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Posted 11-03-2021 09:38 AM
(2657 views)
Hi
I am doing QC on clinical sas ADAM datasets like ADSL ADAE Etc ..for which I need a help. Infact I am using Proc freq and basic Sql statements to cross check data to find errors. I am not able to find all of the critical errors which is a big problem.
Does anybody provide some programs to cross check data perfectly ? I am really in need of help and I would be very grateful if you could help me out.
Thanks a lot.
I am doing QC on clinical sas ADAM datasets like ADSL ADAE Etc ..for which I need a help. Infact I am using Proc freq and basic Sql statements to cross check data to find errors. I am not able to find all of the critical errors which is a big problem.
Does anybody provide some programs to cross check data perfectly ? I am really in need of help and I would be very grateful if you could help me out.
Thanks a lot.
5 REPLIES 5
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- Using ADaM.adae specification document(s)
- program the data set. (don't look at the source program).
- PROC COMPARE source ADAE with your QC.
Not perfect if source and QC programmers make the same mistake, but you should be able to sleep at night.
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Thank you, yes but it’s takes lot of time to program everything and proc compare. So I am looking for much faster way I can sum it my work.
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Are you using Pinnacle 21 Community Validator ? it is the easiest way to check consistency between domains and it also checks domains against controlled terminology.
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Thank you for reply
This is internal QC to use codes and cross check data.
This is internal QC to use codes and cross check data.
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There is really no such thing as a tool or program that will perfectly validate or check your data. However there are techniques in SAS that can help you like this:
proc freq data = sashelp.class;
table _numeric_;
run;
This program will count discrete levels of all numeric variables in the chosen dataset. Obviously this is only useful for non-continuous numerics but it gives you an idea as to how you can approach validation.
Edit: An example of how to check for missing or zero values:
data test;
set sashelp.class;
output;
if name in ('Carol','Thomas') then do;
age = 0; height = . ; weight = 0;
output;
end;
run;
data test;
drop i;
set test;
array nums (*) _numeric_ ;
do i = 1 to dim(nums);
if nums(i) in (.) then Missing_Flag = 'Y';
if nums(i) in (0) then Zero_Flag = 'Y';
end;
run;
proc freq data = test;
where Missing_Flag = 'Y' or Zero_Flag = 'Y';
table name * _numeric_ / missing list;
run;