cd1-line_icd_dgns_cd13 hcpcs_cd1-hcpcs_cd13 line_nch_pmt_amt1-line_nch_pmt_amt13 prf_physn_npi1-prf_physn_npi13 tax_num1-tax_num13; array xline_icd_dgns_cd(13) line_icd_dgns_cd1-line_icd_dgns_cd13; array xline_1st_expns_dt_(13) line_1st_expns_dt_1-line_1st_expns_dt_13; array xline_last_expns_dt_(13) line_last_expns_dt_1-line_last_expns_dt_13; array xhcpcs_cd(13) hcpcs_cd1-hcpcs_cd13; array xline_nch_pmt_amt(13) line_nch_pmt_amt1-line_nch_pmt_amt13; array xprf_physn_npi(13) prf_physn_npi1-prf_physn_npi13; array xtax_num(13) tax_num1-tax_num13; if first.clm_id then do; do i=1 to 13; xline_icd_dgns_cd(line_num)=''; xline_1st_expns_dt_(line_num)=.; xline_last_expns_dt_(line_num)=.; xhcpcs_cd(line_num)=''; xline_nch_pmt_amt(line_num)=.; xprf_physn_npi(line_num)=''; xtax_num(line_num)=''; end; end; xline_icd_dgns_cd(line_num)=line_icd_dgns_cd; xline_1st_expns_dt_(line_num)=line_1st_expns_dt; xline_last_expns_dt_(line_num)=line_last_expns_dt; xhcpcs_cd(line_num)=hcpcs_cd; xline_nch_pmt_amt(line_num)=line_nch_pmt_amt; xprf_physn_npi(line_num)=prf_physn_npi; xtax_num(line_num)=tax_num; if last.clm_id then output; run; Log: 1 OPTIONS NONOTES NOSTIMER NOSOURCE NOSYNTAXCHECK; 72 73 /* STEP 5.2: TRANSFORM CARRIER LINE FILE */ 74 data bcarline2016_2021_wide (drop=i line_1st_expns_dt line_last_expns_dt hcpcs_cd line_icd_dgns_cd line_num 74 ! line_nch_pmt_amt prf_physn_npi tax_num); 75 format line_1st_expns_dt_1-line_1st_expns_dt_13 mmddyy10. 76 line_last_expns_dt_1-line_last_expns_dt_13 mmddyy10. 77 line_icd_dgns_cd1- line_icd_dgns_cd13 $5. 78 hcpcs_cd1-hcpcs_cd13 $7. 79 line_nch_pmt_amt1-line_nch_pmt_amt13 10.2 80 prf_physn_npi1-prf_physn_npi13 $12. 81 tax_num1-tax_num13 $10.; 82 set bcarline2016_2021; 83 by bene_id clm_id line_num; 84 retain line_1st_expns_dt_1-line_1st_expns_dt_13 85 line_last_expns_dt_1-line_last_expns_dt_13 86 line_icd_dgns_cd1-line_icd_dgns_cd13 87 hcpcs_cd1-hcpcs_cd13 88 line_nch_pmt_amt1-line_nch_pmt_amt13 89 prf_physn_npi1-prf_physn_npi13 90 tax_num1-tax_num13; 91 92 array xline_icd_dgns_cd(13) line_icd_dgns_cd1-line_icd_dgns_cd13; 93 arrayxline_1st_expns_dt_(13) line_1st_expns_dt_1-line_1st_expns_dt_13; 94 arrayxline_last_expns_dt_(13) line_last_expns_dt_1-line_last_expns_dt_13; 95 arrayxhcpcs_cd(13) hcpcs_cd1-hcpcs_cd13; 96 arrayxline_nch_pmt_amt(13) line_nch_pmt_amt1-line_nch_pmt_amt13; 97 arrayxprf_physn_npi(13) prf_physn_npi1-prf_physn_npi13; 98 arrayxtax_num(13) tax_num1-tax_num13; 99 100 if first.clm_id then do; 101 do i=1 to 13; 102 xline_icd_dgns_cd(line_num)=''; 103 xline_1st_expns_dt_(line_num)=.; 104 xline_last_expns_dt_(line_num)=.; 105 xhcpcs_cd(line_num)=''; 106 xline_nch_pmt_amt(line_num)=.; 107 xprf_physn_npi(line_num)=''; 108 xtax_num(line_num)=''; 109 end; 110 end; 111 112 xline_icd_dgns_cd(line_num)=line_icd_dgns_cd; 113 xline_1st_expns_dt_(line_num)=line_1st_expns_dt; 114 xline_last_expns_dt_(line_num)=line_last_expns_dt; 115 xhcpcs_cd(line_num)=hcpcs_cd; 116 xline_nch_pmt_amt(line_num)=line_nch_pmt_amt; 117 xprf_physn_npi(line_num)=prf_physn_npi; 118 xtax_num(line_num)=tax_num; 119 120 if last.clm_id then output; 121 run; ERROR: Array subscript out of range at line 112 column 2. line_1st_expns_dt_1=04/15/2021 line_1st_expns_dt_2=04/15/2021 