About rsanchez87

rsanchez87 · ‎06-14-2019

Hi, I am looking to understanding the change pre-post for a single group as well. Here is my data: SRC COUNT AGE0_10 MEMCOUNT PERCENT_SRC POST 18794 0 17166 91.3377 PRE 6956 0 6832 98.2174 POST 18794 1 1628 8.6623 PRE 6956 1 124 1.7826 These are dependent samples. The category being evaluated is AGE0_10 (age between 0 and 10) in the pre-period and post-period. As you can see there is a 7% increase from pre-perod (2%) to the post-period (8%) This is the code i wrote: ODS HTML CLOSE; ODS HTML; PROC FREQ DATA = AGE0_10_SUM ORDER = DATA; WEIGHT MEMCOUNT; TABLES AGE0_10*SRC / AGREE; TITLE "AGE0_10"; RUN; ;QUIT; Which is giving me a p-vale <0.0001 ---> I believe this is wrong. The change may be significant, but not at this magnitude. Can you advise how to set up the summary table, I have subject level details. Thank you.

rsanchez87 · ‎01-31-2019

Is the McNemar test appropriate?

rsanchez87 · ‎01-31-2019

Thanks in advance! I have a pre-post analysis Of cases and controls. The outcome variables of interest are emergency room visits and hospitalization visits - both rare occurrences with median = 0 in both periods. I want to evaluate if the change in pre-post is different between cohorts. Each cohort has 48 months of enrollment pre-intervention and 36 months post-intervention. There are 500 cases and 17,000 controls. Cases and controls are demographically not different and are paired with themselves pre and post periods. Several issues arise: 1. This is non-normal count data. So I summed the number of visits per period and ran a Wilcoxon sum rank test to assess the pre-post difference between groups; a wilcoxon sign test to assess within group differences. I did not control for the unbalanced pre and post months. Is this okay? I’m getting heat for having unbalanced periods. So I created a per-member-per-month rate for each subject, pre-post. The data is still nonparametric but scaled over the number of months. What test is best suited for this - sum and sign rank? Folks want to see the percent difference in utilization. So I calculated a percent of user for each period. This is rolls up to th group level. So there are two observations per cohort for users:pre-users and post-user. I tried sum and sign tests, but there aren’t enough records. I do capture the denominator here and the number of users and percent. What is the best approach to assess if the change in the percent of users before and after the time period is different between cohorts? Suggestions and ideas welcomed! Thanks!

rsanchez87 · ‎01-26-2019

Hi, Here are two methods: METHOD 1: Using a where clause in both directions combined by a union all. Method 1: PROC SQL; CREATE TABLE WANT AS SELECT UNIQUE NEW_ID FROM HAVE_1 WHERE NEW_ID NOT IN (SELECT UNIQUE NEW_ID FROM HAVE_2) UNION ALL SELECT UNIQUE NEW_ID FROM HAVE_2 WHERE NEW_ID NOT IN (SELECT UNIQUE NEW_ID FROM HAVE_2) ;QUIT; Method 2: Leveraging subqueries to create a union all NEW_ID's. Both tables have only 1 column, so it isn't ambiguous. The outermost query deduplicates. METHOD 2: PROC SQL; CREATE TABLE WANT AS SELECT UNIQUE * FROM (SELECT * FROM (SELECT UNIQUE NEW_ID FROM HAVE_1) UNION ALL SELECT * FROM (SELECT UNIQUE NEW_ID FROM HAVE_1) ) ;QUIT;

