The DOI matches its Crossref record.
Does Central Monitoring Lead to Higher Quality? An Analysis of Key Risk Indicator Outcomes
Open accessAbstract
Background: Central monitoring, which typically includes the use of key risk indicators (KRIs), aims at improving the quality of clinical research by pro-actively identifying 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. However, there has to-date been a relative lack of direct quantitative evidence published supporting the claim that central monitoring actually leads to improved quality. Material and Methods: Nine commonly used KRIs were analyzed for evidence of quality improvement using data retrieved from a large central monitoring platform. A total of 212 studies comprising 1676 sites with KRI signals were used in the analysis, representing central monitoring activity from 23 different sponsor organizations. Two quality improvement metrics were assessed for each KRI, one based on a statistical score (p-value) and the other based on a KRI’s observed value. Results: Both KRI quality metrics showed improvement in a vast majority of sites (82.9% for statistical score, 81.1% for observed KRI value). Additionally, the statistical score and the observed KRI values improved, respectively by 66.1% and 72.4% on average towards the study average for those sites showing improvement. Conclusion: The results of this analysis provide clear quantitative evidence supporting the hypothesis that use of KRIs in central monitoring is leading to improved quality in clinical trial conduct and associated data across participating sites.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 13
- de Viron S, Trotta L, Schumacher H, Lomp HJ, Höppner S, Young S, et al. Detection of fraud in a clinical trial using unsupervised statistical monitoring. Ther Innov Regul Sci. 2022;56(1):130–6. · DOI · Paper page
- Venet D, Doffagne E, Burzykowski T, Beckers F, Tellier Y, Genevois-Marlin E, et al. A statistical approach to central monitoring of data quality in clinical trials. Clin Trials Lond Engl. 2012;9(6):705–13. · DOI
- Desmet L, Venet D, Doffagne E, Timmermans C, Burzykowski T, Legrand C, et al. Linear mixed-effects models for central statistical monitoring of multicenter clinical trials. Stat Med. 2014;33:5265–79. · DOI
- Trotta L, Kabeya Y, Buyse M, Doffagne E, Venet D, Desmet L, et al. Detection of atypical data in multicenter clinical trials using unsupervised statistical monitoring. Clin Trials Lond Engl. 2019;16(5):512–22. · DOI
- Barnes B, Stansbury N, Brown D, Garson L, Gerard G, Piccoli N, et al. Risk-based monitoring in clinical trials: past, present, and future. Ther Innov Regul Sci. 2021;55(4):899–906. · DOI
- Wilson B, Provencher T, Gough J, Clark S, Abdrachitov R, de Roeck K, et al. Defining a central monitoring capability: sharing the experience of transcelerate biopharma’s approach, part 1. Ther Innov Regul Sci. 2014;48(5):529–35. · DOI
- Gough J, Wilson B, Zerola M, Wallis P, Mork L, Knepper D, et al. Defining a central monitoring capability: sharing the experience of transcelerate biopharma’s approach, part 2. Ther Innov Regul Sci. 2016;50(1):8–14. · DOI
- Barnes S, Katta N, Sanford N, Staigers T, Verish T. Technology considerations to enable the risk-based monitoring methodology. Ther Innov Regul Sci. 2014;48(5):536–45. · DOI
- Timmermans C, Venet D, Burzykowski T. Data-driven risk identification in phase III clinical trials using central statistical monitoring. Int J Clin Oncol. 2016;21(1):38–45. · DOI
- Makowski M, Bhagat R, Chevalier S, Gilbert S, Görtz DR, Kozinska M, et al. Historical benchmarks for quality tolerance limits parameters in clinical trials. Ther Innov Regul Sci. 2021;55:1265–73. · DOI
- Desmet L, Venet D, Doffagne E, Timmermans C, Legrand C, Burzykowski T, et al. Use of the beta-binomial model for central statistical monitoring of multicenter clinical trials. Stat Biopharm Res. 2017;9(1):1–11. · DOI
- Kondo H, Kamiyoshihara T, Fujisawa K, Nojima T, Tanigawa R, Fujiwara H, et al. Evaluation of data errors and monitoring activities in a trial in Japan using a risk-based approach including central monitoring and site risk assessment. Ther Innov Regul Sci. 2021;55(4):841–9. · DOI
- Sheetz N, Wilson B, Benedict J, Huffman E, Lawton A, Travers M, et al. Evaluating source data verification as a quality control measure in clinical trials. Ther Innov Regul Sci. 2014;48(6):671–80. · DOI
Cited by
Sorted by citations
de Viron, S., Trotta, L., Steijn, W., Young, S., & Buyse, M. (2024) · Therapeutic Innovation & Regulatory Science · doi:10.1007/s43441-024-00613-w · Cited by 9
Self-citation
Metadata
- Authors
- de Viron, S., Trotta, L., Steijn, W., Young, S., & Buyse, M.
- Affiliations
- International Drug Development Institute (Belgium) (BE)
- Corresponding author
- de Viron, Sylviane
- Venue
- Therapeutic Innovation & Regulatory Science
- Published
- 2022-10-21
- Volume
- 57
- Issue
- 2
- Pages
- 295-303
- ISSN
- 2168-4790 (ISSN-L)
- 2168-4804
- PMC
- PMC9589525
- OpenAlex
- W4307031881
- Cited by
- 9
- References
- 0
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 2
- FWCI
- 1.11
- 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 › Nursing › Issues, ethics and legal aspects › Pharmaceutical industry and healthcare
- Keywords
- central monitoring, key risk indicators, clinical trial quality, quality improvement, clinical trial conduct, patient safety
- MeSH terms
- Humans, Reproducibility of Results, Benchmarking
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review