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de Viron S, Trotta L, Steijn W, Young S, Buyse M. Does Central Statistical Monitoring Improve Data Quality? An Analysis of 1,111 Sites in 159 Clinical Trials. Ther Innov Regul Sci 2024; 58:483-494. [PMID: 38334868 PMCID: PMC11043176 DOI: 10.1007/s43441-024-00613-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 01/08/2024] [Indexed: 02/10/2024]
Abstract
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 -log10 P-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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Affiliation(s)
- Sylviane de Viron
- CluePoints S.A, Avenue Albert Einstein, 2a 1348, Louvain-la-Neuve, Belgium.
| | - Laura Trotta
- CluePoints S.A, Avenue Albert Einstein, 2a 1348, Louvain-la-Neuve, Belgium
| | - William Steijn
- CluePoints S.A, Avenue Albert Einstein, 2a 1348, Louvain-la-Neuve, Belgium
| | | | - Marc Buyse
- CluePoints S.A, Avenue Albert Einstein, 2a 1348, Louvain-la-Neuve, Belgium
- International Drug Development Institute (IDDI), Louvain-la-Neuve, Belgium
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Hasselt, Belgium
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2
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Fneish F, Ellenberger D, Frahm N, Stahmann A, Fortwengel G, Schaarschmidt F. Application of Statistical Methods for Central Statistical Monitoring and Implementations on the German Multiple Sclerosis Registry. Ther Innov Regul Sci 2023; 57:1217-1228. [PMID: 37450198 PMCID: PMC10579126 DOI: 10.1007/s43441-023-00550-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 06/08/2023] [Indexed: 07/18/2023]
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).
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Affiliation(s)
- Firas Fneish
- Department of Biostatistics, Institute of Cell Biology and Biophysics, Leibniz University Hannover, Herrenhäuser Straße 2, 30419 Hannover, Germany
- German MS-Register, MS Forschungs- und Projektentwicklungs- gGmbH [MSFP], Krausenstraße 50, 30171 Hannover, Germany
| | - David Ellenberger
- German MS-Register, MS Forschungs- und Projektentwicklungs- gGmbH [MSFP], Krausenstraße 50, 30171 Hannover, Germany
| | - Niklas Frahm
- German MS-Register, MS Forschungs- und Projektentwicklungs- gGmbH [MSFP], Krausenstraße 50, 30171 Hannover, Germany
| | - Alexander Stahmann
- German MS-Register, MS Forschungs- und Projektentwicklungs- gGmbH [MSFP], Krausenstraße 50, 30171 Hannover, Germany
| | - Gerhard Fortwengel
- Faculty III–Media, Information, and Design, Hochschule Hannover, 30539 Hannover, Germany
| | - Frank Schaarschmidt
- Department of Biostatistics, Institute of Cell Biology and Biophysics, Leibniz University Hannover, Herrenhäuser Straße 2, 30419 Hannover, Germany
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3
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de Viron S, Trotta L, Steijn W, Young S, Buyse M. Does Central Monitoring Lead to Higher Quality? An Analysis of Key Risk Indicator Outcomes. Ther Innov Regul Sci 2023; 57:295-303. [PMID: 36269551 PMCID: PMC9589525 DOI: 10.1007/s43441-022-00470-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 09/30/2022] [Indexed: 11/21/2022]
Abstract
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.
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Affiliation(s)
- Sylviane de Viron
- CluePoints S.A., Avenue Albert Einstein, 2a, 1348, Louvain-la-Neuve, Belgium.
| | - Laura Trotta
- CluePoints S.A., Avenue Albert Einstein, 2a, 1348 Louvain-la-Neuve, Belgium
| | - William Steijn
- CluePoints S.A., Avenue Albert Einstein, 2a, 1348 Louvain-la-Neuve, Belgium
| | | | - Marc Buyse
- CluePoints S.A., Avenue Albert Einstein, 2a, 1348 Louvain-la-Neuve, Belgium ,grid.482598.aInternational Drug Development Institute (IDDI), Louvain-la-Neuve, Belgium ,grid.12155.320000 0001 0604 5662Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Hasselt, Belgium
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4
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de Viron S, Trotta L, Schumacher H, Lomp HJ, Höppner S, Young S, Buyse M. Detection of Fraud in a Clinical Trial Using Unsupervised Statistical Monitoring. Ther Innov Regul Sci 2021; 56:130-136. [PMID: 34590286 PMCID: PMC8688378 DOI: 10.1007/s43441-021-00341-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 09/19/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND A central statistical assessment of the quality of data collected in clinical trials can improve the quality and efficiency of sponsor oversight of clinical investigations. MATERIAL AND METHODS The database of a large randomized clinical trial with known fraud was reanalyzed with a view to identifying, using only statistical monitoring techniques, the center where fraud had been confirmed. The analysis was conducted with an unsupervised statistical monitoring software using mixed-effects statistical models. The statistical analyst was unaware of the location, nature, and extent of the fraud. RESULTS Five centers were detected as atypical, including the center with known fraud (which was ranked 2). An incremental analysis showed that the center with known fraud could have been detected after only 25% of its data had been reported. CONCLUSION An unsupervised approach to central monitoring, using mixed-effects statistical models, is effective at detecting centers with fraud or other data anomalies in clinical trials.
