An Open-Source R Package for Detection of Adverse Events Under-Reporting in Clinical Trials: Implementation and Validation by the IMPALA (Inter coMPany quALity Analytics) Consortium.
Björn KoneswarakanthaRonojit AdyanthayaJennifer EmersonFrederik CollinAnnett KellerMichaela MattheusIoannis SpyroglouSandra DonevskaTimothé Ménardnull nullPublished in: Therapeutic innovation & regulatory science (2024)
Accurate and timely reporting of adverse events (AEs) in clinical trials is crucial to ensuring data integrity and patient safety. However, AE under-reporting remains a challenge, often highlighted in Good Clinical Practice (GCP) audits and inspections. Traditional detection methods, such as on-site investigator audits via manual source data verification (SDV), have limitations. Addressing this, the open-source R package {simaerep} was developed to facilitate rapid, comprehensive, and near-real-time detection of AE under-reporting at each clinical trial site. This package leverages patient-level AE and visit data for its analyses. To validate its efficacy, three member companies from the Inter coMPany quALity Analytics (IMPALA) consortium independently assessed the package. Results showed that {simaerep} consistently and effectively identified AE under-reporting across all three companies, particularly when there were significant differences in AE rates between compliant and non-compliant sites. Furthermore, {simaerep}'s detection rates surpassed heuristic methods, and it identified 50% of all detectable sites as early as 25% into the designated study duration. The open-source package can be embedded into audits to enable fast, holistic, and repeatable quality oversight of clinical trials.
Keyphrases
- clinical trial
- loop mediated isothermal amplification
- patient safety
- big data
- adverse drug
- quality improvement
- electronic health record
- real time pcr
- label free
- clinical practice
- phase ii
- healthcare
- open label
- emergency department
- machine learning
- primary care
- artificial intelligence
- double blind
- case report
- randomized controlled trial
- deep learning
- drug induced