1. An Introduction to Data Lifecycle Plans ® Kit Howard Kestrel Consultants Data Lifecycle Plan ® is a registered trademark of Kestrel Consultants.

2. Agenda Quality Standards and Quality Data Lifecycle Plans – Structure – Implementation – Impact Regulatory’s Role 2

3. Defining Quality Deming Conforming to the customer’s expectations. Nothing else matters. Juran “Fit for their intended uses in operations, decision making and planning.” Sales Giving the customer not only what they want, but what they didn’t know they wanted Manufacturing Fit for the use to which the product will be put

4. “Quality” is not an absolute attribute It depends on the needs and perceptions of the customer and the degree to which they are met

5. Institutes of Medicine report in 1999 defined “Quality Data” “Data that support the same interpretations and conclusions as those derived from error-free data ” It does not say that quality data are error-free

6. 6 CDM Statistician Clinician Programmer CRA Writer Site Subject DATA Who Touches the Data?

7. And in the Bigger Picture? Subjects Patients Regulations Ethics Publication Submission Company Warehouse Registries Janus

8. Product Registration View Right Questions Right analyses Right Label Quality NDA 8 Right Subjects, Right Data

9. The Great Wall-Toss Statistician Clinician Programmer CRA Data Manager Regulatory Writer QA 9

10. Results of the Great Wall-Toss Much time and money are spent Reinventing and recreating the wheel Auditing to identify problems and cleaning to remove them Fixing planning problems Fixing communication problems With no guarantee of quality 10

11. Agenda Quality Standards and Quality Data Lifecycle Plans – Structure – Implementation – Impact Regulatory’s Role 11

12. Good Standards... Use common – Dictionaries and codes – Variables and datasets – Reporting programs (listings, tabulations) And add – Consistent clinical requirements – Integrated operational practices – Transparency between functional areas For all data in all studies across all therapeutic areas 12

13. Quality is about the Data and the Process

14. Agenda Quality Standards and Quality Data Lifecycle Plans – Structure – Implementation – Impact Regulatory’s Role 14

15. Data Lifecycle Plan ® A Data Lifecycle Plan ® is a centrally accessible, study-independent, software-independent document that defines in non-technical language the data structures and associated processes from protocol definition through clinical study reports for a given data domain – Adverse events – Labs – Pain Intensity Scale – Etc. 15

16. Data Lifecycle Plan ® Structure 16 Regulatory Requirements Data Capture CRF/eCRF Design CRF/eCRF Annotations Completion Instructions Data Review Data Review Listings Monitoring GLs Electronic Edit Checks Safety Monitoring Dataset Structure Data Capture Data Reporting Derivations Data Displays Data Listings Summary Tabulations Graphics Stats Considerations What to define in the SAP & relevant assumptions Clinical Study Report How data will be presented and discussed Protocol How the data should be described Purpose Why and how are the data used References Published Key Decisions

17. Implementing DLPs Step 1: Develop/customize DLPs (cross- functional) Step 2: Build functional libraries and templates Step3: Use DLPs and libraries in studies 17

18. DLPs in the Process Protocol Synopsis Develop protocol Develop CRF Develop monitoring GLs Develop edit checks Develop database First subject in/Last subject out Generate TLG Run analysesWrite reportDevelop ISS/ISE Assemble submission 18

19. DLPs in the Process Protocol Synopsis Develop protocol Develop CRF Develop monitoring GLs Develop edit checks Develop database First subject in/Last subject out Generate TLG Run analysesWrite reportDevelop ISS/ISE Assemble submission 19 Select/develop DLPs Regulatory sits on this team

20. DLPs in the Process Protocol Synopsis Develop protocol Develop CRF Develop monitoring GLs Develop edit checks Develop database First subject in/Last subject out Generate TLG Run analysesWrite reportDevelop ISS/ISE Assemble submission 20 DLPs

21. Benefits of the DLP Approach All the usual benefits of standardization, plus: – Promote planning up-front, reducing rework – Collect the right data to answer the right questions – Document assumptions and requirements (internal and outsourced) – Reduce cross-departmental misunderstandings – Determine how good is good enough – Improve poolability of studies – Capture institutional knowledge 21