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Affiliation(s)
- Sylviane de Viron
- CluePoints S.A., Avenue Albert Einstein, 2a, 1348, Louvain-la-Neuve, Belgium.
| | - Laura Trotta
- CluePoints S.A., Avenue Albert Einstein, 2a, 1348, Louvain-la-Neuve, Belgium
| | | | | | - Sebastiaan Höppner
- CluePoints S.A., Avenue Albert Einstein, 2a, 1348, Louvain-la-Neuve, Belgium
| | | | - Marc Buyse
- CluePoints S.A., Avenue Albert Einstein, 2a, 1348, Louvain-la-Neuve, Belgium.,International Drug Development Institute (IDDI), Louvain-la-Neuve, Belgium.,Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Hasselt, Belgium
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5
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Cragg WJ, Hurley C, Yorke-Edwards V, Stenning SP. Dynamic methods for ongoing assessment of site-level risk in risk-based monitoring of clinical trials: A scoping review. Clin Trials 2021; 18:245-259. [PMID: 33611927 PMCID: PMC8010889 DOI: 10.1177/1740774520976561] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Background/Aims It is increasingly recognised that reliance on frequent site visits for monitoring clinical trials is inefficient. Regulators and trialists have recently encouraged more risk-based monitoring. Risk assessment should take place before a trial begins to define the overarching monitoring strategy. It can also be done on an ongoing basis, to target sites for monitoring activity. Various methods have been proposed for such prioritisation, often using terms like ‘central statistical monitoring’, ‘triggered monitoring’ or, as in the International Conference on Harmonization Good Clinical Practice guidance, ‘targeted on-site monitoring’. We conducted a scoping review to identify such methods, to establish if any were supported by adequate evidence to allow wider implementation, and to guide future developments in this field of research. Methods We used seven publication databases, two sets of methodological conference abstracts and an Internet search engine to identify methods for using centrally held trial data to assess site conduct during a trial. We included only reports in English, and excluded reports published before 1996 or not directly relevant to our research question. We used reference and citation searches to find additional relevant reports. We extracted data using a predefined template. We contacted authors to request additional information about included reports. Results We included 30 reports in our final dataset, of which 21 were peer-reviewed publications. In all, 20 reports described central statistical monitoring methods (of which 7 focussed on detection of fraud or misconduct) and 9 described triggered monitoring methods; 21 reports included some assessment of their methods’ effectiveness, typically exploring the methods’ characteristics using real trial data without known integrity issues. Of the 21 with some effectiveness assessment, most contained limited information about whether or not concerns identified through central monitoring constituted meaningful problems. Several reports demonstrated good classification ability based on more than one classification statistic, but never without caveats of unclear reporting or other classification statistics being low or unavailable. Some reports commented on cost savings from reduced on-site monitoring, but none gave detailed costings for the development and maintenance of central monitoring methods themselves. Conclusion Our review identified various proposed methods, some of which could be combined within the same trial. The apparent emphasis on fraud detection may not be proportionate in all trial settings. Despite some promising evidence and some self-justifying benefits for data cleaning activity, many proposed methods have limitations that may currently prevent their routine use for targeting trial monitoring activity. The implementation costs, or uncertainty about these, may also be a barrier. We make recommendations for how the evidence-base supporting these methods could be improved.
