1. An exploration of quality gaps in SDTM implementation activities and ideas on how to address these gaps through appropriate resourcing
Dianne Weatherall:

2. GOAL Adoption of CDISC standards has led to:
new processes (aCRF, metadata, programming)
new responsibilities
Goal: to discuss the “best” SDTM team to implement the new process

3. DEFINE THE PROBLEM
Define what is wrong with the current setup

4. ROOT CAUSE OF QUALITY ISSUES
Poll on the SDTM LinkedIn group: What is the primary cause of quality issues in SDTM?

5. ROOT CAUSE OF QUALITY ISSUES
Lack of understanding of SDTM – WHY? 1 2 3
Lack of skills
Lack of training
Expensive
It is complicated
Too much room for personal preference
Customer specific implementations
Unclear process
Too many cross-functional teams involved
Companies are too silo’ed
Lack of expert support
It takes time and effort to become an expert

6. ROOT CAUSE OF QUALITY ISSUES
Lack of understanding of clinical data – WHY? 1 2 3
Lack of data / clinical skills of teams
Clinical data is complicated
Highly un-normalized
Database structures are often developed for data entry and clinician preference, not CDASH/SDTM standards
The CRF changes over time
Therapeutic areas have complicated study designs (e.g. cohort changes)
Poor planning from the study design stage

7. ROOT CAUSE OF QUALITY ISSUES
Non-standard data – WHY? 1 2 3
Legacy studies
SDTM standards are relatively recent
Customer specific requirements
Customer specific implementations
Customer legacy systems are not CDISC-compatible
Therapeutic areas have complicated study designs (e.g. cohort changes)
Poor planning from the study design stage

8. SUMMARY OF ROOT CAUSES Company silo’s 
Lack of data skills of Biostats teams 
Lack of CDASH / SDTM skills of Data teams 
Time and effort to build expertise 
Customer-specific 
Poor study planning 
Expensive  - join a user group!

9. CRITERIA FOR THE BEST SDTM TEAM
Corporate structure
Team scenarios

10. CORPORATE STRUCTURE Operations Biometrics Data Management
Biostatistics
*** Blur the line between DM and BIOS

11. BEST TEAM SCENARIO SDTM experts? Programmers? Biostatisticians?
Data genius?
Cheap?
Available?

12. ROLES AND SKILLS Data collection: CRF design (CDASH / SDTM experts)
SDTM mapping: CRF annotation/ specifications (SDTM experts)
Programming: (SAS experts)
Review: (SDTM + Biostatistics + Data experts)
Data Management Biostatistics

13. TEAM SCENARIO 1 Advantages Disadvantages Continuity from aCRF
Study
CRF / DB Design
aCRF
Specs
Programming 1 A
Reviewer R1
B1 (domain A)
B2 (domain B)
Etc
Reviewer R2 B1 B2
Advantages
Disadvantages
Continuity from aCRF
Time consuming
SME on certain domains
Pressure (updates)
Small team
Inconsistent mapping across domains
Boring
Lack of continuity from CRF design
Inconsistent metadata

14. TEAM SCENARIO 2 Advantages Disadvantages
Study
CRF / DB Design
aCRF
Specs
Programming 1 A
Reviewer R1
B (all domains)
Reviewer R2 C1 C2
Advantages
Disadvantages
Consistent mapping across domains
Time consuming
Pressure (updates)
Lack of continuity from CRF design
Inconsistent metadata

15. TEAM SCENARIO 3 Advantages Disadvantages
Study
CRF / DB Design
aCRF
Specs
Programming 1 A
Reviewer R1
Team B
(all domains)
Reviewer R2 C1 C2
Advantages
Disadvantages
Consistent mapping across domains
Resourcing issue (more people needed), particularly for a submission
Less advanced tasks can be done by cheaper resources, freeing advanced programmers for critical tasks
Lack of continuity from CRF design
Expert group for support

16. TEAM SCENARIO 4 Advantages Disadvantages
Study
CRF / DB Design
aCRF
Specs
Programming 1 A
Reviewer Team
Team B
(all domains) C1 C2
Advantages
Disadvantages
Consistent mapping across domains
Difficult to develop/find reviewer skills (for design and mapping)
Less advanced tasks can be done by cheaper resources, freeing advanced programmers for critical tasks
Expert group for support
Continuity of whole process

17. Other things to consider
Submissions
Continuity across studies
Consistency across studies
Change control
Bottle necks (reviewer team)
ADaM / statistical output resourcing

18. RESOURCE CRITERIA FROM TO SDTM after design Just save costs
Allocate availability TO
SDTM during design
Invest in expert team
Look at continuity

19. THE BEST SDTM TEAM Expert Reviewer Team (CDASH / SDTM)
Team Leader (Biometrics)
CRF Designer
aCRF / SDTM Mapper Team
Programmer(s)
*** Understand the data
*** Understand the purpose

20. ?????????????????????????????????????
QUESTIONS