Detection of Fraud in a Clinical Trial Using Unsupervised Statistical Monitoring

Author:

de Viron SylvianeORCID,Trotta Laura,Schumacher Helmut,Lomp Hans-Juergen,Höppner Sebastiaan,Young Steve,Buyse Marc

Abstract

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.

Funder

Boehringer Ingelheim

CluePoints

Publisher

Springer Science and Business Media LLC

Subject

Pharmacology (medical),Public Health, Environmental and Occupational Health,Pharmacology, Toxicology and Pharmaceutics (miscellaneous)

Reference24 articles.

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2. US Department of Health and Human Services, Food and Drug Administration. A Risk-Based Approach to Monitoring of Clinical Investigations Questions and Answers [Internet]. U.S. Food and Drug Administration. FDA; 2019. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/risk-based-approach-monitoring-clinical-investigations-questions-and-answers. Accessed 20 Aug 2021

3. Venet D, Doffagne E, Burzykowski T, Beckers F, Tellier Y, Genevois-Marlin E, et al. A statistical approach to central monitoring of data quality in clinical trials. Clin Trials Lond Engl. 2012;9(6):705–13.

4. Pogue JM, Devereaux PJ, Thorlund K, Yusuf S. Central statistical monitoring: detecting fraud in clinical trials. Clin Trials Lond Engl. 2013;10(2):225–35.

5. George SL, Buyse M. Data fraud in clinical trials. Clin Investig. 2015;5(2):161–73.

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