Hi All, I would like to ask if there is a log captured when a user access has been removed from a library or a user group? I have user reported that he suddenly not able to access to a library. Upon checking, it is confirmed that his does not have access to the library. Issue is, he insists that he has the access to the library previously.  Do metadata logs capture the event where the user access was removed?   Thanks. ... View more

I would like to be able to hover over a data set name in the server\library window and get the label. When i hover over the name I get nothing. Indeed no hovering anywhere in the server window produces any result. I cannot find an option to change this. I know I can right click on a data set and select properties and click on that and then the dat set label along with other attributes will show up in a separate window (that locks the data set while it is open).  That's not conducive to work flow.  ... View more

Hello Lets say I have a sas program that run long time (1 hour). The program should run 48 times (each time with different macro arguments). The final result of the run is update of one permanent data set (This data set is saved in a permanent libarary). Note-Each run update the same data set and add rows to this data set (So this data set is called accumulated data set because each  run add it rows) My question- Is it possible that some people run the same sas program (with different macro arguments) ? So ,multiple runs (done by multiple people) update the same data set Is it possible? The idea is to save run time because multiple people run in same time   ... View more

Hello Lets say that I want to run statistics on multiple variables. Lets say that this code is working 100% and I get desired results. My question- Is there better way to write the code (using proc step instead of proc sql)? Please note that in real word I run it on 300 fields and data set has 2 million rows   %macro dist(VAR,tbl,level,format); %do j=1 %to 6; %let version=%scan(&Vector_Versions.,&j.,+); proc sql; create table _dist_ as select Left("&VAR.") as Var_name LENGTH=300, put(&VAR.,&Format.) as Cat LENGTH=100, count(*) as nr, calculated nr/(select count(*) as total_nr from &tbl.) as PCT format=percent8.5 from &tbl. group by calculated CAT union all select Left("&VAR.") as Var_name LENGTH=300, 'Total' as CAT, count(*) as nr, 1 as PCT format=percent8.5 from &tbl. ; quit; proc sort data=_dist_; by var_name level cat_order; Run; proc append data=_dist_ base=r_r.Accum_tbl force;quit; %end; %mend dist; %dist(Var=Wealth,tbl=Y_ttt&version.,level=Y,Format=Fmt_1F.) %dist(Var=LoansBalance ,tbl=Y_ttt&version.,level=Y,Format=Fmt_continuous.) %dist(Var=AVG_PAYOFF_LAST_MON,tbl=Y_ttt&version.,level=Y,Format=Fmt_1F.) %dist(Var=HIYUV_EXLISCAR_AMT,tbl=L_ttt&version.,level=L,Format=Fmt_1F.)   %macro dist(VAR,tbl,level,format); %do j=1 %to 6; %let version=%scan(&Vector_Versions.,&j.,+); proc sql; create table _dist_ as select Left("&VAR.") as Var_name LENGTH=300, put(&VAR.,&Format.) as Cat LENGTH=100, count(*) as nr, calculated nr/(select count(*) as total_nr from &tbl.) as PCT format=percent8.5 from &tbl. group by calculated CAT union all select Left("&VAR.") as Var_name LENGTH=300, 'Total' as CAT, count(*) as nr, 1 as PCT format=percent8.5 from &tbl. ; quit; proc sort data=_dist_; by var_name level cat_order; Run; proc append data=_dist_ base=r_r.Accum_tbl force;quit; %end; %mend dist; %dist(Var=Wealth,tbl=Y_ttt&version.,level=Y,Format=Fmt_1F.) %dist(Var=LoansBalance ,tbl=Y_ttt&version.,level=Y,Format=Fmt_continuous.) %dist(Var=AVG_PAYOFF_LAST_MON,tbl=Y_ttt&version.,level=Y,Format=Fmt_1F.) %dist(Var=HIYUV_EXLISCAR_AMT,tbl=L_ttt&version.,level=L,Format=Fmt_1F.)   ... View more

