Does Central Statistical Monitoring Improve Data Quality? An Analysis of 1,111 Sites in 159 Clinical Trials
Open accessAbstract
Background: Central monitoring aims at improving the quality of clinical research by pro-actively identifying risks and remediating emerging issues in the conduct of a clinical trial that may have an adverse impact on patient safety and/or the reliability of trial results. This paper, focusing on statistical data monitoring (SDM), is the second of a series that attempts to quantify the impact of central monitoring in clinical trials. Material and Methods: Quality improvement was assessed in studies using SDM from a single large central monitoring platform. The analysis focused on a total of 1111 sites that were identified as at-risk by the SDM tests and for which the study teams conducted a follow-up investigation. These sites were taken from 159 studies conducted by 23 different clinical development organizations (including both sponsor companies and contract research organizations). Two quality improvement metrics were assessed for each selected site, one based on a site data inconsistency score (DIS, overall -log 10P-value of the site compared with all other sites) and the other based on the observed metric value associated with each risk signal. Results: The SDM quality metrics showed improvement in 83% (95% CI, 80–85%) of the sites across therapeutic areas and study phases (primarily phases 2 and 3). In contrast, only 56% (95% CI, 41–70%) of sites showed improvement in 2 historical studies that did not use SDM during study conduct. Conclusion: The results of this analysis provide clear quantitative evidence supporting the hypothesis that the use of SDM in central monitoring is leading to improved quality in clinical trial conduct and associated data across participating sites.
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Some types of monitoring have stronger evidence for their benefits and efficiency than others.
“Some types of monitoring have stronger evidence for their benefits and efficiency than others.”
From SPIRIT 2025 explanation and elaboration: updated guideline for protocols of randomised trials (2025) · cites this paper as [469]
Central statistical monitoring has been shown to improve multiple indicators of data quality.
“For example, central statistical monitoring has been shown to improve multiple indicators of data quality.468 469”
From SPIRIT 2025 explanation and elaboration: updated guideline for protocols of randomised trials (2025) · cites this paper as [469]
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Cites
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“A theoretical concern exists that sites observed with a high DIS at one timepoint will naturally tend toward a lower DIS subsequently due to a regression-to-the-mean effect [21].”
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“There are examples of analyses focusing on the quantitative performance of central monitoring but they have typically analyzed one or only a few datasets or studies retrospectively and generally used simulated data [6, 18, 19].”
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“The DIS observed at the end of the study is used instead, which enables an assessment of the natural evolution of an “at-risk” site’s DIS in the absence of SDM. Additionally, 95% Wilson score confidence intervals were used to estimate the DIS improvement in both studies [20].”
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Does Central Monitoring Lead to Higher Quality? An Analysis of Key Risk Indicator Outcomes
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“However, some factors may increase those risks, including the following: complex study protocols, complicated eCRF and database designs, and poor site training [22, 23].”
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- Authors
- de Viron, S., Trotta, L., Steijn, W., Young, S., & Buyse, M.
- Affiliations
- International Drug Development Institute (Belgium) (BE)
- Venue
- Therapeutic Innovation & Regulatory Science
- Published
- 2024-02-09
- Volume
- 58
- Issue
- 3
- Pages
- 483-494
- ISSN
- 2168-4790 (ISSN-L)
- 2168-4804
- PMC
- PMC11043176
- OpenAlex
- W4391683339
- Cited by
- 9
- References
- 20
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 2
- FWCI
- 3.09
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Therapeutic Innovation & Regulatory Science (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Document Server@UHasselt (UHasselt) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Toxicology › Pharmacovigilance and Adverse Drug Reactions
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Pharmaceutical studies and practices
- Keywords
- central monitoring, clinical trial data quality, data inconsistency, quality improvement, risk signals, clinical trial conduct, contract research organizations
- MeSH terms
- Data Accuracy, Clinical Trials as Topic, Humans, Reproducibility of Results, Clinical Trials Data Monitoring Committees, Quality Improvement, Patient Safety
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