Detection of Fraud in a Clinical Trial Using Unsupervised Statistical Monitoring

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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.

Related collections

Most cited references 17

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European Stroke Prevention Study. 2. Dipyridamole and acetylsalicylic acid in the secondary prevention of stroke.

H.C. Diener, L. Cunha, C. Forbes … (1996)

In 1988, we undertook a randomized, placebo-controlled, double-blind trial to investigate the safety and efficacy of low-dose acetylsalicylic acid (ASA), modified-release dipyridamole, and the two agents in combination for secondary prevention of ischemic stroke. Patients with prior stroke or transient ischemic attack (TIA) were randomized to treatment with ASA alone (50 mg daily), modified-release dipyridamole alone (400 mg daily), the two agents in a combined formulation, or placebo. Primary endpoints were stroke, death, and stroke or death together. TIA and other vascular events were secondary endpoints. Patients were followed on treatment for two years. Data from 6,602 patients were analysed. Factorial analysis demonstrated a highly significant effect for ASA and for dipyridamole in reducing the risk of stroke (p < or = 0.001) and stroke or death combined (p < 0.01). In pairwise comparisons, stroke risk in comparison to placebo was reduced by 18% with ASA alone (p = 0.013); 16% with dipyridamole alone (p = 0.039); and 37% with combination therapy (p < 0.001). Risk of stroke or death was reduced by 13% with ASA alone (p = 0.016); 15% with dipyridamole alone (p = 0.015); and 24% with the combination (p < 0.001). The treatment had no statistically significant effect on the death rate alone. Factorial analysis also demonstrated a highly significant effect of ASA (p < 0.001) and dipyridamole (p < 0.01) for preventing TIA. The risk reduction for the combination was 36% (p < 0.001) in comparison with placebo. Headache was the most common adverse event, occurring more frequently in dipyridamole-treated patients. All-site bleeding and gastrointestinal bleeding were significantly more common in patients who received ASA in comparison to placebo or dipyridamole. We conclude that (1) ASA 25 mg twice daily and dipyridamole, in a modified-release form, at a dose of 200 mg twice daily have each been shown to be equally effective for the secondary prevention of ischemic stroke and TIA; (2) when co-prescribed the protective effects are additive, the combination being significantly more effective than either agent prescribed singly; (3) low-dose ASA does not eliminate the propensity for induced bleeding.

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Data fraud in clinical trials

Stephen L George, Marc Buyse (2015)

Highly publicized cases of fabrication or falsification of data in clinical trials have occurred in recent years and it is likely that there are additional undetected or unreported cases. We review the available evidence on the incidence of data fraud in clinical trials, describe several prominent cases, present information on motivation and contributing factors and discuss cost-effective ways of early detection of data fraud as part of routine central statistical monitoring of data quality. Adoption of these clinical trial monitoring procedures can identify potential data fraud not detected by conventional on-site monitoring and can improve overall data quality.

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A statistical approach to central monitoring of data quality in clinical trials.

Erik Doffagne, Jean-François Beckers, David Venet … (2012)

Classical monitoring approaches rely on extensive on-site visits and source data verification. These activities are associated with high cost and a limited contribution to data quality. Central statistical monitoring is of particular interest to address these shortcomings. This article outlines the principles of central statistical monitoring and the challenges of implementing it in actual trials. A statistical approach to central monitoring is based on a large number of statistical tests performed on all variables collected in the database, in order to identify centers that differ from the others. The tests generate a high-dimensional matrix of p-values, which can be analyzed by statistical methods and bioinformatic tools to identify extreme centers. Results from actual trials are provided to illustrate typical findings that can be expected from a central statistical monitoring approach, which detects abnormal patterns that were not (or could not have been) detected by on-site monitoring. Central statistical monitoring can only address problems present in the data. Important aspects of trial conduct such as a lack of informed consent documentation, for instance, require other approaches. The results provided here are empirical examples from a limited number of studies. Central statistical monitoring can both optimize on-site monitoring and improve data quality and as such provides a cost-effective way of meeting regulatory requirements for clinical trials.

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Author and article information

Contributors

Sylviane de Viron:

ORCID: http://orcid.org/0000-0003-3890-557X

sylviane.deviron@CluePoints.com

Journal

Journal ID (nlm-ta): Ther Innov Regul Sci

Journal ID (iso-abbrev): Ther Innov Regul Sci

Title: Therapeutic Innovation & Regulatory Science

Publisher: Springer International Publishing (Cham )

ISSN (Print): 2168-4790

ISSN (Electronic): 2168-4804

Publication date (Electronic): 29 September 2021

Publication date PMC-release: 29 September 2021

Publication date (Print): 2022

Volume: 56

Issue: 1

Pages: 130-136

Affiliations

[1 ]CluePoints S.A., Avenue Albert Einstein, 2a, 1348 Louvain-la-Neuve, Belgium

[2 ]Statistical Consultant, Ingelheim Am Rhein, Germany

[3 ]GRID grid.420061.1, ISNI 0000 0001 2171 7500, Boehringer Ingelheim, ; Biberach an der Riss, Germany

[4 ]CluePoints Inc., King of Prussia, USA

[5 ]GRID grid.482598.a, International Drug Development Institute (IDDI), ; Louvain-la-Neuve, Belgium

[6 ]GRID grid.12155.32, ISNI 0000 0001 0604 5662, Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), , Hasselt University, ; Hasselt, Belgium

Author information

Sylviane de Viron http://orcid.org/0000-0003-3890-557X

Article

Publisher ID: 341

DOI: 10.1007/s43441-021-00341-5

PMC ID: 8688378

PubMed ID: 34590286

SO-VID: 65726fa5-5af2-4828-9059-4cec17e48152

License:

Open AccessThis 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/.

History

Date received : 7 June 2021

Date accepted : 19 September 2021

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100001003, Boehringer Ingelheim;

Funded by: CluePoints

Custom metadata

Keywords: statistical monitoring,central monitoring,risk-based monitoring,fraud,misconduct

Data availability:

Keywords: statistical monitoring, central monitoring, risk-based monitoring, fraud, misconduct

Detection of Fraud in a Clinical Trial Using Unsupervised Statistical Monitoring

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Abstract

Background

Material and Methods

Results

Conclusion

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Most cited references 17

European Stroke Prevention Study. 2. Dipyridamole and acetylsalicylic acid in the secondary prevention of stroke.

Data fraud in clinical trials

A statistical approach to central monitoring of data quality in clinical trials.

Author and article information

Contributors

Journal

Affiliations

Author information

Article

History

Funding

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Custom metadata

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