line_1st_expns_dt_3=04/15/2021 line_1st_expns_dt_4=04/15/2021 line_1st_expns_dt_5=04/15/2021 line_1st_expns_dt_6=04/15/2021 line_1st_expns_dt_7=04/15/2021 line_1st_expns_dt_8=04/15/2021 line_1st_expns_dt_9=04/15/2021 line_1st_expns_dt_10=04/15/2021 line_1st_expns_dt_11=04/15/2021 line_1st_expns_dt_12=04/15/2021 line_1st_expns_dt_13=04/15/2021 line_last_expns_dt_1=04/15/2021 line_last_expns_dt_2=04/15/2021 line_last_expns_dt_3=04/15/2021 line_last_expns_dt_4=04/15/2021 line_last_expns_dt_5=04/15/2021 line_last_expns_dt_6=04/15/2021 line_last_expns_dt_7=04/15/2021 line_last_expns_dt_8=04/15/2021 line_last_expns_dt_9=04/15/2021 line_last_expns_dt_10=04/15/2021 line_last_expns_dt_11=04/15/2021 line_last_expns_dt_12=04/15/2021 line_last_expns_dt_13=04/15/2021 line_icd_dgns_cd1=E782 line_icd_dgns_cd2=E782 line_icd_dgns_cd3=E782 line_icd_dgns_cd4=E782 line_icd_dgns_cd5=E782 line_icd_dgns_cd6=E782 line_icd_dgns_cd7=E782 line_icd_dgns_cd8=E782 line_icd_dgns_cd9=E782 line_icd_dgns_cd10=E782 line_icd_dgns_cd11=E782 line_icd_dgns_cd12=E782 line_icd_dgns_cd13=E782 hcpcs_cd1=81161 hcpcs_cd2=81238 hcpcs_cd3=81411 hcpcs_cd4=81413 hcpcs_cd5=81414 hcpcs_cd6=81401 hcpcs_cd7=81404 hcpcs_cd8=81404 hcpcs_cd9=81404 hcpcs_cd10=81404 hcpcs_cd11=81404 hcpcs_cd12=81405 hcpcs_cd13=81405 line_nch_pmt_amt1=279.00 line_nch_pmt_amt2=600.00 line_nch_pmt_amt3=1350.19 line_nch_pmt_amt4=584.90 line_nch_pmt_amt5=584.90 line_nch_pmt_amt6=137.00 line_nch_pmt_amt7=274.83 line_nch_pmt_amt8=274.83 line_nch_pmt_amt9=274.83 line_nch_pmt_amt10=274.83 line_nch_pmt_amt11=274.83 line_nch_pmt_amt12=301.35 line_nch_pmt_amt13=301.35 prf_physn_npi1=1386023620 prf_physn_npi2=1386023620 prf_physn_npi3=1386023620 prf_physn_npi4=1386023620 prf_physn_npi5=1386023620 prf_physn_npi6=1386023620 prf_physn_npi7=1386023620 prf_physn_npi8=1386023620 prf_physn_npi9=1386023620 prf_physn_npi10=1386023620 prf_physn_npi11=1386023620 prf_physn_npi12=1386023620 prf_physn_npi13=1386023620 tax_num1=472935323 tax_num2=472935323 tax_num3=472935323 tax_num4=472935323 tax_num5=472935323 tax_num6=472935323 tax_num7=472935323 tax_num8=472935323 tax_num9=472935323 tax_num10=472935323 tax_num11=472935323 tax_num12=472935323 tax_num13=472935323 BENE_ID=eeeeee119111E11 CLM_ID=eeeeJ9jJE9reaEa LINE_NUM=14 CLM_THRU_DT=15APR2021 PRF_PHYSN_NPI=1386023620 TAX_NUM=472935323 LINE_1ST_EXPNS_DT=15APR2021 LINE_LAST_EXPNS_DT=15APR2021 HCPCS_CD=81406 LINE_NCH_PMT_AMT=282.88 LINE_ICD_DGNS_CD=E782 Year=2021 Case=Lines FIRST.BENE_ID=0 LAST.BENE_ID=0 FIRST.CLM_ID=0 LAST.CLM_ID=0 FIRST.LINE_NUM=1 LAST.LINE_NUM=1 i=. _ERROR_=1 _N_=5808 NOTE: The SAS System stopped processing this step because of errors. NOTE: There were 5809 observations read from the data set WORK.BCARLINE2016_2021. WARNING: The data set WORK.BCARLINE2016_2021_WIDE may be incomplete. When this step was stopped there were 3237 observations and 96 variables. WARNING: Data set WORK.BCARLINE2016_2021_WIDE was not replaced because this step was stopped. NOTE: DATA statement used (Total process time): real time 0.00 seconds cpu time 0.00 seconds 122 123 OPTIONS NONOTES NOSTIMER NOSOURCE NOSYNTAXCHECK; 135    ... View more

