Detection of Fraud in a Clinical Trial Using Unsupervised Statistical Monitoring
| Autor: | Hans-Juergen Lomp, Sylviane de Viron, Laura Trotta, Marc Buyse, Sebastiaan Höppner, Steve Young, Helmut Schumacher |
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| Přispěvatelé: | de Viron, Sylviane, Trotta, Laura, Schumacher, Helmut, Lomp, Hans-Juergen, Hoppner, Sebastiaan, Young, Steve, BUYSE, Marc |
| Rok vydání: | 2021 |
| Předmět: |
Fraud
Misconduct Central monitoring Models Statistical Computer science Public Health Environmental and Occupational Health Statistical model Statistical monitoring Risk-based monitoring computer.software_genre humanities law.invention Clinical trial Randomized controlled trial law Pharmacology (medical) Data mining Pharmacology Toxicology and Pharmaceutics (miscellaneous) computer health care economics and organizations Original Research |
| Zdroj: | Therapeutic Innovation & Regulatory Science |
| ISSN: | 2168-4804 2168-4790 |
| DOI: | 10.1007/s43441-021-00341-5 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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