Hello SAS experts,   I am working on single-group, two-stage interrupted time series analysis and have written SAS code for both the continuous outcome (using PROC MIXED) and the binary outcome (using a Poisson model). I have included detailed comments in the code and have accounted for potential confounders. I have also attached the resulting plots and output tables, as well as a sample of the original dataset for reference.   Could you please review the PROC MIXED version and the Poisson version of the code, along with the generated graphs and results, and let me know if they are correct? I would greatly appreciate any suggestions for optimization.   Thank you very much for your help!   /********************************************* Step 1: Single-group, two-stage ITS (continuous Y) — PROC MIXED version variables: - id : Subject identifier - MO : Month (1–12) - time 1 : (equal to MO) - Y : Continuous outcome variable (each person’s annual observation) - intervention1 : Indicator for first intervention (0/1), equals 1 from MO=4 onward - intervention2 : Indicator for second intervention (0/1), equals 1 from MO=6 onward - city : Binary indicator for location (0/1) - count_gr : Outpatient visit count group (1/2/3) *********************************************/ proc mixed data=ITS.COST method=ml; /* (1) Specify categorical variables */ class id city count_gr; /* (2) Main model: time trend, two intervention effects and interactions, and covariates */ model COST = /* change variable placeholder */ time1 intervention1 time1*intervention1 intervention2 time1*intervention2 /* Covariates */ city count_gr / solution; /* (3) Specify repeated measures structure (multiple yearly observations per subject): unstructured covariance */ repeated / subject=id type=un; /* (4) Save model results to its_model_continuous for subsequent PROC PLM scoring */ store out=its_model_continuous; /* (5) Estimate statements: segmented regression estimates of slopes and level changes */ estimate 'Pre slope' time1 1 / cl e; estimate 'Post1 slope' time1 1 time1*intervention1 1 / cl e; estimate 'Gap1 (1st level shift)' intervention1 1 time1*intervention1 1 / cl e; estimate 'Post2 slope' time1 1 time1*intervention1 1 time1*intervention2 1 / cl e; estimate 'Gap2 (2nd level shift)' intervention2 1 time1*intervention2 1 / cl e; run; /********************************************* Step 2: Use PROC PLM to obtain predicted values and standard errors for each time point *********************************************/ proc plm restore=its_model_continuous; score data=ITS.COST out=pred_panel_cont predicted=pred_value stderr=StdErrPred; run; /********************************************* Step 3: Aggregate average predicted values and 95% confidence intervals for each time point *********************************************/ proc summary data=pred_panel_cont nway; class time1; var pred_value StdErrPred; output out=avg_pred_cont mean= / autoname; run; data avg_pred_cont; set avg_pred_cont; /* If after summary, variable names are pred_value_Mean and StdErrPred_Mean */ pred = pred_value_Mean; se = StdErrPred_Mean; lcl = pred - 1.96 * se; ucl = pred + 1.96 * se; run; /********************************************* Step 4: Plot ITS predicted values (continuous Y → predicted values and 95% CI) *********************************************/ ods graphics on / attrpriority=none; proc sgplot data=avg_pred_cont; /* Confidence interval band */ band x=time1 lower=lcl upper=ucl / fillattrs=(color=gray transparency=0.9) legendlabel="95% Confidence Interval"; /* Predicted values line */ series x=time1 y=pred / markers lineattrs=(thickness=2 color=blue) markerattrs=(symbol=circlefilled size=8 color=blue) legendlabel="Predicted Value"; /* Add intervention point reference lines (first intervention time1=3, second intervention time1=5) */ refline 3 / axis=x lineattrs=(pattern=shortdash color=black) label="First Intervention" labelattrs=(weight=bold