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Central Statistical Monitoring (CSM) is gaining widespread recognition for its contribution to improving data integrity and regulatory compliance in clinical trials. While the existing literature offers numerous approaches based on fundamental statistical techniques, many of these methods exhibit notable limitations and shortcomings.

This web application introduces a flexible framework for implementing CSM by comparing the average values from individual centers to the overall Grand Mean (GM). The methodology is adaptable to diverse data types through suitable statistical modeling. Users can upload their datasets and perform these comparisons directly within the app.

References

1. Fneish, F., Ellenberger, D., Frahm, N. et al. Application of Statistical Methods for Central Statistical Monitoring and Implementations on the German Multiple Sclerosis Registry. Ther Innov Regul Sci 57, 1217–1228 (2023). https://doi.org/10.1007/s43441-023-00550-0

2. Fneish F, Ellenberger D, Frahm N, Stahmann A, Schaarschmidt F. Appropriate statistical model for count data in central statistical monitoring and application on German Multiple Sclerosis Registry. In German Medical Science GMS Publishing House; 2023. p. DocAbstr. 164

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4. Fneish F, Ellenberger D, Frahm N, Stahmann A, Fortwengel G, Schaarschmidt F. Central Statistical Monitoring for time-to-event Endpoints and Application on Data from the German Multiple Sclerosis Registry. In German Medical Science GMS Publishing House; 2024. p. DocAbstr. 184.

5. Konietschke F, Hothorn LA, Brunner E. Rank-based multiple test procedures and simultaneous confdence intervals. Electron J Stat.2012;6:738–59

6. Konietschke, F., Placzek, M., Schaarschmidt, F., & Hothorn, L. A. (2015). nparcomp: An R Software Package for Nonparametric Multiple Comparisons and Simultaneous Confidence Intervals. Journal of Statistical Software, 64(9), 1–17

7. Ioannis Kosmidis, David Firth, Jeffreys-prior penalty, finiteness and shrinkage in binomial-response generalized linear models, Biometrika, Volume 108, Issue 1, March 2021, Pages 71–82

8. Andrew Gelman. Aleks Jakulin. Maria Grazia Pittau. Yu-Sung Su. “A weakly informative default prior distribution for logistic and other regression models.” Ann. Appl. Stat. 2 (4) 1360 – 1383, December 2008

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