rsanchez87 · ‎09-26-2018

Hi there, I had this same issue with a public data set. The instructions to the data set labeled the values of -996 as a V, for a valid skip. When you try to filter for -996 or V, it doesn't work. You have to harness the VVALUEX formatting of the column. Below is my solution. I am changing the format for 'valid skips', missing values, 'errors', 'refused', and 'don't know'. 1. I first create a table that has all of the column variable names, their order and their data type. 2. I determine the max size of the table. 3. I throw that table reference table into a macro and sequence them to the maximum record value. 4. The macro cycles through each column name and data type associated with the reference table Ith value 5. Logic is applied. 6. Lastly, a proc freq of the new table outputs the distribution of new codes. I recommend taking a before and after snapchat to validate the code is correct. *** OUTPUT ALL TABLE CONTENTS TO A NEW TABLE ***; PROC CONTENTS DATA= OUTFILE.AGES5_17 OUT= COLUMN_NAMES NOPRINT; RUN; *** FORMAT THE COLUMN NAMES TO BE INCLUDED IN THE MACRO FOR VARIABLE AND DATA TYPE COLUMNS ***; PROC SQL; CREATE TABLE COLUMN_NAMES2 AS SELECT VARNUM AS POSITION, NAME ,CATS("'",NAME,"'") AS VARIABLE ,TYPE ,CASE WHEN TYPE EQ 1 THEN "'NUMERIC'" ELSE "'CHARACTER'" END AS VARIABLE_TYPE FORMAT = $15. FROM COLUMN_NAMES ORDER BY VARNUM ;QUIT; *** DETERMINE THE MAXIMUM NUMBER OF RECORDS IN THE TABLE ***; PROC SQL NOPRINT; SELECT MAX INTO: MAX FROM (SELECT MAX(POSITION) AS MAX FROM COLUMN_NAMES2) ;QUIT; %PUT MAX NUMBER OF COLUMNS === &MAX.; *** TEST FILE ***; DATA TEST ; SET OUTFILE.AGES5_17 (OBS = 1000); RUN; *** THESE OPTIONS MUTES THE SAS LOG ***; OPTIONS NOPRINTMSGLIST NOMPRINT NONOTES NOSOURCE NOSOURCE2 ERROR = 0; *** THIS MACRO ASSIGNS THE TARGET FILE, ITERATES THROUGH EACH REFERENCE VARIABLE AND DATA TYPE UNTIL THE MAX RECORD IS REACH AND APPLIES THE LOGIC TO THE VVALUEX FORMAT OF THE COLUMN ***; %MACRO VARS(FILE); %DO I = 1 %TO &MAX.; PROC SQL NOPRINT; SELECT NAME INTO: VARLIST_&I. FROM COLUMN_NAMES2 WHERE POSITION EQ &I. ;QUIT; PROC SQL NOPRINT; SELECT VARIABLE_TYPE INTO: TYPELIST_&I. FROM COLUMN_NAMES2 WHERE POSITION EQ &I. ;QUIT; %PUT VARIABLE NAME === &&&VARLIST_&I. ||| VARIABLE TYPE === &&&TYPELIST_&I.; DATA TEST /*(KEEP = &&&VARLIST_&I.)*/; SET TEST; IF COMPRESS(VVALUE(&&&VARLIST_&I.)) = 'V' THEN DO; &&&VARLIST_&I. = 996; OUTPUT; END; ELSE IF COMPRESS(VVALUE(&&&VARLIST_&I.)) = 'R' OR COMPRESS(VVALUE(&&&VARLIST_&I.)) = 'D' OR COMPRESS(VVALUE(&&&VARLIST_&I.)) = . THEN DO; &&&VARLIST_&I. = 999; OUTPUT; END; ELSE IF COMPRESS(VVALUE(&&&VARLIST_&I.)) = 'E' THEN DO; &&&VARLIST_&I. = 994; OUTPUT; END; ELSE DO; &&&VARLIST_&I. = &&&VARLIST_&I.; OUTPUT; END; %END; %MEND VARS; %VARS(FILE = OUTFILE.AGES5_17); PROC FREQ DATA = TEST; TABLES MARITAL BARCODE_FAHITEMS / MISSING; RUN; HOPE THIS HELPS!!!

