| PHUSE US |
2024 |
Guidance Beyond the SDTM Implementation Guide |
Michael Beers |
source presentation source |
10 |
| PHUSE US |
2020 |
Confusing Data Validation Rules Explained, Part 2 |
Michael Beers |
source |
6 |
| PHUSE US |
2020 |
Getting It Right: Refinement of SEND Validation Rules |
Kristin Kelly |
source presentation source |
6 |
| PHUSE US |
2020 |
SUPPQUAL Datasets: Good Bad and Ugly |
Sergiy Sirichenko |
source presentation source |
13 |
| PHUSE EU |
2019 |
Considerations When Representing Multiple Subject Enrollments in SDTM |
Kristin Kelly |
source presentation source |
7 |
| PHUSE EU |
2019 |
How to use SUPPQUAL for specifying natural key variables in define.xml? |
Sergiy Sirichenko |
source presentation source |
8 |
| PHUSE EU |
2018 |
How to automate validation with Pinnacle 21 command line interface and SAS |
Amy Garrett |
source poster source |
7 |
| PharmaSUG |
2006 |
Human Efficiency: A Factor That Can't Be Ignored When Designing a SAS Macro |
Michele Zhang |
|
8 |
| PharmaSUG |
2006 |
The Skinny on Processing Very Large Health Claims Data Sets |
Gopal Rajagopal |
|
10 |
| PharmaSUG |
2006 |
When There Are Too Many Macro Parameters and We Need One More The Challenges of Writing and Using a Complex Macro |
Eric Qi |
|
9 |
| PharmaSUG |
2005 |
To Macro or Not To Macro? |
Amy Gillespie |
|
8 |
| PharmaSUG |
2001 |
Can standards be implemented effectively without compromising science? |
Amy Morris |
|
4 |