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Affiliation(s)
- William J Cragg
- MRC Clinical Trials Unit at UCL, London, UK.,Clinical Trials Research Unit, Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK
| | - Caroline Hurley
- Health Research Board-Trials Methodology Research Network (HRB-TMRN), National University of Ireland Galway, Galway, Ireland
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Yamada O, Chiu SW, Takata M, Abe M, Shoji M, Kyotani E, Endo C, Shimada M, Tamura Y, Yamaguchi T. Clinical trial monitoring effectiveness: Remote risk-based monitoring versus on-site monitoring with 100% source data verification. Clin Trials 2020; 18:158-167. [PMID: 33258688 DOI: 10.1177/1740774520971254] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND/AIMS Traditional on-site monitoring of clinical trials via frequent site visits and 100% source data verification is cost-consuming, and it still cannot guarantee data quality effectively. Depending on the types and designs of clinical trials, an alternative would be combining several monitoring methods, such as risk-based monitoring and remote monitoring. However, there is insufficient evidence of its effectiveness. This research compared the effectiveness of risk-based monitoring with a remote monitoring system with that of traditional on-site monitoring. METHODS With a cloud-based remote monitoring system called beagle View®, we created a remote risk-based monitoring methodology that focused only on critical data and processes. We selected a randomized controlled trial conducted at Tohoku University Hospital and randomly sampled 11 subjects whose case report forms had already been reviewed by data managers. Critical data and processes were verified retrospectively by remote risk-based monitoring; later, all data and processes were confirmed by on-site monitoring. We compared the ability of remote risk-based monitoring to detect critical data and process errors with that of on-site monitoring with 100% source data verification, including an examination of clinical trial staff workload and potential cost savings. RESULTS Of the total data points (n = 5617), 19.7% (n = 1105, 95% confidence interval = 18.7-20.7) were identified as critical. The error rates of critical data detected by on-site monitoring, remote risk-based monitoring, and data review by data managers were 7.6% (n = 84, 95% CI = 6.2-9.3), 7.6% (n = 84, 95% confidence interval = 6.2-9.3), and 3.9% (n = 43, 95% confidence interval = 2.9-5.2), respectively. The total number of critical process errors detected by on-site monitoring was 14. Of these 14, 92.9% (n = 13, 95% confidence interval = 68.5-98.7) and 42.9% (n = 6, 95% confidence interval = 21.4-67.4) of critical process errors were detected by remote risk-based monitoring and data review by data managers, respectively. The mean time clinical trial staff spent dealing with remote risk-based monitoring was 9.9 ± 5.3 (mean ± SD) min per visit per subject. Our calculations show that remote risk-based monitoring saved between 9 and 41 on-site monitoring visits, corresponding to a cost of between US$13,500 and US$61,500 per trial site. CONCLUSION Remote risk-based monitoring was able to detect critical data and process errors as well as on-site monitoring with 100% source data verification, saving travel time and monitoring costs. Remote risk-based monitoring offers an effective alternative to traditional on-site monitoring of clinical trials.
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Affiliation(s)
- Osamu Yamada
- Division of Biostatistics, Graduate School of Medicine, Tohoku University, Miyagi, Japan
| | - Shih-Wei Chiu
- Division of Biostatistics, Graduate School of Medicine, Tohoku University, Miyagi, Japan.,Clinical Research Data Center, Tohoku University Hospital, Miyagi, Japan
| | - Munenori Takata
- Division of Biostatistics, Graduate School of Medicine, Tohoku University, Miyagi, Japan.,Clinical Research Data Center, Tohoku University Hospital, Miyagi, Japan
| | - Michiaki Abe
- Department of Education and Support for Regional Medicine, Tohoku University Hospital, Miyagi, Japan
| | - Mutsumi Shoji
- Department of Education and Support for Regional Medicine, Tohoku University Hospital, Miyagi, Japan
| | - Eri Kyotani
- Clinical Research Data Center, Tohoku University Hospital, Miyagi, Japan
| | - Chiyo Endo
- Clinical Research Data Center, Tohoku University Hospital, Miyagi, Japan
| | - Minami Shimada
- Clinical Research Data Center, Tohoku University Hospital, Miyagi, Japan
| | | | - Takuhiro Yamaguchi
- Division of Biostatistics, Graduate School of Medicine, Tohoku University, Miyagi, Japan.,Clinical Research Data Center, Tohoku University Hospital, Miyagi, Japan
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Buyse M, Trotta L, Saad ED, Sakamoto J. Central statistical monitoring of investigator-led clinical trials in oncology. Int J Clin Oncol 2020; 25:1207-1214. [PMID: 32577951 PMCID: PMC7308734 DOI: 10.1007/s10147-020-01726-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Accepted: 06/14/2020] [Indexed: 01/17/2023]
Abstract
Investigator-led clinical trials are pragmatic trials that aim to investigate the benefits and harms of treatments in routine clinical practice. These much-needed trials represent the majority of all trials currently conducted. They are however threatened by the rising costs of clinical research, which are in part due to extensive trial monitoring processes that focus on unimportant details. Risk-based quality management focuses, instead, on “things that really matter”. We discuss the role of central statistical monitoring as part of risk-based quality management. We describe the principles of central statistical monitoring, provide examples of its use, and argue that it could help drive down the cost of randomized clinical trials, especially investigator-led trials, whilst improving their quality.