rsanchez87 · ‎09-07-2018

THANK YOU SO MUCH! I was exploring those option, but I confused myself. Going to start fresh with that plan of action. Unfortunately, I don't have SAS/ETS, so GLIMMIX it is. I will follow your guidance and report out. I am looking at the data in two ways: (1) population level, and (2) individual level. At the population level, there are 84 records, one for each month of the study with columns indicating when the intervention started, utilization for the intervention and control, the difference in utilization rates between intervention and control, and a counter for the time after the intervention. Our outcome variable here is the difference in utilization rates. The rates are nicely distributed, with a normal distribution - but they still arise from count data. However, the difference is always negative - the intervention rates are always less than the control rates. Is Poisson, NegBin, and/or ZBIN still appropriate for negative values? At the population level, the GLIMMIX is as follows: PROC GLIMMIX DATA = OUTFILE._MEDS1_&SRC. PLOTS = RESIDUALPANEL ; MODEL RATE_DIFF = TIME POST TIME_AFTER POST*TIME_AFTER / SOLUTION CHISQ CORRB ; OUTPUT OUT = _GLIMMIX_POPULATION_&SRC. PRED = PREDICTED RESID = RESIDUALS; ODS OUTPUT PARAMETERESTIMATES = _MEDS0_&SRC._GLIMMIX; RANDOM _RESIDUAL_ / TYPE = ARMA(1,1); TITLE "PROC GLIMMIX - REGRESSION FOR: &SRC."; RUN; At the member level, we add the same variables as above, except we include the member identifier. The outcome variable here is the count of utilization. Code: PROC GLIMMIX DATA = OUTFILE.RATES_MEDS00_&SRC. PLOTS = RESIDUALPANEL; CLASS MEMBNO; MODEL ENCOUNTERS = TIME POST TIME_AFTER / SOLUTION CHISQ CORRB; OUTPUT OUT = _GLIMMIX_SUBJECT_&SRC. PRED = REGPRED; ODS OUTPUT PARAMETERESTIMATES = RATES_MEDS00_&SRC._GLIMMIX; RANDOM _RESIDUAL_ / SUBJECT = MEMBNO TYPE = ARMA(1,1); TITLE "PROC GLIMMIX - MEMBER LEVEL REGRESSION UNADJUSTED FOR: &SRC."; RUN; So, to your question: yes, the utilization accounts for the same people over time. Am I appropriately account for this with the "SUBJECT = MEMBNO" statement. MEMBNO is the individual identifier. Ideally, I need the analysis at the individual level so I can adjust for additional variables. Let me know what you think, I'll be glad to fill in any holes and or share data. Thank you.

rsanchez87 · ‎09-06-2018

Apologies, please see below. Poisson or negative binomial? How would you proceed? Some thoughts: 1. The data is first order auto-regressive (i.e. what happens last month, influences this month). I rolled up the data to the year-quarter, and it remains auto-regressive. Not surprised. There is no way around it, as this is real-world healthcare data. Still, I need Proc Autoreg - but can I transform the data or account for the autocorrelation somehow in an estimate, akin to a two-step model?. 😞 2. Most members have utilization of 0 at each month, would it make sense to roll the members to the year-quarter or simply the year mark to reduce zeroes? Thanks for your help!

rsanchez87 · ‎09-05-2018

Thanks again. I am using health care utilization data, which is typically composed of a scattered high utilizers and low utilizers and many zeroes. This is count data per month per person for 84 months (N = 6,017 person). How would you assess the distribution here? Raw data:

rsanchez87 · ‎08-31-2018

Thanks again for the clarification. I had to update the methodology to count visits in our process, thus changing the values of our outcome variable. Running the program again, I get an Ar(1) of 03354 (0.1134). This would mean that there is auto-correlation? I am using PROC GLIMMIX to run the stats - that is the only available procedure I have access too. Code below: PROC GLIMMIX DATA = OUTFILE._MEDS0_&SRC. PLOTS = RESIDUALPANEL ; MODEL ENCOUNTERS_DIFF = TIME POST TIME_AFTER POST*TIME_AFTER/ SOLUTION CHISQ CORRB; OUTPUT OUT = _GLIMMIX_POPULATION_&SRC. PRED = REGPRED; ODS OUTPUT PARAMETERESTIMATES = _MEDS0_&SRC._GLIMMIX; RANDOM _RESIDUAL_ / TYPE = AR(1); TITLE "PROC GLIMMIX - REGRESSION FOR: &SRC."; RUN; Essentially, this is a time series between an intervention and a control group for 84 months (2011 - 2017). In 2015 (49th month), the intervention began. The goal is to evaluate the impact on the intervention group compared to themselves and the comparison group. If the AR(1) stat is showing auto-correlation, is this model appropriate? Also, the AIC value is 970 with a Chi-Sq. of 8847. I also have the data t the individual level, but the AR(1) increases, and the AIC value skyrockets. And the distribution isn't Gaussian, it appears to be negative binomial to me (as this is health care utilization data). Thoughts on how to handle this panel/time series data? Thank you for your help!

rsanchez87 · ‎08-11-2018

Many thanks!

rsanchez87 · ‎08-11-2018

Thank you, I overlooked including that in my logic. The estimate for Time_After is -0.0223 (p = 0.0105). How should I interpret the AR(1) output: AR(1) estimate: 0.05051 AR(1) SE: 0.115 Residual estimate: 0.1655 Residual SE: 0.026 Thank you for your help.

