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I am currently analyzing the impact of an intervention on medication numbers using difference-in-difference analysis, but I have encountered several challenges. Following the SAS support instructions, I conducted the difference-in-difference analysis. However, I noticed a discrepancy between my results and SAS's example (Usage Note 61830: Estimating the difference in differences of means). In the example, the value of 'Mean Estimate' in 'Contrast Estimate Results' is identical to the 'Estimate' in 'Least Squares Means Estimate'. However, in my case, these values were different. I suspect this could be due to my use of the negative binomial distribution with a log link, resulting in exponential values. Consequently, I am unsure whether to rely on the 'Mean Estimate' in 'Contrast Estimate Results' or the 'Estimate' in 'Least Squares Means Estimate', and how to interpret the results." Contrast Estimate Results Label Mean Estimate Mean Confidence Limits L'Beta Estimate Standard Error diff in diff 1.51 1.49 0.41 0.0051 a*b Least Squares Means a b Estimate Standard Error z value Pr > |z| 1 1 0.77 0.00434 178.19 <.0001 1 0 0.03 0.00508 6.5 <.0001 0 1 0.72 0.00408 177.71 <.0001 0 0 0.40 0.00426 93.11 <.0001 Least Squares Means Estimate Effect Label Estimation Standard Error z value Pr > |z| time*hospitalize diff in diff 0.41 0.00509 81.01 <.0001
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Hi, I'm currently merging multiple datasets and consistently using the following SQL code to verify there are no duplicate IDs at each stage of the process: proc sql; select count(distinct ID) as UniqueIDs, count(*) as NObs from dataset; quit; This approach effectively identifies duplicates when datasets have multiple timepoints, and I make sure to only select one timepoint per ID. Throughout the merging process, my checks confirm that all IDs remain unique (e.g., 1500 observations and 1500 unique IDs). However, after the final merge, the same SQL query unexpectedly shows 1500 observations but only 1300 unique IDs. Manual verification confirms that duplicates are present, despite prior checks showing no such issues. I'm looking for insights into why these duplicates weren’t detected sooner by the SQL query, or if there's a specific merging condition I might have overlooked. Edited to add: The SQL code above is the one that I use to check each dataset for duplicates (which it seems to find pretty well) and after each merge. Merge code (throughout and also the last one): data merged_dataset; merge dataset1 dataset2; by ID; run; Attaching the LOG for the last merge + SQL check. The result for this was 1181 UniqueIDs and 1229 NObs, while previously I was getting 1229 UniqueIDs and 1229 NObs. LOG for the last merge + SQL check
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I need to define column width to standardize the look of the report. However, when I set the column width it automatically expands the row height for each cell. I tried setting the cellheight in various places in the code but I can't seem to constrain it. How is this done?
Here's an example using the sashelp.cars dataset that gives me the same results.
ODS results off;
ODS listing close;
ODS TAGSETS.EXCELXP
file="C:\test.xml"
STYLE=Printer
OPTIONS (
Sheet_Name = "NEW"
Orientation = 'landscape'
FitToPage = 'no'
Pages_FitWidth = '1'
Pages_FitHeight = '100'
embedded_titles = 'yes'
);
PROC REPORT DATA=sashelp.cars
style(header)=[fontfamily=helvetica fontsize=8pt textalign=l]
style(column)=[fontfamily=helvetica fontsize=8pt textalign=l TAGATTR='format:text'];
columns make model type origin msrp drivetrain horsepower mpg;
DEFINE make / STYLE(column)={width=2cm};
DEFINE model / STYLE(column)={width=2cm};
DEFINE type / STYLE(column)={width=2cm};
DEFINE origin / STYLE(column)={width=2cm};
DEFINE msrp / STYLE(column)={width=15cm};
DEFINE drivetrain / STYLE(column)={width=2cm};
DEFINE horsepower / STYLE(column)={width=2.5cm};
DEFINE mpg / STYLE(column)={width=2cm};
RUN;
ods tagsets.excelxp close;
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I am currently analyzing the impact of an intervention on medication numbers using difference-in-difference analysis, but I have encountered several problems. Following the SAS support instructions, I conducted the difference-in-difference analysis. However, I noticed a discrepancy between my results and SAS's example 61830. In the example, the value of 'Mean Estimate' in 'Contrast Estimate Results' is identical to the 'Estimate' in 'Least Squares Means Estimate'. However, in my case, these values were different. I suspect this could be due to my use of the negative binomial distribution with a log link, resulting in exponential values. Consequently, I am unsure whether to rely on the 'Mean Estimate' in 'Contrast Estimate Results' or the 'Estimate' in 'Least Squares Means Estimate', and how to interpret this results. Thank you very much. Here is my result: Contrast Estimate Results Label Mean Estimate Mean Confidence Limits L'Beta Estimate Standard Error Pr > ChiSq diff in diff 1.51 1.49-1.52 0.41 0.0051 <.0001 time*intervention Least Squares Means time intervention Estimate SE z value Pr > |z| 1 1 0.77 0.00434 178.19 <.0001 1 0 0.03 0.00508 6.5 <.0001 0 1 0.72 0.00408 177.71 <.0001 0 0 0.40 0.00426 93.11 <.0001 Least Squares Means Estimate Effect Label Estimate SE Z value Pr > |z| time*intervention diff in diff 0.41 0.00509 81.01 <.0001 SAS code: proc genmod data = ALL_DID;
class _MatchID intervention time /ref = first;
model drug_use = time intervention time*intervention/dist = negbin link=log;
repeated subject =_MatchID/type = exch;
estimate "diff in diff" time*intervention 1 -1 -1 1;
lsmeans time*intervention;
lsmestimate time*intervention "diff in diff" 1 -1 -1 1;
run;
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data vs ; input patient cpevent$ vstest$ vsren vsorresu$; cards ; 100 base pr 78 min 100 scrn hr 72 /min 100 week1 sbp 70 mmhg 100 week7 dbp 110 mm 100 week21 weight 75 kg
100 fwp height 120 kg 100 base pr 78 min 100 scrn hr 79 /mn 100 week1 sbp 70 mmhg 100 week7 dbp 110 mm 100 week21 weight 80 pounds
100 fwp height 75 kg ;
Condition below
If VSTEST having same VSREN and VSORRESU for 4 consecutive visits then highlight those with flag as
"dulipcate record"
Based on above condition and data , create a flag
Please anyone help me .....
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