Data Interoperability in COVID-19 Vaccine Trials: Methodological Approach in the VACCELERATE Project

Area of Science:

• Clinical research
• Data science
• Vaccinology

Background:

• Data standards are essential for efficient data processing and interoperability in clinical trials.
• Structured data facilitates analysis, reduces cleaning efforts, and enables secondary data use.
• A common language and shared expectations improve system and device interoperability.

Purpose of the Study:

• Identify commonalities and differences in clinical trial metadata, protocols, and data collection within the VACCELERATE project.
• Assess the achieved interoperability and suggest methodological improvements.
• Evaluate the impact of data standards on clinical trial processes.

Main Methods:

• Interoperable points were identified based on core outcome areas: immunogenicity, safety, and efficacy.
• Compared 3 VACCELERATE clinical trial protocols against a master protocol template.
• Analyzed metadata and conducted a questionnaire survey on data management systems and structures.

Main Results:

• Noncommonalities in protocols and metadata were linked to population differences, protocol design, and vaccination patterns.
• Detailed metadata followed internal standards and the Clinical Data Acquisition Standards Harmonisation (CDASH) approach.
• A single data management provider streamlined database development, ensuring uniformity and secure data transfer.

Conclusions:

• Master protocols significantly improve trial operational efficiency and data interoperability when uniform infrastructure and data management are used.
• Shared data must be structured, described, formatted, and stored using recognized standards for better interpretation and analysis.
• Adherence to widely recognized data and metadata standards is key to enhancing data interoperability and facilitating research.