rsanchez87 · ‎08-10-2018

Hi All, I am conducting a time series analysis on rates of health care utilization, I understand PROC AUTOREG is the most appropriate option in SAS. However, I do not have SAS/ETS. I have read several articles that use PROC Reg and call DWPROB to test for autocorrelation. Other sources have used Proc GLM or Proc GLIMMIX, but I do not think these can account for autocorrelation. We have designed this study well, but the intervention itself may not be strong. Are there any SAS alternative(s) for PROC Autoreg (not in SAS/ETS) that I can leverage. The proc reg and proc glimmix models are: PROC REG DATA = DATA; MODEL CHANGE = TIME POST TIME_AFTER / DWPROB; RUN; PROC GLIMMIX DATA = DATA; MODEL CHANGE = TIME POST TIME_AFTER/ SOLUTION CHISQ CORRB; RANDOM _RESIDUAL_; RUN; The Darwin & Watson test returns a value of 1.980, which suggests to me there is no autocorrelation between these variables and the outcome. PROC Reg and Prog GLIMMIX produce the same result - I am not sure if this is correct. Parameter Estimates Effect, Estimate, Standard Error, DF, t Value, Pr > |t| Intercept, 0.03472, 0.1190, 80, 0.29, 0.7713 Time, 0.01221, 0.004228, 80, 2.89, 0.0050 Post, 0.04177, 0.1800, 80, 0.23, 0.8171 time_after, -0.022390, .007764, 80, -2.88, 0.0050 This output would then suggest that the intervention resulted in a statically significant reduction of rates, albeit very small (-2% reduction). Looking forward to your ideas, questions, concerns, clarifications, all! Description of data: Change = change in rates of utilization between intervention and control Time = sequential number for the time period (84 records representing all months between 2011 and 2017) Post = 0 or 1 value for "no intervention" (0) or "intervention" (1) period Time_After = sequential number starting on the first intervention month on 01/01/2015 Full data below: SRC TIME EFFECTIVE_PERIOD POST PERIOD INT_RATE CONTROL_RATE INT_CLAIMS CONTROL_CLAIMS CHANGE CLAIMS_CHANGE TIME_AFTER ALL 1 1/1/2011 0 NO INTERVENTION 4.6 4.06 1377 1219 0.54 158 0 ALL 2 2/1/2011 0 NO INTERVENTION 3.91 3.67 1265 1111 0.24 154 0 ALL 3 3/1/2011 0 NO INTERVENTION 4.6 4.43 1566 1440 0.17 126 0 ALL 4 4/1/2011 0 NO INTERVENTION 3.42 3.86 1137 1155 -0.44 -18 0 ALL 5 5/1/2011 0 NO INTERVENTION 3.88 4.04 1339 1274 -0.16 65 0 ALL 6 6/1/2011 0 NO INTERVENTION 3.69 4.28 1182 1207 -0.59 -25 0 ALL 7 7/1/2011 0 NO INTERVENTION 3.79 3.56 1208 1027 0.23 181 0 ALL 8 8/1/2011 0 NO INTERVENTION 3.62 3.62 1229 1102 0 127 0 ALL 9 9/1/2011 0 NO INTERVENTION 4.57 4.15 1386 1129 0.42 257 0 ALL 10 10/1/2011 0 NO INTERVENTION 4.47 4.23 1354 1220 0.24 134 0 ALL 11 11/1/2011 0 NO INTERVENTION 4.86 4.24 1459 1256 0.62 203 0 ALL 12 12/1/2011 0 NO INTERVENTION 3.94 3.85 1233 1129 0.08 104 0 ALL 13 1/1/2012 0 NO INTERVENTION 4.59 3.84 1473 1227 0.75 246 0 ALL 14 2/1/2012 0 NO INTERVENTION 4.41 4.1 1369 1333 0.31 36 0 ALL 15 3/1/2012 0 NO INTERVENTION 4.51 4.57 1487 1405 -0.07 82 0 ALL 16 4/1/2012 0 NO INTERVENTION 4.18 4.02 1346 1310 0.16 36 0 ALL 17 5/1/2012 0 NO INTERVENTION 4.97 4.65 1664 1411 0.33 253 0 ALL 18 6/1/2012 0 NO INTERVENTION 4.62 3.81 1409 1228 0.81 181 0 ALL 19 7/1/2012 0 NO INTERVENTION 3.9 4.17 1254 1269 -0.27 -15 0 ALL 20 8/1/2012 0 NO INTERVENTION 4.21 3.74 1267 1151 0.47 116 0 ALL 21 9/1/2012 0 NO INTERVENTION 3.82 4.14 1104 1221 -0.32 -117 0 ALL 22 10/1/2012 0 NO INTERVENTION 4.39 4.27 1223 1276 0.13 -53 0 ALL 23 11/1/2012 0 NO INTERVENTION 3.95 4.05 1128 1156 -0.11 -28 0 ALL 24 12/1/2012 0 NO INTERVENTION 3.63 3.34 996 978 0.29 18 0 ALL 25 1/1/2013 0 NO INTERVENTION 5.12 3.88 1457 1025 1.24 432 0 ALL 26 2/1/2013 0 NO INTERVENTION 3.56 3.18 1106 894 0.38 212 0 ALL 27 3/1/2013 0 NO INTERVENTION 4.02 3.73 1206 1018 0.29 188 0 ALL 28 4/1/2013 0 NO INTERVENTION 4.37 4.09 1337 1130 0.28 207 0 ALL 29 5/1/2013 0 NO INTERVENTION 4.12 3.91 1204 1121 0.21 83 0 