Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/35774
Title: Detection of Fraud in a Clinical Trial Using Unsupervised Statistical Monitoring
Authors: de Viron, Sylviane
Trotta, Laura
Schumacher, Helmut
Lomp, Hans-Juergen
Hoppner, Sebastiaan
Young, Steve
BUYSE, Marc 
Issue Date: 2022
Publisher: SPRINGER HEIDELBERG
Source: Therapeutic Innovation and Regulatory Science, 56, p. 130-136
Abstract: Background A central statistical assessment of the quality of data collected in clinical trials can improve the quality and efficiency of sponsor oversight of clinical investigations. Material and Methods The database of a large randomized clinical trial with known fraud was reanalyzed with a view to identifying, using only statistical monitoring techniques, the center where fraud had been confirmed. The analysis was conducted with an unsupervised statistical monitoring software using mixed-effects statistical models. The statistical analyst was unaware of the location, nature, and extent of the fraud. Results Five centers were detected as atypical, including the center with known fraud (which was ranked 2). An incremental analysis showed that the center with known fraud could have been detected after only 25% of its data had been reported. Conclusion An unsupervised approach to central monitoring, using mixed-effects statistical models, is effective at detecting centers with fraud or other data anomalies in clinical trials.
Notes: De Viron, S (corresponding author), CluePoints SA, Ave Albert Einstein,2a, B-1348 Louvain La Neuve, Belgium.
sylviane.deviron@CluePoints.com
Keywords: Statistical monitoring;Central monitoring;Risk-based monitoring;Fraud;Misconduct
Document URI: http://hdl.handle.net/1942/35774
ISSN: 2168-4790
e-ISSN: 2168-4804
DOI: 10.1007/s43441-021-00341-5
ISI #: WOS:000701359000001
Rights: The Author(s) 2021. Open Access. 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/.
Category: A1
Type: Journal Contribution
Validations: ecoom 2023
Appears in Collections:Research publications

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