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Affiliation(s)
- Marc Buyse
- International Drug Development Institute (IDDI), San Francisco, CA, USA. .,Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Hasselt, Belgium. .,CluePoints, Louvain-la-Neuve, Belgium.
| | | | - Everardo D Saad
- International Drug Development Institute (IDDI), 30 avenue provinciale, 1340, Ottignies-Louvain-la-Neuve, Belgium
| | - Junichi Sakamoto
- Tokai Central Hospital, Kakamigahara, Japan.,Epidemiological and Clinical Research Information Network (ECRIN), Kyoto, Japan
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8
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Trotta L, Kabeya Y, Buyse M, Doffagne E, Venet D, Desmet L, Burzykowski T, Tsuburaya A, Yoshida K, Miyashita Y, Morita S, Sakamoto J, Praveen P, Oba K. Detection of atypical data in multicenter clinical trials using unsupervised statistical monitoring. Clin Trials 2019; 16:512-522. [PMID: 31331195 DOI: 10.1177/1740774519862564] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND/AIMS A risk-based approach to clinical research may include a central statistical assessment of data quality. We investigated the operating characteristics of unsupervised statistical monitoring aimed at detecting atypical data in multicenter experiments. The approach is premised on the assumption that, save for random fluctuations and natural variations, data coming from all centers should be comparable and statistically consistent. Unsupervised statistical monitoring consists of performing as many statistical tests as possible on all trial data, in order to detect centers whose data are inconsistent with data from other centers. METHODS We conducted simulations using data from a large multicenter trial conducted in Japan for patients with advanced gastric cancer. The actual trial data were contaminated in computer simulations for varying percentages of centers, percentages of patients modified within each center and numbers and types of modified variables. The unsupervised statistical monitoring software was run by a blinded team on the contaminated data sets, with the purpose of detecting the centers with contaminated data. The operating characteristics (sensitivity, specificity and Youden's J-index) were calculated for three detection methods: one using the p-values of individual statistical tests after adjustment for multiplicity, one using a summary of all p-values for a given center, called the Data Inconsistency Score, and one using both of these methods. RESULTS The operating characteristics of the three methods were satisfactory in situations of data contamination likely to occur in practice, specifically when a single or a few centers were contaminated. As expected, the sensitivity increased for increasing proportions of patients and increasing numbers of variables contaminated. The three methods showed a specificity better than 93% in all scenarios of contamination. The method based on the Data Inconsistency Score and individual p-values adjusted for multiplicity generally had slightly higher sensitivity at the expense of a slightly lower specificity. CONCLUSIONS The use of brute force (a computer-intensive approach that generates large numbers of statistical tests) is an effective way to check data quality in multicenter clinical trials. It can provide a cost-effective complement to other data-management and monitoring techniques.
