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1000 Titel
  • 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
1000 Autor/in
  1. Koneswarakantha, Björn |
  2. Adyanthaya, Ronojit |
  3. Emerson, Jennifer |
  4. Collin, Frederik |
  5. Keller, Annett |
  6. Mattheus, Michaela |
  7. Spyroglou, Ioannis |
  8. Donevska, Sandra |
  9. Ménard, Timothé |
  10. On behalf of the IMPALA (Inter coMPany quALity Analytics) Consortium |
1000 Verlag Springer International Publishing
1000 Erscheinungsjahr 2024
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2024-04-02
1000 Erschienen in
1000 Quellenangabe
  • 58(4):591-599
1000 Copyrightjahr
  • 2024
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1007/s43441-024-00631-8 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11169048/ |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • <jats:title>Abstract</jats:title><jats:p>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.</jats:p>
1000 Sacherschließung
lokal Adverse Drug Reaction Reporting Systems/standards [MeSH]
lokal GCP
lokal Analytical Report
lokal Drug-Related Side Effects and Adverse Reactions [MeSH]
lokal Validation
lokal Clinical Trials as Topic [MeSH]
lokal Humans [MeSH]
lokal Software [MeSH]
lokal Open-source
lokal Analytics
lokal Transparency
lokal Quality assurance
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://orcid.org/0000-0003-4585-7799|https://frl.publisso.de/adhoc/uri/QWR5YW50aGF5YSwgUm9ub2ppdA==|https://frl.publisso.de/adhoc/uri/RW1lcnNvbiwgSmVubmlmZXI=|https://frl.publisso.de/adhoc/uri/Q29sbGluLCBGcmVkZXJpaw==|https://frl.publisso.de/adhoc/uri/S2VsbGVyLCBBbm5ldHQ=|https://frl.publisso.de/adhoc/uri/TWF0dGhldXMsIE1pY2hhZWxh|https://frl.publisso.de/adhoc/uri/U3B5cm9nbG91LCBJb2Fubmlz|https://frl.publisso.de/adhoc/uri/RG9uZXZza2EsIFNhbmRyYQ==|https://orcid.org/0000-0003-4545-6944|https://frl.publisso.de/adhoc/uri/T24gYmVoYWxmIG9mIHRoZSBJTVBBTEEgKEludGVyIGNvTVBhbnkgcXVBTGl0eSBBbmFseXRpY3MpIENvbnNvcnRpdW0=
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1000 Erstellt am 2025-07-05T15:27:14.759+0200
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1000 Zuletzt bearbeitet 2025-08-11T10:45:15.614+0200
1000 Objekt bearb. Mon Aug 11 10:45:15 CEST 2025
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