TY  - JOUR
U1  - Wissenschaftlicher Artikel
A1  - Fneish, Firas
A1  - Ellenberger, David
A1  - Frahm, Niklas
A1  - Stahmann, Alexander
A1  - Fortwengel, Gerhard
A1  - Schaarschmidt, Frank
T1  - Application of Statistical Methods for Central Statistical Monitoring and Implementations on the German Multiple Sclerosis Registry
JF  - Therapeutic Innovation & Regulatory Science
N2  - Monitoring of clinical trials is a fundamental process required by regulatory agencies. It assures the compliance of a center to the required regulations and the trial protocol. Traditionally, monitoring teams relied on extensive on-site visits and source data verification. However, this is costly, and the outcome is limited. Thus, central statistical monitoring (CSM) is an additional approach recently embraced by the International Council for Harmonisation (ICH) to detect problematic or erroneous data by using visualizations and statistical control measures. Existing implementations have been primarily focused on detecting inlier and outlier data. Other approaches include principal component analysis and distribution of the data. Here we focus on the utilization of comparisons of centers to the Grand mean for different model types and assumptions for common data types, such as binomial, ordinal, and continuous response variables. We implement the usage of multiple comparisons of single centers to the Grand mean of all centers. This approach is also available for various non-normal data types that are abundant in clinical trials. Further, using confidence intervals, an assessment of equivalence to the Grand mean can be applied. In a Monte Carlo simulation study, the applied statistical approaches have been investigated for their ability to control type I error and the assessment of their respective power for balanced and unbalanced designs which are common in registry data and clinical trials. Data from the German Multiple Sclerosis Registry (GMSR) including proportions of missing data, adverse events and disease severity scores were used to verify the results on Real-World-Data (RWD).
KW  - Data quality control
KW  - Multicenter clinical trials
KW  - Grand mean
KW  - Registry data
KW  - Monitoring
KW  - Datenqualität
KW  - Klinisches Experiment
Y1  - 2023
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:960-opus4-29162
SN  - 2168-4804
SS  - 2168-4804
U6  - https://doi.org/10.25968/opus-2916
DO  - https://doi.org/10.25968/opus-2916
SP  - 12
S1  - 12
ER  -