Hi, I have been searching for a solution to this for a while and my googling hasn't resulted in anything tangible. I hope someone here can help. I have 2 datasets (all data is made up but the gist should be there):   1. Transaction_Tbl - the source dataset that contains transactional data of call reps for selected dates and call durations: Transaction_Tbl           ID_NUM EMP_NM DATE CALL_START CALL_END CALL_DUR 000121 Mark 6/4/2023 4:30 PM 4:32 PM 2 000121 Mark 8/15/2023 8:30 AM 8:31 AM 1 000121 Mark 9/6/2023 9:15 AM 9:18 AM 3 000121 Mark 5/6/2024 10:05 AM 10:06 AM 1 000121 Mark 6/10/2024 2:22 PM 2:23 PM 1 000122 Jessie 4/29/2023 8:45 AM 8:55 AM 10 000122 Jessie 11/29/2023 11:15 AM 11:30 AM 15 000122 Jessie 12/8/2023 1:23 PM 1:40 PM 17 000122 Jessie 2/16/2024 1:40 PM 1:55 PM 15 000123 James 2/8/2023 12:10 PM 12:20 PM 10 000123 James 2/22/2023 2:33 PM 2:55 PM 22 000123 James 1/21/2024 4:12 PM 4:19 PM 7 000124 Felicia 5/23/2024 3:42 PM 3:55 PM 13 000127 Henry 1/2/2023 9:02 AM 9:10 AM 8 000127 Henry 3/18/2023 9:50 AM 10:00 AM 10 000127 Henry 4/21/2023 10:05 AM 10:20 AM 15 000127 Henry 10/26/2023 1:22 PM 1:27 PM 5 000127 Henry 11/12/2023 2:00 PM 2:04 PM 4   2. Card_Tbl - The supplemental dataset that shows what "card" the rep held between a certain date range. The card changes annually based on performance and defines the expected call duration at the card level:   Card_Tbl           ID_NUM EMP_NM EFF_DT END_DT CARD CARD_DESC 000121 Mark 1/1/2020 12/31/2020 GREEN Complete calls within 15 minutes. 000121 Mark 1/1/2021 12/31/2021 BLUE Complete calls within 5 minutes. 000121 Mark 1/1/2022 12/31/2022 ORANGE Complete calls within 8 minutes. 000121 Mark 1/1/2023 12/31/2023 GREEN Complete calls within 15 minutes. 000121 Mark 1/1/2024 12/31/2024 BLUE Complete calls within 5 minutes. 000122 Jessie 1/1/2022 12/31/2022 GREEN Complete calls within 15 minutes. 000122 Jessie 1/1/2023 12/31/2023 GREEN Complete calls within 15 minutes. 000122 Jessie 1/1/2024 12/31/2024 GREEN Complete calls within 15 minutes. 000123 James 7/1/2022 6/30/2023 GREEN Complete calls within 15 minutes. 000123 James 7/1/2023 6/30/2024 ORANGE Complete calls within 8 minutes. 000123 James 7/1/2024 6/30/2025 YELLOW Complete calls within 10 minutes. 000124 Felicia 3/1/2024 2/28/2025 GREEN Complete calls within 15 minutes. 000127 Henry 1/1/2023 12/31/2023 GREEN Complete calls within 15 minutes. 000127 Henry 1/1/2024 12/31/2024 BLUE Complete calls within 5 minutes.   I am currently reviewing the rep call duration manually by dumping the data to excel and adding the relevant card color from Card_tbl and determining a pass or fail if the call duration meets expectation.   The table i'd like to get by using SAS instead of manual work in excel is this (and complete a full population review vs a manual one): ID_NUM, EMP_NM, DATE, and CALL_DUR from Transaction_Tbl CARD from Card_Tbl based on the date RESULT is new to the want_tbl and will show pass/fail depending on CALL_DUR and CARD value Want_tbl           ID_NUM EMP_NM DATE CALL_DUR CARD RESULT 000121 Mark 6/4/2023 2 GREEN PASS 000121 Mark 8/15/2023 1 GREEN PASS 000121 Mark 9/6/2023 3 GREEN PASS 000121 Mark 5/6/2024 1 BLUE PASS 000121 Mark 6/10/2024 1 BLUE PASS 000122 Jessie 4/29/2023 10 GREEN PASS 000122 Jessie 11/29/2023 15 GREEN PASS 000122 Jessie 12/8/2023 17 GREEN FAIL 000122 Jessie 2/16/2024 15 GREEN PASS 000123 James 2/8/2023 10 GREEN PASS 000123 James 2/22/2023 22 GREEN FAIL 000123 James 1/21/2024 7 ORANGE PASS 000124 Felicia 5/23/2024 13 GREEN PASS 000127 Henry 1/2/2023 8 GREEN PASS 000127 Henry 3/18/2023 10 GREEN PASS 000127 Henry 4/21/2023 15 GREEN PASS 000127 Henry 10/26/2023 5 GREEN PASS 000127 Henry 11/12/2023 4 GREEN PASS   I cannot figure out how to grab the applicable CARD value from the Card_Tbl as it applies to the ID_NUM/date in the Transaction_Tbl.   