Risk-Based Fraud Detection: How Centralized Monitoring Can Boost Data Quality

This webinar presents the results of a new study that further demonstrated that risk-based monitoring is an efficient tool to detect fraud at clinical trial sites and early identify data quality and safety issues in clinical trials.
More than 7,000 patients across 60 sites and 13 countries took part in the Second European Stroke Prevention Study (ESPS2) in the early 1990s. The international, multisite, randomized, double-blind trial compared acetylsalicylic acid and/or dipyridamole to matching placebos to prevent stroke or death in patients with pre-existing ischemic cerebrovascular disease. Severe inconsistencies in the case report forms (CRF) at one site led the trial’s steering committee to question the data’s reliability. A for-cause analysis of quality control samples and extensive additional analyses, including blood concentrations of the investigational drugs, showed the patients had never received the protocol medications.
CluePoints’ advanced SMARTTM engine, a set of advanced statistical methods, was applied to the completed study to identify unusual patterns at sites. The CluePoints data analyst performing the analysis reminded blinded regarding the site that committed fraud. The SMARTTM engine was able to detect the fraudulent site by applying an unsupervised analysis to all clinical variables. The fraudulent site ranked second among all sites demonstrating the power of applying central statistical monitoring to detect fraud at site.
KEY TAKEAWAYS:
- Central Statistical Monitoring is an efficient tool to monitor clinical trials and identify quality and safety issues at sites
- In extreme cases, Central Statistical Monitoring is an efficient tool to unreveal fraud at site
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[@stephenmccawille9403]

very interesting technology