ALL 30 6/1/2013 0 NO INTERVENTION 3.86 3.58 1037 983 0.27 54 0 ALL 31 7/1/2013 0 NO INTERVENTION 3.83 3.89 1076 1022 -0.06 54 0 ALL 32 8/1/2013 0 NO INTERVENTION 3.96 4.07 1000 1118 -0.11 -118 0 ALL 33 9/1/2013 0 NO INTERVENTION 4.7 3.93 1084 991 0.77 93 0 ALL 34 10/1/2013 0 NO INTERVENTION 4.7 4.13 1213 1147 0.57 66 0 ALL 35 11/1/2013 0 NO INTERVENTION 4.24 3.19 1098 872 1.05 226 0 ALL 36 12/1/2013 0 NO INTERVENTION 3.71 3.16 935 831 0.54 104 0 ALL 37 1/1/2014 0 NO INTERVENTION 4.77 3.55 1263 891 1.22 372 0 ALL 38 2/1/2014 0 NO INTERVENTION 3.92 3.33 999 903 0.59 96 0 ALL 39 3/1/2014 0 NO INTERVENTION 5.26 4.54 1406 1135 0.72 271 0 ALL 40 4/1/2014 0 NO INTERVENTION 5.01 4.64 1339 1219 0.37 120 0 ALL 41 5/1/2014 0 NO INTERVENTION 5.18 4.69 1317 1203 0.49 114 0 ALL 42 6/1/2014 0 NO INTERVENTION 4.48 4.39 1175 1061 0.09 114 0 ALL 43 7/1/2014 0 NO INTERVENTION 4.39 4.36 1163 1092 0.03 71 0 ALL 44 8/1/2014 0 NO INTERVENTION 3.72 3.81 1033 1003 -0.09 30 0 ALL 45 9/1/2014 0 NO INTERVENTION 5.39 4.18 1415 1079 1.21 336 0 ALL 46 10/1/2014 0 NO INTERVENTION 5.18 4.41 1381 1210 0.77 171 0 ALL 47 11/1/2014 0 NO INTERVENTION 4.09 3.56 1155 923 0.53 232 0 ALL 48 12/1/2014 0 NO INTERVENTION 4.72 3.87 1278 1079 0.85 199 0 ALL 49 1/1/2015 1 INTERVENTION 5.15 4.22 1418 1106 0.93 312 1 ALL 50 2/1/2015 1 INTERVENTION 4.3 3.73 1174 994 0.57 180 2 ALL 51 3/1/2015 1 INTERVENTION 6.13 4.56 1631 1221 1.57 410 3 ALL 52 4/1/2015 1 INTERVENTION 5.42 4.66 1473 1300 0.77 173 4 ALL 53 5/1/2015 1 INTERVENTION 4.74 3.83 1276 1150 0.91 126 5 ALL 54 6/1/2015 1 INTERVENTION 4.81 4.15 1325 1144 0.66 181 6 ALL 55 7/1/2015 1 INTERVENTION 4.81 4.25 1391 1131 0.56 260 7 ALL 56 8/1/2015 1 INTERVENTION 4.49 4.44 1278 1221 0.05 57 8 ALL 57 9/1/2015 1 INTERVENTION 4.53 4.46 1252 1135 0.07 117 9 ALL 58 10/1/2015 1 INTERVENTION 5.52 4.61 1493 1245 0.91 248 10 ALL 59 11/1/2015 1 INTERVENTION 4.59 4.33 1268 1200 0.26 68 11 ALL 60 12/1/2015 1 INTERVENTION 4.68 4.63 1363 1220 0.05 143 12 ALL 61 1/1/2016 1 INTERVENTION 4.81 4.2 1267 1168 0.61 99 13 ALL 62 2/1/2016 1 INTERVENTION 4.36 4.76 1241 1258 -0.41 -17 14 ALL 63 3/1/2016 1 INTERVENTION 6.01 4.84 1679 1370 1.17 309 15 ALL 64 4/1/2016 1 INTERVENTION 4.94 4.73 1408 1379 0.2 29 16 ALL 65 5/1/2016 1 INTERVENTION 4.84 4.49 1407 1263 0.35 144 17 ALL 66 6/1/2016 1 INTERVENTION 5.9 5.07 1472 1415 0.84 57 18 ALL 67 7/1/2016 1 INTERVENTION 5.06 4.73 1316 1344 0.33 -28 19 ALL 68 8/1/2016 1 INTERVENTION 5.59 4.98 1454 1338 0.61 116 20 ALL 69 9/1/2016 1 INTERVENTION 4.99 5.14 1321 1271 -0.16 50 21 ALL 70 10/1/2016 1 INTERVENTION 5.09 4.56 1343 1310 0.53 33 22 ALL 71 11/1/2016 1 INTERVENTION 4.95 4.41 1336 1174 0.54 162 23 ALL 72 12/1/2016 1 INTERVENTION 4.61 4.43 1289 1234 0.18 55 24 ALL 73 1/1/2017 1 INTERVENTION 4.4 4.58 1185 1356 -0.18 -171 25 ALL 74 2/1/2017 1 INTERVENTION 4.66 4.44 1247 1187 0.22 60 26 ALL 75 3/1/2017 1 INTERVENTION 5.59 4.89 1480 1399 0.7 81 27 ALL 76 4/1/2017 1 INTERVENTION 4.24 4.33 1170 1244 -0.09 -74 28 ALL 77 5/1/2017 1 INTERVENTION 5.7 4.64 1496 1336 1.06 160 29 ALL 78 6/1/2017 1 INTERVENTION 5.16 4.46 1451 1270 0.7 181 30 ALL 79 7/1/2017 1 INTERVENTION 4.41 3.92 1318 1200 0.49 118 31 ALL 80 8/1/2017 1 INTERVENTION 5.3 4.2 1455 1268 1.11 187 32 ALL 81 9/1/2017 1 INTERVENTION 5.17 4.65 1434 1276 0.52 158 33 ALL 82 10/1/2017 1 INTERVENTION 5.05 5.15 1379 1421 -0.1 -42 34 ALL 83 11/1/2017 1 INTERVENTION 5.27 4.89 1316 1367 0.38 -51 35 ALL 84 12/1/2017 1 INTERVENTION 4.35 4.2 1243 1196 0.15 47 36 Thank you! Some References: 1. (USES PROC GLIMMIX) Wong, EC. Analysing Phased Intervention with Segmented Regression and Stepped Wedge Deisngs: https://www.lexjansen.com/wuss/2014/74_Final_Paper_PDF.pdf 2. (USES PROC AUTOREG) Penfold, R. Use of Interrupted Time Series Analysis in Evaluating Health Care Quality Improvements https://www.academicpedsjnl.net/article/S1876-2859(13)00210-6/pdf

