Details
| Original language | English |
|---|---|
| Pages (from-to) | 1217–1228 |
| Number of pages | 12 |
| Journal | Therapeutic Innovation and Regulatory Science |
| Volume | 57 |
| Issue number | 6 |
| Early online date | 14 Jul 2023 |
| Publication status | Published - Nov 2023 |
Abstract
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).
Keywords
- Data quality control, Grand mean, Monitoring, Multicenter clinical trials, Registry data
ASJC Scopus subject areas
- Pharmacology, Toxicology and Pharmaceutics(all)
- Pharmacology, Toxicology and Pharmaceutics (miscellaneous)
- Medicine(all)
- Public Health, Environmental and Occupational Health
- Medicine(all)
- Pharmacology (medical)
Sustainable Development Goals
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In: Therapeutic Innovation and Regulatory Science, Vol. 57, No. 6, 11.2023, p. 1217–1228.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Application of Statistical Methods for Central Statistical Monitoring and Implementations on the German Multiple Sclerosis Registry
AU - Fneish, Firas
AU - Ellenberger, David
AU - Frahm, Niklas
AU - Stahmann, Alexander
AU - Fortwengel, Gerhard
AU - Schaarschmidt, Frank
N1 - Open Access funding enabled and organized by Projekt DEAL. The author(s) received no financial support for the research, authorship, and/or publication of this article.
PY - 2023/11
Y1 - 2023/11
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).
AB - 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 - Grand mean
KW - Monitoring
KW - Multicenter clinical trials
KW - Registry data
UR - http://www.scopus.com/inward/record.url?scp=85164841434&partnerID=8YFLogxK
U2 - 10.1007/s43441-023-00550-0
DO - 10.1007/s43441-023-00550-0
M3 - Article
C2 - 37450198
AN - SCOPUS:85164841434
VL - 57
SP - 1217
EP - 1228
JO - Therapeutic Innovation and Regulatory Science
JF - Therapeutic Innovation and Regulatory Science
SN - 2168-4790
IS - 6
ER -