© CDISC 2014 Wayne R. Kubick CDISC CTO 1 What’s New with CDISC.
CDISC Implementation Strategies: Lessons Learned & Future Directions MBC Biostats & Data Management...
-
Upload
alvin-manning -
Category
Documents
-
view
213 -
download
0
Transcript of CDISC Implementation Strategies: Lessons Learned & Future Directions MBC Biostats & Data Management...
CDISC Implementation Strategies: Lessons Learned & Future DirectionsMBC Biostats & Data Management Committee
12 March 2008Kathleen Greene & A. Brooke Hinkson,BioMedical Operations, Genzyme Corporation
Agenda Take you on a journey through time to reflect on
Genzyme’s CDISC implementation strategies We will travel
Back in time to “The Past” Through “The Present” Into “The Future”
Questions & Comments
Introduction to SDTM Submissions
Outsourced end stage SDTM conversion submitted to FDA
Data Operations Submission Datasets: SDTM-like datasets
Create SDTM-like datasets from raw EDC data Data collection: Define new CRF standards
Incorporate SDTM variables into eCRFs
July 2005 First SDTM Submission Motive: Desire to comply with eCTD
Guidance Timeline: September 04 – April 2005
Provided listing CRTs and SDTM datasets to FDA SDTM datasets & define.xml never used by FDA
reviewers Outsourced:
Performed end-stage conversion (mapping & creation of SDTM datasets)
Created define.xml & annotated CRFs Scope: 41 domains; 28 SUPPQUALS
Submissions Lessons Learned First SDTM submission effort required significant
amount of unanticipated Genzyme effort Valuable lessons regarding implementing SDTM
Detailed knowledge of the study data Mapping exercise required cross functional team
Interpretation of standard There are implementation choices; no universal way all
companies should implement SDTM
Implementation standards Genzyme needs to create implementation standards and
governance of the standards
SDTM Flight Attempts Submission datasets:
• Create SDTM-like datasets from raw EDC dataData collection: Define new CRF standards
•Incorporate SDTM variables into CRFs
Why Attempts FizzledDatasets Initiative must be cross-functional
Change cannot be made in isolation; must have up and downstream agreement on new processes and deliverables
Did not have infrastructure to work with fully compliant SDTM datasets Conflicts with project timelines
Data Collection Competing with other initiatives
New version of Clintrial, EDC implementations, M&A’s Push submission requirements upstream
ODM Experiences Electronic Submissions
define.xml: submit case report tabulation metadata to FDA
Metadata Driven Study Authoring Begin establishing libraries of proprietary
and non-proprietary eCRFs Create vendor extensions to ODM
Generate visualizations that mirror EDCvendor’s application user interface & functionality
Import Genzyme defined ODM into vendorstudy architect tools
ODM Lessons Learned Metadata Driven Study Authoring
Make decisions regarding horizontal/vertical specifications
Successfully exchange study metadata (forms and workflow) with EDC vendors
Need infrastructure to successfully utilize tool Limited reusability of individual study CRF builds
across programs Not just anyone should define studies using the tool
Study modeler should have strong understanding of database design and CDISC SDTM & ODM
May 2007 Second SDTM Submission Motive: FDA requested SDTM for all domains
Jan 07 negotiated DM, AE & all SUPPQUALS Timeline: October 2006 – March 2007
Provided listing CRTs, CSR and CRFs to FDA in March May provided DM, AE, SUPPQUALS, define.xml and
annotated CRFs Descriptive documentation of our mapping process
SDTM datasets and define.xml were used by FDA medical reviewer for safety review
Outsourced: Performed end stage conversion (mapping & creation of
SDTM datasets) Created define.xml
Scope: 2 domains & 2 SUPPQUALS
Lessons Learned FDA requesting SDTM now!! Applied lessons learned from 1st experience to 2nd
project Weekly cross-functional meeting with vendor
Output failed WebSDM validation Validation failures identified at Genzyme We need to incorporate our submission requirements
upstream in data collection Not efficient implementation strategy to convert data to
SDTM so late in the clinical data lifecycle Creating extra work for stat. programming, stats. and esub
End stage conversion is expensive!!
CDISC Roadmap Purpose To present a clear and complete picture of:
Where CDISC standards fit into the entire clinical data lifecycle
What activities must occur to integrate the standards into the processes and sub-processes within each lifecycle stage
Provide a common language and reference for further dialog, planning, design, and implementation of CDISC Standards.
Series of Initiatives Build a Complete CDISC Standards ImplementationData Flow #1: Late-Stage
Conversion
Data Flow #2: Mid-Stage Conversion
Data Flow #4: CDISC Standards inin Trial Design
Provide SDTM data to FDA
Data Flow #3: Standards in Collection,Processing & Storage
Submit (as SDTM) the collected data on which analysis is based
Collect, process & store data according to standards
Extend standards-based metadata-driven data flow further upstream into trial design
Data Flow Strategy Meet regulatory current requests and soon-
to-be requirements as soon as possible Integrate CDISC standards more broadly and
deeply into business processes Develop clinical data based upon CDISC
standards instead of converting the data to CDISC standards
Fully gain operational efficiencies from the use of standards
Metadata Repository Currently being defined Manage data about the data Serves as a central hub for automation of upstream
and downstream processes and tools i.e. protocol & CRF development, SAS TLF programming
Enforces standards Improves efficiency of process flow Enables reusability
Data Standards Team & Governance Data Standards Team is essential to
successfully implement CDISC standards Data Standards Team will develop,
implement, maintain, educate, communicate and govern the standards globally Standards cannot be viewed as optional
Implementation of data standards includes process changes, technology modifications and more subject matter expertise
Triage Team Charter An interim committee to provide guidance
and support to a select number of studies for mapping & programming SDTM datasets Focus on end & mid-stage conversion activities Will not be involved with attempts to implement
standards at the protocol, CRF or database design lifecycle phases
Will be replaced by the global cross-functional governance body implemented as part of CDISC Roadmap Project
Triage InitiativeTriage Team*
Stat Programming (4) Data Management (4) CDDS (4)Biostats (4)
Project Specialists (1)
Initiative began Q4 07 will go through 2008 Completed 2 reviews so far Anticipate conducting 10 reviews in Q2 & Q3, with
additional studies to be determined in Q4 Currently considering expanding scope to include
review of CRF and database design
*Include Clinical, Coding, IT & RA as needed
Triage Lessons Learned Process works!
Highlights importance of cross-functional communication
Need additional cross-functional resources to support initiative
Need to operationalize training for new projects going through triage reviews
Implementation questions: obtain outside guidance, when needed
Parallel Efforts Converge
2008
Phase I: CDISC Roadmap
Phase II: Design Phase
CRF Standards
Triage Review
Phase III: Implementation
Participation in Standards Activities CDASH HIMSS SDTM Device Sub Team ADaM Working Group WebSDM User Group FDA ODM Pilot HL7 (Q2 2008) CDISC User Networks (BACUN)
Future Environment Visions is evolving Established standards and governance Adoption of a growing list of commercially
available standards based products Process improvements enabled by
technological advances Technological and operational infrastructure
to support a metadata driven end-to-end clinical data lifecycle
Sample Future Capabilities Ability to collect, store, analyze/report,
compile/submit data to FDA according to SDTM, in conjunction with other CDISC standards
Ability to integrate other, non-CDISC, standards ODM XML based interchanges of clinical data with
vendors (i.e. EDC vendors, labs, FDA, etc.) Metadata based protocol writing tools that establish
the framework for collection, analysis & reporting at the inception of the study design