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P206: Program Level Data Strategy: Standardization of the Data Collection for an Integrated Submission at a Program Level





Poster Presenter

      Ryan Naville-Cook

      • Data Strategist
      • Eli Lilly and Company
        United States

Objectives

The objective of this thought leader poster is to provide a more cogent and cohesive approach to implementing data collection strategies at the program level instead of identifying at the trial level.

Method

Use of two key processes to demonstrate the benefit of program data collection and delivery with the use of data delivery plan and critical data and process tool. Co-Author: Andrea Milner, Sr. Principal Data Strategist, Lilly

Results

Data Collection: With the implementation of a program level strategy, consistency is documented across data collection (CRFs, eCOA, Devices, Biomarkers, Central Laboratory, and PK/PD), data cleaning, SDTM datasets, and summary of that data in the CSR for each respective study. This streamlines strategy, reduces rework, and ensures key study members on the program level are aligned. This helps facilitate confidence and data integrity across each study teams during study build and execution. Submission: The implementation of this strategy allows for program teams to provide cohesive responses to regulators either in key meetings, for example, End of Phase 2, or during inspections. This helps facilitate learning across the program ensures mitigation strategies are in place if issues arise in one study and can be replicated across the program. This eliminates each study working in a vacuum and causing a potential delay in submission to a regulatory agency.

Conclusion

This approach allows for a more nuanced method of data collection that is often at the trial level. It helps bridge the gaps between study teams and differing opinions on what data is critical for the success of each trial in a program and provides a mechanism for learnings, as well as for the study team to be informed regarding the collection and integrity of this data. Additional Author: Andrea Milner

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