What is the ultimate goal for every clinical trial? High-Quality Data that proves safety and/or efficacy endpoints while safeguarding clinical trial participants. How can data integrity be ensured? This requires a combination of a few key players during the course of a study.
Clinical data management teams prepare data for statistical analysis with two key objectives in mind:
- Ensure the data is complete
- Ensure the number of errors is low
Companies with increasingly stringent clinical trial timelines are meeting these objectives by recruiting skilled data managers and implementing new technologies. Skilled data management teams use the following methods and technologies to improve data quality:
- Create and implement a comprehensive data management plan and database for a specified protocol.
- Integrate the database with a data capture system.
- Create security systems and audit trails.
- Develop technical quality controls and implement quality assurance systems.
- Compile, clean, code, and validate the data.
- Issue and resolve queries.
- Maintain data back-up and storage facilities.
- Resolve outstanding issues to prepare for database-locks.
Even the best data managers cannot ensure data is complete and error-free. Clinical monitors in the field are needed to improve data quality and protect the human subjects from whom the data came. Monitors meet face-to-face with clinical investigator sites, so they can perform many tasks that data managers cannot:
- Review regulatory documentation to ensure data is obtained from sites that follow Federal regulations, the Investigative plan, Agreements, and IRB requirements (FAIR). For example, a monitor can verify that data is obtained from a qualified individual in a certified lab.
- Review informed consent forms and processes to ensure that data is obtained from properly consented subjects.
- Review medical records to verify that all data matches the source documents.
- Resolve data discrepancies by assisting the site with query resolution.
- Review records to ensure all adverse events are properly documented and reported.
- Look-out for fraudulent data.
- Assist site personnel with data capture and data transfer problems.
- Watch for protocol deviations and help the site prevent future occurrences.
- Keep the site personnel motivated in order to increase subject recruitment and ensure proper data collection.
- Keep the site’s focus on the data and procedures that are related to primary study endpoints.
Collaboration between data management teams and field monitors is needed to data is complete and the number of errors is low. Only high-quality data can prove study endpoints AND protect human subjects.
Many companies have recently incorporated a risk-based monitoring approach. This strategy requires both centralized monitoring and data management, AND monitors in the field. IMARC’s whitepaper, Centralized vs. OnSite Monitoring: A Sponsor’s Balancing Act Applying a Risk-based Approach provides an overview of the approach.
FDA released a guidance document titled, “Guidance for Industry Oversight of Clinical Investigations — A Risk-Based Approach to Monitoring”, and NIH has an article on their website “Data management in clinical research: An overview” which are great resources on this topic.
Do you think that effective data management requires a monitor in the field? How important are skilled data management teams to clinical research? Share your thoughts below!
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