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Dirks A, Florez M, Torche F, Young S, Slizgi B, Getz K. Comprehensive Assessment of Risk-Based Quality Management Adoption in Clinical Trials. Ther Innov Regul Sci 2024; 58:520-527. [PMID: 38366107 PMCID: PMC11043178 DOI: 10.1007/s43441-024-00618-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 01/08/2024] [Indexed: 02/18/2024]
Abstract
BACKGROUND Risk-based monitoring (RBM) and risk-based quality management (RBQM) offer a compelling approach to increase efficiency, speed and quality in clinical trials by prioritizing and mitigating risks related to essential safety and efficacy data. Since 2013, the FDA and EMA have encouraged the use of RBM/RBQM, however adoption has been slow with limited understanding of the barriers to adoption. METHODS The Tufts Center for the Study of Drug Development conducted an online survey among pharmaceutical, biotechnology, and contract research organizations and gathered 206 responses on 32 distinct RBQM practices. RESULTS On average, companies implemented RBQM in 57% of their clinical trials. Lower levels of adoption were observed among companies conducting fewer than 25 trials annually (48%) compared to those conducting more than 100 trials annually (63%). Primary barriers to adoption include lack of organizational knowledge and awareness, mixed perceptions of the value proposition of RBQM, and poor change management planning and execution. Insights into improving the level of adoption are discussed.
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Affiliation(s)
- Abigail Dirks
- Tufts Center for the Study of Drug Development, Tufts School of Medicine, Boston, MA, USA.
| | - Maria Florez
- Tufts Center for the Study of Drug Development, Tufts School of Medicine, Boston, MA, USA
| | | | | | - Brian Slizgi
- Price Waterhouse Coopers (PWC), London, United Kingdom
| | - Kenneth Getz
- Tufts Center for the Study of Drug Development, Tufts School of Medicine, Boston, MA, USA
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Kornek D, Menichelli D, Leske J, Hofmann M, Antkiewicz D, Brandt T, Ott OJ, Lotter M, Lang-Welzenbach M, Fietkau R, Bert C. Development and clinical implementation of a digital system for risk assessments for radiation therapy. Z Med Phys 2023:S0939-3889(23)00092-2. [PMID: 37666699 DOI: 10.1016/j.zemedi.2023.08.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 08/08/2023] [Accepted: 08/09/2023] [Indexed: 09/06/2023]
Abstract
Before introducing new treatment techniques, an investigation of hazards due to unintentional radiation exposures is a reasonable activity for proactively increasing patient safety. As dedicated software is scarce, we developed a tool for risk assessment to design a quality management program based on best practice methods, i.e., process mapping, failure modes and effects analysis and fault tree analysis. Implemented as a web database application, a single dataset was used to describe the treatment process and its failure modes. The design of the system and dataset allowed failure modes to be represented both visually as fault trees and in a tabular form. Following the commissioning of the software for our department, previously conducted risk assessments were migrated to the new system after being fully re-assessed which revealed a shift in risk priorities. Furthermore, a weighting factor was investigated to bring risk levels of the migrated assessments into perspective. The compensation did not affect high priorities but did re-prioritize in the midrange of the ranking. We conclude that the tool is suitable to conduct multiple risk assessments and concomitantly keep track of the overall quality management activities.
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Affiliation(s)
- Dominik Kornek
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054 Erlangen, Germany.
| | | | - Jörg Leske
- IBA Dosimetry GmbH, 90592 Schwarzenbruck, Germany.
| | | | | | - Tobias Brandt
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054 Erlangen, Germany.
| | - Oliver J Ott
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054 Erlangen, Germany.
| | - Michael Lotter
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054 Erlangen, Germany.
| | - Marga Lang-Welzenbach
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054 Erlangen, Germany.
| | - Rainer Fietkau
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054 Erlangen, Germany.
| | - Christoph Bert
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054 Erlangen, Germany.
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