The Guardian Research Network: A Real-World Data Source for Pharmacoepidemiologic Research and Regulatory...

Area of Science:

• Real-world data (RWD) quality assessment
• Regulatory science
• Health informatics

Background:

• Real-world data (RWD) quality is paramount for generating reliable real-world evidence (RWE) for regulatory submissions.
• Data owners must document data quality assessments for regulators.
• Assessing data reliability, including its journey, is as critical as its relevance for valid conclusions.

Purpose of the Study:

• To replicate the transparent reporting of data quality attributes for the Guardian Research Network (GRN) RWD source.
• To characterize the quality attributes of GRN, a database of aggregated electronic health records (EHRs).
• To align GRN's data quality framework with established guidelines.

Main Methods:

• Described GRN data elements, timeliness, representativeness, and access.
• Detailed methods for ensuring and assessing data reliability (accuracy, traceability, timeliness, completeness) and relevance (availability, sufficiency, representativeness).
• Provided examples of data quality checks and their outcomes within GRN.

Main Results:

• GRN employs structured processes to ensure data reliability and relevance, consistent with published guidelines.
• Specific data quality checks were applied and their outcomes documented for GRN data.
• The study demonstrated a systematic approach to RWD quality assurance.

Conclusions:

• Documenting and communicating RWD quality attributes is vital for regulatory use.
• Transparent reporting enhances feasibility assessments and builds regulator confidence in RWE.
• Structured approaches are essential for identifying regulatory-ready data sources.