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

OBJECTIVES: 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. STUDY DESIGN AND SETTING: 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. RESULTS: 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. CONCLUSION: 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.

Predicting enrollment performance of investigational centers in phase III multi-center clinical trials.

Failure to meet subject recruitment targets in clinical trials continues to be a widespread problem with potentially serious scientific, logistical, financial and ethical consequences. On the operational level, enrollment-related issues may be mitigated by careful site selection and by allocating monitoring or training resources proportionally to the anticipated risk of poor enrollment. Such procedures require estimates of the expected recruitment performance that are sufficiently reliable to allow centers to be sensibly categorized. In this study, we investigate whether information obtained from feasibility questionnaires can potentially be used to predict which centers will and which centers will not meet their enrollment targets by means of multivariable logistic regression analysis. From a large set of 59 candidate predictors, we determined the subset that is optimal for predictive purposes using Least Absolute Shrinkage and Selection Operator (LASSO) regularization. Although the extent to which the results are generalizable remains to be determined, they indicate that the prediction accuracy of the optimal model is only a marginal improvement over the intercept-only model, illustrating the difficulty of prediction in this setting.