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Implementing CDISC Standards for Efficient Data Collection and Reporting

Learn how to implement CDISC standards to streamline your data collection processes from capture to reporting, with a focus on challenges, risks, efficiency, and cost reduction.

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Implementing CDISC Standards for Efficient Data Collection and Reporting

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Presentation Transcript

  1. Communicating with StandardsKeeping it Simple Pamela Ryley Vertex Pharmaceuticals, Inc. September 29, 2006

  2. Implementing CDISC • Why? • What Point in Study to apply CDISC • Preparation for submission • Database extraction • Data collection / Database creation • STDM – Okay What about the rest of CDISC? Trial Design, ADaM, LAB Model

  3. Concurrent Changes • New Electronic Data Capture system • New Electronic Reporting and Repository system • New Standards - CDISC • Opportunity to determine where to implement CDISC • Push CDISC strategy back to data collection – data collection items being redefined anyway.

  4. Implementing STDM in Data Collection • Identifying and implementing the use of one to one matches • Implementing logical naming and structure where one to one matches not possible. • Identifying limitations of chosen EDC database and preparing for those.

  5. Implementing STDM in Data Collection Collection variables to reporting variables • eCRF Data • Controlled terminology • Electronically loaded data Efficiency and Reduced Cost

  6. Implementing STDM in Data Collection • Close collaboration between Data Management, Statisticians and Statistical Programming to streamline processing from data capture to reporting. • Education and acceptance of other groups. • Recognition of need and willingness to replace earlier standards

  7. Challenges of Implementing STDM in Data Collection Items without a one to one correlation • Variables that require transposition • Items collected for operational use • Greater detail than required by CDISC • Submissions to other agencies • Exploratory use or Publication • Items of Operational Value

  8. Extending the Implementation Legacy data Mapping • Variables • One to many • Many to One • Controlled Terminology • Add Trial Design Datasets

  9. Extending the Implementation • Increases ability to easily combine data across protocols early in compound development. • Creation of updated set of standard programs that take advantage of features in SAS version 9.

  10. Confirming Structure General Conformance • SAS v9 Proc CDISC • Web SDM Combining data across protocols • Combining legacy and current data • Data as required • Structure & Content

  11. Programming • Benefits of Pushing CDISC standards to data collection • Minimize preparation of submission datasets • Time • Resources used for more valuable endeavors • Consistency & Transparency

  12. Risks of Implementing CDISC in Data Capture • Changes to CDISC • New domains • Modifications to Draft Domains • Modifications to controlled terminology • Broader Limitations • SAS V5 Transport files  xml files • 8 character limitations on numerous variables • Changes to Trial Design, ADaM, LAB Model

  13. Challenges • Trial Design Datasets • Reserved domains • Determining when to use supplemental qualifiers versus creating new domains • Complex Design of Protocols – difficult to fit into CDISC standards • No single source for answers to questions. We do have user groups

  14. Thank You!

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