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Central Statist Monitoring (CSM) receives widespread recognition for the contribution to improving data integrity and regulatory compliance in clinical investigations. Although the existing literature offers countless approaches based on fundamental statistical techniques, many of these methods show remarkable limitations and shortcomings.
This web application Introduces a flexible framework for the implementation of CSM by comparing the average values of individual centers with the total Grand Average (GM). The methodology is adaptable to various data types through suitable statistical modeling. Users can upload their data sets and perform these comparisons directly within the app.
References
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- Fneish F, Ellenberger D, Frahm N, Stahmann A, Schaarschmidt F. Pas statistical model for tel data in central statistical monitoring and application to the German multiple sclerosis register. In German medical science GMS Publishing House; 2023. P. Docabstr. 164
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- Fneish F, Ellenberger D, Frahm N, Stahmann A, Fortwengel G, Schaarschmidt F. Central statistical monitoring for end points of time-to-event and application to data from the German multiple sclerosis register. In German medical science GMS Publishing House; 2024. P. Docabstr. 184.
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- Ioannis Kosmidis, David Firth, Jeffreys-Prior Penalty, Finitess and Shrimp in Binomial-Response Generalized Linear Models, Biometrika, Volume 108, Number 1, March 2021, pages 71–82
- Andrew Gelman. Aleks Jakulin. Maria Grazia Pittau. Yu-sung su. “A weakly informative standard prior distribution for logistics and other regression models.” Ann. Appl. Stat. 2 (4) 1360 – 1383, December 2008
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