A computationally simple central monitoring procedure, effectively applied to empirical trial data with known fraud

Central monitoring of multicenter clinical trials becomes an ever more feasible quality assurance tool, in particular for the detection of data fabrication. More widespread application, across both industry sponsored as well as academic clinical trials, requires central monitoring methodologies that are both effective and relatively simple in implementation.We describe a computationally simple fraud detection procedure intended to be applied repeatedly and (semi-)automatically to accumulating baseline data and to detect data fabrication in multicenter trials as early as possible. The procedure is based on anticipated characteristics of fabricated data. It consists of seven analyses, each of which flags approximately 10% of the centers. Centers that are flagged three or more times are considered "potentially fraudulent" and require additional investigation. The procedure is illustrated using empirical trial data with known fraud.In the illustration data, the fraudulent center is detected in most repeated applications to the accumulating trial data, while keeping the proportion of false-positive results at sufficiently low levels.The proposed procedure is computationally simple and appears to be effective in detecting center-level data fabrication. However, assessment of the procedure on independent trial data sets with known data fabrication is required.