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Implementation of a Validated Statistical Computing Environment Presented by Jeff Schumack, Associate Director – Drug Development Information September.

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Presentation on theme: "Implementation of a Validated Statistical Computing Environment Presented by Jeff Schumack, Associate Director – Drug Development Information September."— Presentation transcript:

1 Implementation of a Validated Statistical Computing Environment Presented by Jeff Schumack, Associate Director – Drug Development Information September 16, 2005

2 2 Statistical Computing Environment (SCE) Data Repository of source code, input data & output Version controlled programs and output Reusable programming ability Links to Clinical Data Management System (CDMS) & Documentum

3 3 Prototype Determined requirements Performed market search Worked collaboratively to achieve our vision Conducted extensive pilot

4 4 SCE: Where Does it Fit in? Capture and Organize Data Analyze Data, Generate and Deliver Reports Clintrial/CRO SAS, Files Documentum/Publishing Clinical Data Management Clinical BiostatisticsRegulatory Affairs Statistical Computing Environment Analysis, Programming and Reporting under 21 CFR Part 11 compliance Submission of Reports to the FDA

5 5 Interfaces Clinical Data Management Core SCE Documentum

6 6 Clinical Data Management Connector

7 7 Extraction Process Query CDMS SAS or XML Execute data extraction –Extraction in context –Record data and output under correct study

8 8 Audit Trails Who What When Comment if any

9 9 Core SCE Functionality

10 10 Database Connectivity Oracle Role Based Security Require username / password Secure – only authorized personnel allowed to connect Level of access based on user privilege

11 11 External Applications SAS system version 8.12 (or higher) R (Splus) version or higher UltraEdit version 10 or higher

12 12 Repository Secure Oracle Database Easily retrievable Hierarchical Structure –Indication -> Study -> Programs (etc.)

13 13 Traceable Pathway SCE provides Clear pathway from field to report Mechanism for data extraction of analyses Dependency map of input-to-programs-to-output Push analyses into Documentum Audit Trails

14 14 Dependency Map Graphically represent program-to-input-to-output – Including version numbers

15 15 Security Manager Individual user accounts Password Management: LDAP Permissions: –Group –User –Project

16 16 File Management Folder types / File types / File properties Check out / Check in File annotations Source Code Editor File Viewers Execution and display results, logs, graphics Audit Trails

17 17 Pack & Go Pull files from SCE onto local disks –Indication –Study –Analysis Useful for Archival Collaboration with external contributors –Partners –CROs –Data Monitoring Committees

18 18 Study Setup - Templates Nested Hierarchy templates –Indication –Study –Analyses Replicate Studies using templates Retire templates no longer in use

19 19 Job Management Server based running of jobs Define jobs / Submit jobs / Run jobs / Run & Record Input / output dependencies Table of Content user interface / comment blocks Relative file pathways –Macro search –Common Code search Batch execution / job sequencing Record Outputs Traceability – Dependency Mapping –Input data / program / macros -> main program -> Output (s)

20 20 Life Cycle Management Draft / Quality Controlled / Final Business rules Draft from Final

21 21 Reporting Audit Trail reports Output formats – PDF, XML, RTF etc. Combine Outputs Interface with external reporting tools

22 22 Documentum Connector

23 23 Documentum Authentication Username / password protected Documentum user information verification Documentum permissions verification

24 24 User Interface Automatic population of Documentum based on lifecycle state of document in SCE Manual population based on user triggers User comments Version controls

25 25 Audit Trails Who What When Version control information

26 26 Conclusion New technology Close to realizing HGS SCE vision Acknowledgments: –HGS Biostats & IT teams –Waban Software –Alan Hopkins Contact Information: –


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