Master best practices for managing non-CRF data across clinical trials
Discover best practices for collecting, standardizing and integrating non-CRF data. If you’re involved in clinical data management, don’t miss this opportunity to stay ahead of the growing complexity when handling non-CRF data.
Why this guide is a must-have
💪 Teaches you how to tackle growing data complexity
⚠️ Learn how to reduce risk & rework to prevent downstream delays
🚀 A key step in understanding how to gain submission-ready data from the outset
The top 3 critical failures in managing non-CRF data are:
- Lack of early planning and governance (causing data gaps & inconsistencies)
- Poor integration with CRF and SDTM workflows
- Lack of traceability and risk of non-compliance due to inaccurate data
This guide addresses these knowledge gaps and puts you in the strongest position for successfully managing non-CRF data. Simply complete the form to download your guide.
What you’ll learn in this guide
- Practical guidance on managing diverse non-CRF data sources (e.g. labs, imaging, devices etc)
- How to identify, classify, and plan for non-CRF data sources early in the study lifecycle
- How to align non-CRF data with CDISC standards and regulatory expectations from the outset
- Best practices for vendor data management, governance and oversight
- Strategies for standardizing non-CRF data to support SDTM mapping and submission readiness
- How to improve data quality, traceability, and compliance across data streams
Submit the form now to get your hands on this essential guide ➡️
About the author
Erin Erginer, Director of Product
Innovative leader with 20 years of clinical research and healthcare experience, specializing in acquisition, management, and transformation of clinical biospecimen and digital health assessment data. Collaborative creator of tech-enabled solutions for the pharmaceutical industry.
