Tubestop - Data Management

Description

Clinical trial data management is the process of collecting, cleaning, and managing data from clinical trials to ensure its accuracy and integrity.

Clinical trial data management typically begins with the development of a data management plan.
The data management plan defines the data management process, including the data collection methods, data cleaning procedures, and data storage and security requirements. This includes developing the case record forms and database structures to be used to collect and store data.
Once the data management plan is in place, data is collected from clinical trial research site staff and participants.
The collected data is then cleaned to remove any errors or inconsistencies and stored in a secure database.

DCT-specific Considerations/Inputs

Data collection: Decentralized clinical trials collect data from participants in a variety of ways, including electronic data capture (EDC) systems, remote patient monitoring (RPM) devices, and videoconferencing. Data may also be collected at more locations than in a traditional trial, e.g., at in-home visits, at pharmacies, etc. This requires a more flexible and adaptable data management approach than traditional clinical trials, which typically collect data at research site-based locations.
Data cleaning: There may be more people collecting, entering and answering queries about the clinical trial data than in a traditional trial. The processes for training and managing data clearing need to be well understood by all involved.
Data storage: Decentralized clinical trials may capture data from more locations (e.g., smartphone data entry, clinical site data entry, direct data capture from wearables, etc). The data needs to be stored securely and in a way that allows oversight to all the data by the appropriate study team members (PIs, sponsor team members)
Data security: Decentralized clinical trials may collect sensitive data directly from participants, including patient contact information. This requires more robust data security processes than traditional clinical trials.

Best Practices

Mapping the Patient Journey

Technology and Data Standards

DCT Curriculum

Key Data Management Considerations

eCRF / eCOA / ePRO requirements
Lab / biospecimen data collection requirements
Radiology data collection requirements.
IRF data access and review requirements (if needed)
Drug accountability / patient adherence data requirements
Long term follow up stds (if any) or cross over design data needs
DCT platform configuration/testing standards including plans for limiting data access by role
Monitoring plan including RBQM stds, SDV / SDR needs, Site re-training plan
RACI for monitoring expectations (blinded data role, Site / Sponsor / CRO roles
Data mgt / lock plan, RACI
Statistical QTL
Analysis of datasets per protocol to meet study objectives
Analysis of datasets per protocol to assess participant safety / risk benefit
Clear RACI on where assessments are performed
Tracking (ideally in platform) of which assessments were done where per patient

U.S. FDA Final Guidance Document: Conducting Clinical Trials With Decentralized Elements Guidance for Industry, Investigators, and Other Interested Parties - September 2024

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U.S. FDA Guidance for Industry : Computerized Systems Used in Clinical Investigations

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