Additional information on completing the Validation Sheet #81
eljanssens
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In the current Rule Authoring Process Training: https://wiki.cdisc.org/pages/viewpage.action?pageId=142086812&spaceKey=CORE&title=Training%2BLinks
It was explained that for empty values, [ABSENT] should be used. This is incorrect. [ABSENT] should be used for missing datasets and variables but not for empty values. In case a value of a variable is empty = 'null' then the error value can be left empty in the validation sheet.
1. Checking incorrect presence of a variable:
Highlight (yellow) the variable in the dataset

Complete the validation sheet as such:

2. Checking incorrect absence of a variable:
You can not highlight (yellow) the variable in the dataset as it is not present in the negative dataset
Complete the validation sheet as such:

3. Checking empty or completed values
4. Output from 2 or more datasets
If you have a rule that is checking between 2 or more datasets, then you should always start with listing the output records from the starting domain first. The records from the merged domain should be listed after that. If you switch and start with listing the merged dataset, then the validation will not be succesfull.
Example: CG0136/CORE-000252 - start from DM and then matches DS.
Correct validation sheet:

Incorrect validation sheet:

5. Adding operations in the output
Although adding operations as output variables should be limited as they can result in huge lists in the output, there are cases where these can be added value for the reviewer. These operations can just be added to the validation sheet similar to what you would do for variables and values.
Example: CG0006/CORE-000529
6. Presence or absence of datasets
Example: CG0191/CORE-000188
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