size=9); refline 5 / axis=x lineattrs=(pattern=shortdash color=black) label="Second Intervention" labelattrs=(weight=bold size=9); xaxis label="Time (Month)" values=(1 to 12 by 1) /* Force display of 1,2,...,12 */ valuesdisplay=("1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12") integer; /* Display only integer tick values */ yaxis label="Total Cost"; keylegend / position=bottom location=outside across=1; run; Dataset：ITS.COST /********************************************* Single-group two-stage ITS (binary Y) — Poisson model version *********************************************/ /* Step 1: Aggregate → annual PIM_count, N_person */ proc sql; create table ITS.POLY_agg as select MO, /* Cumulative number of POLY events */ sum(POLY) as POLY_count, /* Total number of people in that year (distinct id) */ count(distinct id) as N_person, /* 1. For binary categorical variables, use mean() to get proportion (0/1 only needs one variable) */ mean(city) as prop_city, /* Proportion of urban vs. non-urban */ /* 4. Proportions for count_gr (outpatient visit count groups 1, 2, 3) */ sum(case when count_gr = 1 then 1 else 0 end) / count(distinct id) as prop_count1, sum(case when count_gr = 2 then 1 else 0 end) / count(distinct id) as prop_count2, sum(case when count_gr = 3 then 1 else 0 end) / count(distinct id) as prop_count3 from VAR.POLY group by MO order by MO ; quit; /* Step 2: Add time, intervention indicators, and offset */ data ITS.POLY_poisson; set ITS.POLY_agg; time = MO; if MO >= 4 then intervention1 = 1; else intervention1 = 0; if MO >= 6 then intervention2 = 1; else intervention2 = 0; lnN = log(N_person); /* Poisson offset */ run; /* Step 3: ITS model estimation */ proc genmod data=ITS.POLY_poisson; model POLY_count = time intervention1 time*intervention1 intervention2 time*intervention2 /* Control covariates: binary categorical variables (0/1 feed in as proportions) */ prop_city /* Proportion of urban vs. non-urban (city=1) */ /* Use prop_count1, prop_count2; count_gr=0 is reference */ prop_count1 /* Proportion with count_gr=1 */ prop_count2 /* Proportion with count_gr=2 */ / dist=poisson link=log offset=lnN; /* ITS estimates for slopes/level changes in each stage (adjust as needed) */ estimate 'PRE slope' time 1 / exp; estimate 'POST1 slope' time 1 time*intervention1 1 / exp; estimate 'GAP1 (1st level shift)' intervention1 1 / exp; estimate 'POST2 slope' time 1 time*intervention1 1 time*intervention2 1 / exp; estimate 'GAP2 (2nd level shift)' intervention2 1 / exp; output out=pred1 pred=predcount lower=lcl upper=ucl; run; /* Step 4a: Calculate “rate” in pred1, If pred1 already contains POLY_count and N_person, process as follows:*/ data pred1_rate; set pred1; /* Calculate rate = event count ÷ population */ rate = POLY_count / N_person; format rate percent7.2; run; /* Step 4b: Plot “rate” line and scatter with SGPLOT */ proc sgplot data=pred1_rate noautolegend; /* (1) Rate line */ series x=time y=rate / lineattrs=(thickness=2 color=blue) name="RateLine"; /* (2) Rate scatter points + data labels */ scatter x=time y=rate / markerattrs=(symbol=CircleFilled color=blue size=8) datalabel=rate datalabelattrs=(size=9 color=black) name="RatePts"; /* (3) Intervention time points (first at time=4; second at time=6) */ refline 4 6 / axis=x lineattrs=(pattern=shortdash color=black); /* (4) Axis settings */ xaxis label="Time (Year)" values=(1 to 12 by 1) valuesdisplay=("1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12") integer; yaxis label="POLY Rate"; /* (5) Legend */ keylegend "RatePts" / title="Observed Rate (Points/Labels)" position=topright across=1; keylegend "RateLine" / title="Observed Rate (Line)" position=topright across=1; run; Dataset：VAR.POLY Dataset：ITS.POLY_agg Dataset：ITS.POLY_poisson   ... 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