1. An exploration of quality gaps in SDTM implementation activities and ideas on how to address these gaps through appropriate resourcing Dianne Weatherall: 2013-04-11

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? 123 Lack of skillsLack of trainingExpensive It is complicatedToo much room for personal preference Customer specific implementations Unclear processToo many cross-functional teams involved Companies are too silo’ed Lack of expert supportLack of skillsIt takes time and effort to become an expert

6. ROOT CAUSE OF QUALITY ISSUES Lack of understanding of clinical data – WHY? 123 Lack of data / clinical skills of teams Clinical data is complicatedHighly un-normalizedDatabase structures are often developed for data entry and clinician preference, not CDASH/SDTM standards The CRF changes over timeTherapeutic 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? 123 Legacy studiesSDTM standards are relatively recent Customer specific requirementsCustomer 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 Operations Biometrics Data Management Biostatistics Operations 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 StudyCRF / DB DesignaCRFSpecsProgramming 1A Reviewer R1 B1 (domain A) B2 (domain B) Etc Reviewer R2 B1 B2 Reviewer R2 B1 B2 Reviewer R2 AdvantagesDisadvantages Continuity from aCRFTime consuming SME on certain domainsPressure (updates) Small teamInconsistent mapping across domains Boring Lack of continuity from CRF design Inconsistent metadata

14. TEAM SCENARIO 2 StudyCRF / DB DesignaCRFSpecsProgramming 1A Reviewer R1 B (all domains) Reviewer R2 C1 C2 Reviewer R2 C1 C2 Reviewer R2 AdvantagesDisadvantages Consistent mapping across domains Time consuming Pressure (updates) Lack of continuity from CRF design Inconsistent metadata

15. TEAM SCENARIO 3 StudyCRF / DB DesignaCRFSpecsProgramming 1A Reviewer R1 Team B (all domains) Reviewer R2 Team B (all domains) Reviewer R2 C1 C2 Reviewer R2 AdvantagesDisadvantages 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 StudyCRF / DB DesignaCRFSpecsProgramming 1A Reviewer Team Team B (all domains) Reviewer Team Team B (all domains) Reviewer Team C1 C2 Reviewer Team AdvantagesDisadvantages 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 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 ?????????????????????????????????????