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
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 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.
To learn more, go to the following resources:
- Links to the Code repository and documentation.
- 2024 Articles on V.0.6.0 with in-frame version and V0.5.0 with over-reporting update.
- Article on validation in Therapeutic Innovation & Regulatory Science.
- News article with company testimonials.
- View the November 9, 2023 Demonstration Webinar or access the slide presentation.