Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/42483
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dc.contributor.authorde Viron, Sylviane-
dc.contributor.authorTrotta, Laura-
dc.contributor.authorSteijn, William-
dc.contributor.authorYoung, Steve-
dc.contributor.authorBUYSE, Marc-
dc.date.accessioned2024-02-27T13:38:06Z-
dc.date.available2024-02-27T13:38:06Z-
dc.date.issued2024-
dc.date.submitted2024-02-27T13:23:38Z-
dc.identifier.citationTherapeutic Innovation & Regulatory Science,-
dc.identifier.urihttp://hdl.handle.net/1942/42483-
dc.description.abstractBackgroundCentral monitoring aims at improving the quality of clinical research by pro-actively identifying risks and remediating emerging issues in the conduct of a clinical trial that may have an adverse impact on patient safety and/or the reliability of trial results. This paper, focusing on statistical data monitoring (SDM), is the second of a series that attempts to quantify the impact of central monitoring in clinical trials.Material and MethodsQuality improvement was assessed in studies using SDM from a single large central monitoring platform. The analysis focused on a total of 1111 sites that were identified as at-risk by the SDM tests and for which the study teams conducted a follow-up investigation. These sites were taken from 159 studies conducted by 23 different clinical development organizations (including both sponsor companies and contract research organizations). Two quality improvement metrics were assessed for each selected site, one based on a site data inconsistency score (DIS, overall -log10P-value of the site compared with all other sites) and the other based on the observed metric value associated with each risk signal.ResultsThe SDM quality metrics showed improvement in 83% (95% CI, 80-85%) of the sites across therapeutic areas and study phases (primarily phases 2 and 3). In contrast, only 56% (95% CI, 41-70%) of sites showed improvement in 2 historical studies that did not use SDM during study conduct.ConclusionThe results of this analysis provide clear quantitative evidence supporting the hypothesis that the use of SDM in central monitoring is leading to improved quality in clinical trial conduct and associated data across participating sites.-
dc.description.sponsorshipFunding This research received no funding other than from the authors’ companies. The data analyzed in this paper were generated by CluePoints’ risk-based quality management (RBQM) platform.-
dc.language.isoen-
dc.publisherSPRINGER HEIDELBERG-
dc.rightsThe Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.-
dc.subject.otherStatistical monitoring-
dc.subject.otherCentral monitoring-
dc.subject.otherRisk-based quality management-
dc.subject.otherRisk-based monitoring-
dc.subject.otherRBM-
dc.subject.otherRBQM-
dc.subject.otherClinical trial quality-
dc.subject.otherData quality assessment-
dc.subject.otherSite performance-
dc.titleDoes Central Statistical Monitoring Improve Data Quality? An Analysis of 1,111 Sites in 159 Clinical Trials-
dc.typeJournal Contribution-
local.format.pages12-
local.bibliographicCitation.jcatA1-
dc.description.notesde Viron, S (corresponding author), CluePoints SA, Ave Albert Einstein 2a, B-1348 Louvain La Neuve, Belgium.-
dc.description.notessylviane.deviron@CluePoints.com-
local.publisher.placeTIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY-
local.type.refereedRefereed-
local.type.specifiedArticle-
local.bibliographicCitation.statusEarly view-
dc.identifier.doi10.1007/s43441-024-00613-w-
dc.identifier.pmid38334868-
dc.identifier.isi001159542000001-
local.provider.typewosris-
local.description.affiliation[de Viron, Sylviane; Trotta, Laura; Steijn, William; Buyse, Marc] CluePoints SA, Ave Albert Einstein 2a, B-1348 Louvain La Neuve, Belgium.-
local.description.affiliation[Young, Steve] CluePoints Inc, King Of Prussia, PA USA.-
local.description.affiliation[Buyse, Marc] Int Drug Dev Inst IDDI, Louvain La Neuve, Belgium.-
local.description.affiliation[Buyse, Marc] Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat I BioSta, Hasselt, Belgium.-
local.uhasselt.internationalyes-
item.fullcitationde Viron, Sylviane; Trotta, Laura; Steijn, William; Young, Steve & BUYSE, Marc (2024) Does Central Statistical Monitoring Improve Data Quality? An Analysis of 1,111 Sites in 159 Clinical Trials. In: Therapeutic Innovation & Regulatory Science,.-
item.fulltextWith Fulltext-
item.accessRightsOpen Access-
item.contributorde Viron, Sylviane-
item.contributorTrotta, Laura-
item.contributorSteijn, William-
item.contributorYoung, Steve-
item.contributorBUYSE, Marc-
crisitem.journal.issn2168-4790-
crisitem.journal.eissn2168-4804-
Appears in Collections:Research publications
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