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Re: Single group comparison of incidence pre and post

Re: Pre post tests for ER and IP visit outcomes between cases and cont...

Pre post tests for ER and IP visit outcomes between cases and controls

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Re: Alternatives to Proc AUTOREG for healthcare time series analysis

Re: Alternatives to Proc AUTOREG for healthcare time series analysis

Re: Alternatives to Proc AUTOREG for healthcare time series analysis

Re: Alternatives to Proc AUTOREG for healthcare time series analysis

Re: Alternatives to Proc AUTOREG for healthcare time series analysis

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Re: Single group comparison of incidence pre and post

Re: Single group comparison of incidence pre and post

Re: Pre post tests for ER and IP visit outcomes between cases and cont...

Pre post tests for ER and IP visit outcomes between cases and controls

Re: sql tables and ambiguous reference error message

Re: Imputing data with valid skips

Re: Alternatives to Proc AUTOREG for healthcare time series analysis

Re: Alternatives to Proc AUTOREG for healthcare time series analysis

Re: Alternatives to Proc AUTOREG for healthcare time series analysis

Re: Alternatives to Proc AUTOREG for healthcare time series analysis

Re: Alternatives to Proc AUTOREG for healthcare time series analysis

Re: Alternatives to Proc AUTOREG for healthcare time series analysis

Alternatives to Proc AUTOREG for healthcare time series analysis