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Affiliation(s)
| | - Yuusuke Kabeya
- Department of Biostatistics, The University of Tokyo, Tokyo, Japan.,EPS Corporation, Tokyo, Japan
| | - Marc Buyse
- International Drug Development Institute (IDDI), San Francisco, CA, USA.,CluePoints, Wayne, PA, USA
| | | | - David Venet
- Institut de Recherches Interdisciplinaires et de Développements en Intelligence Artificielle (IRIDIA), University of Brussels, Brussels, Belgium
| | - Lieven Desmet
- Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA), University of Louvain, Louvain-la-Neuve, Belgium
| | - Tomasz Burzykowski
- International Drug Development Institute (IDDI), Louvain-la-Neuve, Belgium.,Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), University of Hasselt, Hasselt, Belgium
| | - Akira Tsuburaya
- Department of Surgery, Jizankai Medical Foundation, Tsuboi Cancer Center Hospital, Koriyama, Japan
| | - Kazuhiro Yoshida
- Department of Surgical Oncology, Graduate School of Medicine, Gifu University, Gifu, Japan
| | - Yumi Miyashita
- Epidemiological and Clinical Research Information Network (ECRIN), Okazaki, Japan
| | - Satoshi Morita
- Department of Biomedical Statistics and Bioinformatics, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Junichi Sakamoto
- Epidemiological and Clinical Research Information Network (ECRIN), Okazaki, Japan.,Tokai Central Hospital, Kakamigahara, Japan
| | | | - Koji Oba
- Department of Biostatistics, The University of Tokyo, Tokyo, Japan.,Interfaculty Initiative in Information Studies, The University of Tokyo, Tokyo, Japan
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Walker KF, Turzanski J, Whitham D, Montgomery A, Duley L. Monitoring performance of sites within multicentre randomised trials: a systematic review of performance metrics. Trials 2018; 19:562. [PMID: 30326948 PMCID: PMC6192157 DOI: 10.1186/s13063-018-2941-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 09/26/2018] [Indexed: 11/11/2022] Open
Abstract
Background Large multicentre trials are complex and expensive projects. A key factor for their successful planning and delivery is how well sites meet their targets in recruiting and retaining participants, and in collecting high-quality, complete data in a timely manner. Collecting and monitoring easily accessible data relevant to performance of sites has the potential to improve trial management efficiency. The aim of this systematic review was to identify metrics that have either been proposed or used for monitoring site performance in multicentre trials. Methods We searched the Cochrane Library, five biomedical bibliographic databases (CINAHL, EMBASE, Medline, PsychINFO and SCOPUS) and Google Scholar for studies describing ways of monitoring or measuring individual site performance in multicentre randomised trials. Records identified were screened for eligibility. For included studies, data on study content were extracted independently by two reviewers, and disagreements resolved by discussion. Results After removing duplicate citations, we identified 3188 records. Of these, 21 were eligible for inclusion and yielded 117 performance metrics. The median number of metrics reported per paper was 8, range 1–16. Metrics broadly fell into six categories: site potential; recruitment; retention; data collection; trial conduct and trial safety. Conclusions This review identifies a list of metrics to monitor site performance within multicentre randomised trials. Those that would be easy to collect, and for which monitoring might trigger actions to mitigate problems at site level, merit further evaluation.
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Affiliation(s)
- Kate F Walker
- Nottingham Clinical Trials Unit, QMC, Nottingham, NG7 2UH, UK.
| | - Julie Turzanski
- Nottingham Clinical Trials Unit, QMC, Nottingham, NG7 2UH, UK
| | - Diane Whitham
- Nottingham Clinical Trials Unit, QMC, Nottingham, NG7 2UH, UK
| | - Alan Montgomery
- Nottingham Clinical Trials Unit, QMC, Nottingham, NG7 2UH, UK
| | - Lelia Duley
- Nottingham Clinical Trials Unit, QMC, Nottingham, NG7 2UH, UK
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10
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A computationally simple central monitoring procedure, effectively applied to empirical trial data with known fraud. J Clin Epidemiol 2017; 87:59-69. [DOI: 10.1016/j.jclinepi.2017.03.018] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Revised: 02/27/2017] [Accepted: 03/31/2017] [Indexed: 11/20/2022]
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11
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Desmet L, Venet D, Doffagne E, Timmermans C, Legrand C, Burzykowski T, Buyse M. Use of the Beta-Binomial Model for Central Statistical Monitoring of Multicenter Clinical Trials. Stat Biopharm Res 2017. [DOI: 10.1080/19466315.2016.1164751] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Lieven Desmet
- ISBA, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - David Venet
- IRIDIA, Université Libre de Bruxelles, Brussels, Belgium and Breast Cancer Translational Research Lab, Institut Bordet, Université Libre de Bruxelles, Belgium
| | | | - Catherine Timmermans
- Département de Mathématique, Université de Liège, Liège, Belgium
- ISBA, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | | | | | - Marc Buyse
- I-BioStat, Hasselt University, Belgium
- Biostatistics, IDDI, San Francisco, CA
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12
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Affiliation(s)
- Scott D Solomon
- From Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
| | - Marc A Pfeffer
- From Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
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13
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Fraud in clinical trials: complex problem, simple solutions? Int J Clin Oncol 2015; 21:13-4. [PMID: 26577446 DOI: 10.1007/s10147-015-0922-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2015] [Accepted: 10/24/2015] [Indexed: 10/22/2022]
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