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. To address this, we aim to develop an analytics approach that can facilitate rapid, comprehensive, and near-real-time detection of AE under-reporting and over-reporting at each clinical trial site.

Goal

  • To develop a statistical analytics package that detects under-reporting and over-reporting of a range of clinical events at each clinical trial site.
  • To validate the package by piloting it at member companies and explain its utility

Expected Outcomes

  • Open-source r package that is ready for use by the industry
  • White paper sharing the results of piloting the package by member companies and its utility
  • Webinar demonstrating the use of the package

Leads

Resources

Clinical Safety Reporting Package

simaerep icon / logoAccurate 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 was developed to facilitate rapid, comprehensive, and near-real-time detection of AE under-reporting at each clinical trial site. In the v0.5.0 update released in early 2024, the package was updated to also detect over-reporting. This package leverages patient-level AE and visit data for its analyses. The open-source package can be embedded into audits to enable fast, holistic, and repeatable quality oversight of clinical trials. With the v0.6.0 update in late 2024, the package also supports in-database processing, enabling seamless scaling with enterprise IT infrastructure. The statistical probability of a site under-reporting adverse events can be used to manage, target and focus quality assurance activities.

is available publicly on github (https://openpharma.github.io/simaerep/). It follows general good practices and standards for R packages and has a high unit test coverage which is tested by an automated pipeline which creates a validation report that is attached to the latest release.

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