Performance of four centralized statistical monitoring methods for early detection of an atypical center in a multicenter study

Background: The quality of the data collected is essential for the credibility of the results of clinical trials. Centralized statistical monitoring (CSM) has been proposed to quickly identify one study center in which the distribution of a variable is atypical, prompting on-site confirmation of the problem and correction as necessary. The ideal CSM method should allow early detection of a problem and therefore involve the fewest possible participants. Methods We simulated clinical trials and compared the performance of four CSM methods (Student, Desmet, Hatayama, Distance) to detect whether the distribution of a quantitative variable was atypical in one center in relation to the others, with different numbers of participants and different mean deviation amplitudes. Results The Desmet and Distance methods had low sensitivity for low mean-deviation values but very high specificity for detecting all deviations of the mean (including small values). The Student and Hatayama methods had higher sensitivity for low mean-deviation values but very low specificity for detecting all deviations of the mean. Increasing the number of participants in the atypical center, or increasing the ratio of the number of participants in the atypical center to the number of participants in the study, did not fundamentally alter the findings. Conclusion Although the Student and Hatayama methods are more sensitive, their low specificity would lead to too many alerts being triggered, which would result in additional unnecessary control work to ensure data quality. The Desmet and Distance methods have low sensitivity when the deviation from the mean is low, suggesting that the CSM should be used alongside other conventional monitoring procedures rather than replacing them. However, they have excellent specificity, which suggests they can be applied routinely, since using them takes up no time at central level and does not cause any unnecessary workload in investigating centers.