Thank You for your time and expertise. ... View more

i am just now finishing the course ( Data Literacy Essentials   ) but it you dose not send to me any badge to my email why ? ... View more

Hello SAs users Im quite adept with PC SAS, but i am now trying to install SAS Viya and teh documentation looks like a nightmare  Can anyone help talk me through this - i got the software order form with the details and was expecting to find a simple install button !  any CLEAR guidance on this please    thanks  ifty ... View more

For the life of me, I can't figure out how to perform pairwise comparisons in SAS following a second-order Rao-Scott chi-square test of independence. I have two categorical variables (NSEX & DEPLVL). NSEX has two levels (1 = male; 2 = female) and DEPLVL has four levels (0 = No symptoms; 1 = 1 symptom; 2 = 2-4 symptoms; 3 = 5-9 symptoms). This is complex survey data with clustering, stratification, and weighting, so I'm limited to procedures which can incorporate these elements/variables: varmethod=taylor, weight audweight, strata varstrat & cluster varunit.   I want to know if the percentage of women with: no symptoms is significantly different from the percentage of women with 1 symptom no symptoms is significantly different from the percentage of women with 2-4 symptoms no symptoms is significantly different from the percentage of women with 5-9 symptoms and 2-4 symptoms is significantly different from the percentage of women with 5-9 symptoms I also want to know if there are significant differences among men for the same set of DEPLVL comparisons. I've attached the results of the overall test of independence in the hope that it clarifies my research questions/the answers I'm trying to get at (e.g., for scenario 1 above, I want to know if 45.5227 is significantly different from 46.3432).   I'd like to use either the Rao-Scott adjusted Wald test or adjusted F-test for the pairwise comparison (for methodological consistency) with the Holm-Bonferroni multiple comparison adjustment method, but at this point, I'll take anything that gives me adjusted p-values for each of the pairs. I'm trying to avoid the regression route if possible, but I'm just about ready to accept that if it's the only way to get it done.   I should note that I'm brand new to SAS and have been trying to use an AI engine to generate code for the pairwise comparisons, so this is definitely not my area of expertise. Nor is statistics to be honest - I'm writing a dissertation for clinical psych.   Can anyone lend a hand and help me figure this out? I've been trying to figure it out for 3 weeks now and don't feel like I've made any real progress on what (I would have thought) was a pretty straightforward question/procedure. ... View more