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 ➡️
关于作者
Erin Erginer, Director of Product
具有 20 年临床研究和医疗保健经验的创新领导者，专注于临床生物样本和数字化健康评估数据的获取、管理和转换。为制药行业提供技术化解决方案的协作创造者。
