Abstract
BackgroundA 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 MethodsThe 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.ResultsFive 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.ConclusionAn unsupervised approach to central monitoring, using mixed-effects statistical models, is effective at detecting centers with fraud or other data anomalies in clinical trials.
Highlights
Risk-based monitoring is a dynamic approach that focuses on oversight activities during the conduct of clinical trials
The Data Quality Assessment (DQA) analysis was performed on the latest database of the ESPS2 trial
B Date tests look at visits on Saturday/Sunday given that the study did not accept visits and/or assessments being performed on such dates size (x-axis) and Data Inconsistency Score” (DIS) (y-axis) of a center
Summary
Risk-based monitoring is a dynamic approach that focuses on oversight activities during the conduct of clinical trials. A central statistical assessment of the quality of data collected in clinical trials presents “opportunities for new monitoring approaches (e.g., centralized monitoring) that can improve the quality and efficiency of sponsor oversight of clinical investigations.” [1]. Such central statistical monitoring has been suggested to detect fraud and other types of data errors in clinical trials [3,4,5,6]. 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
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