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
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作者:
Koneswarakantha, Bjoern
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F Hoffmann La Roche & Cie AG, CH-4070 Basel, SwitzerlandF Hoffmann La Roche & Cie AG, CH-4070 Basel, Switzerland
Koneswarakantha, Bjoern
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Adyanthaya, Ronojit
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Merck & Co Inc, Rahway, NJ 07065 USAF Hoffmann La Roche & Cie AG, CH-4070 Basel, Switzerland
Adyanthaya, Ronojit
[2
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Emerson, Jennifer
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Boehringer Ingelheim Pharma GmbH & Co KG, D-88397 Biberach, GermanyF Hoffmann La Roche & Cie AG, CH-4070 Basel, Switzerland
Emerson, Jennifer
[3
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Collin, Frederik
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Boehringer Ingelheim Pharma GmbH & Co KG, D-88397 Biberach, GermanyF Hoffmann La Roche & Cie AG, CH-4070 Basel, Switzerland
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.