An Open-Source R Package for Detection of Adverse Events Under-Reporting in Clinical Trials: Implementation and...

Area of Science:

• Clinical Trials Methodology
• Pharmacovigilance
• Data Science in Healthcare

Background:

• Accurate adverse event (AE) reporting is vital for clinical trial integrity and patient safety.
• AE under-reporting persists as a significant challenge, frequently identified during Good Clinical Practice (GCP) audits.
• Current methods like manual source data verification (SDV) have limitations in detecting AE under-reporting efficiently.

Purpose of the Study:

• To introduce and validate the open-source R package, simaerep, for detecting AE under-reporting.
• To assess the package's capability for rapid, comprehensive, and near-real-time AE under-reporting detection at the site level.
• To evaluate simaerep's performance against traditional methods and heuristic approaches.

Main Methods:

• Development of the open-source R package, simaerep, utilizing patient-level AE and visit data.
• Independent validation of the simaerep package by three member companies of the IntercoMPany quALity Analytics (IMPALA) consortium.
• Comparative analysis of simaerep's detection rates against heuristic methods.

Main Results:

• simaerep consistently and effectively identified AE under-reporting across all three validating companies.
• The package demonstrated superior detection rates compared to heuristic methods.
• simaerep identified 50% of all detectable sites by the 25% mark of the study duration.

Conclusions:

• The simaerep R package provides a robust solution for identifying AE under-reporting in clinical trials.
• Its integration into audits facilitates fast, holistic, and repeatable quality oversight.
• The tool significantly improves the ability to ensure data integrity and patient safety through timely AE detection.