Data Management for Non-Data Managers

Data Management (DM) stands at a critical juncture in clinical studies. As such, understanding the role of DM, the differences in perspective between DM and other functions (clinical, operations, project management, medical, regulatory, safety), and how these differences may shape interactions, will add to improving the communication and collaboration in multi-functional teams, often involving people from different organisations.

This course will lead you step by step through the Data Management workflow in a clinical study, from the preparatory stages of system selection and Data Management Plan writing, over the role of Data Managers during the clinical conduct such as database maintenance, running of validation checks, and management of queries, to the final steps necessary for locking the database and transferring it to statisticians for performing the analysis.

• Overview of Data Management in clinical studies
• Regulatory Considerations
  • DM in the context of GCP, other topics and guidelines
• Data Management Planning and Setup
  • essential DM documents, technology/system selection
• Data Capture
  • EDC systems, ePRO, devices, external data
• (Digression: DM legacy systems and processes)
• Data Validation
  • automated checks and manual reviews, front end versus back end checks, types of discrepancies, query management
• Data Coding
  • dictionary selection, brief discussion of different dictionaries (especially MedDRA)
• (Digression: DM in decentralized and virtual trials)
• Data Reconciliation
  • differences to Data Validation, reconciliation against vigilance data, reconciliation against other external data sources
• Data Review
  • Data Managers in the preparation of and as contributors to (blinded) data review meetings
• Database Lock
  • soft locks and hard locks, unlocking
• Archiving & Documentation

At each point, we will be looking not only at the What and How of Data Management, but also at the Why, at What Can Be Done (and at what cost) and at What Cannot Be Done, and we will highlight points of contact between Data Managers and other roles as well as the different expectations that might lead to misunderstandings and other friction.