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      Risk-Based Monitoring in Clinical Trials: 2021 Update

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          Abstract

          Clinical trial quality depends on ensuring participant safety and data integrity, which require careful management throughout the trial lifecycle, from protocol development to final data analysis and submission. Recent developments—including new regulatory requirements, emerging technologies, and trial decentralization—have increased adoption of risk-based monitoring (RBM) and its parent framework, risk-based quality management (RBQM) in clinical trials. The Association of Clinical Research Organizations (ACRO), recognizing the growing importance of these approaches, initiated an ongoing RBM/RBQM landscape survey project in 2019 to track adoption of the eight functional components of RBQM. Here we present results from the third annual survey, which included data from 4889 clinical trials ongoing in 2021. At least one RBQM component was implemented in 88% of trials in the 2021 survey, compared with 77% in 2020 and 53% in 2019. The most frequently implemented components in 2021 were initial and ongoing risk assessments (80 and 78% of trials, respectively). Only 7% of RBQM trials were Phase IV, while the proportions of Phase I–III trials ranged 27–36%. Small trials (< 300 participants) accounted for 60% of those implementing RBQM. The therapeutic areas with the largest number of RBQM trials were oncology (38%), neurology (10%), and infectious diseases (9%). The 2021 survey confirmed a pattern of increasing RBM/RBQM adoption seen in earlier surveys, with risk assessments, which have broad regulatory support, driving RBQM growth; however, one area requiring further development is implementation of centralized monitoring combined with reductions in source data verification (SDV) and source data review (SDR).

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          Estimation of clinical trial success rates and related parameters

          SUMMARY Previous estimates of drug development success rates rely on relatively small samples from databases curated by the pharmaceutical industry and are subject to potential selection biases. Using a sample of 406 038 entries of clinical trial data for over 21 143 compounds from January 1, 2000 to October 31, 2015, we estimate aggregate clinical trial success rates and durations. We also compute disaggregated estimates across several trial features including disease type, clinical phase, industry or academic sponsor, biomarker presence, lead indication status, and time. In several cases, our results differ significantly in detail from widely cited statistics. For example, oncology has a 3.4% success rate in our sample vs. 5.1% in prior studies. However, after declining to 1.7% in 2012, this rate has improved to 2.5% and 8.3% in 2014 and 2015, respectively. In addition, trials that use biomarkers in patient-selection have higher overall success probabilities than trials without biomarkers.
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            Legal, Regulatory, and Practical Issues to Consider When Adopting Decentralized Clinical Trials: Recommendations From the Clinical Trials Transformation Initiative

            Background Traditional clinical trials are often expensive, inefficient, include selected populations, and can create significant participant burden via travel and other logistical demands. Using new technologies and methodologies to promote a decentralized approach has the potential to improve the efficiency of clinical trials. The Clinical Trials Transformation Initiative (CTTI)—a public–private partnership to improve clinical trials—launched a multi-stakeholder Decentralized Clinical Trials (DCTs) Project to provide recommendations on addressing the actual and perceived legal, regulatory, and practical challenges with DCT design and conduct in the United States. Methods Informed by qualitative group interviews and an expert meeting, CTTI engaged stakeholders to identify key challenges to implementing DCTs and possible solutions. Results The CTTI DCT project team used the interview findings and expert feedback to develop recommendations that will drive broader use of DCTs. Conclusions CTTI’s recommendations cover protocol design, use of telemedicine and mobile healthcare providers, medical product supply chain, investigator delegation and oversight, and safety monitoring considerations. By implementing these recommendations, sponsors, contract research organizations, and others can help advance successful medical product development using mobile technologies and methodologies in DCTs.
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              Risk-adapted monitoring is not inferior to extensive on-site monitoring: Results of the ADAMON cluster-randomised study

              Background According to Good Clinical Practice, clinical trials must protect rights and safety of patients and make sure that the trial results are valid and interpretable. Monitoring on-site has an important role in achieving these objectives; it controls trial conduct at trial sites and informs the sponsor on systematic problems. In the past, extensive on-site monitoring with a particular focus on formal source data verification often lost sight of systematic problems in study procedures that endanger Good Clinical Practice objectives. ADAMON is a prospective, stratified, cluster-randomised, controlled study comparing extensive on-site monitoring with risk-adapted monitoring according to a previously published approach. Methods In all, 213 sites from 11 academic trials were cluster-randomised between extensive on-site monitoring (104) and risk-adapted monitoring (109). Independent post-trial audits using structured manuals were performed to determine the frequency of major Good Clinical Practice findings at the patient level. The primary outcome measure is the proportion of audited patients with at least one major audit finding. Analysis relies on logistic regression incorporating trial and monitoring arm as fixed effects and site as random effect. The hypothesis was that risk-adapted monitoring is non-inferior to extensive on-site monitoring with a non-inferiority margin of 0.60 (logit scale). Results Average number of monitoring visits and time spent on-site was 2.1 and 2.7 times higher in extensive on-site monitoring than in risk-adapted monitoring, respectively. A total of 156 (extensive on-site monitoring: 76; risk-adapted monitoring: 80) sites were audited. In 996 of 1618 audited patients, a total of 2456 major audit findings were documented. Depending on the trial, findings were identified in 18%–99% of the audited patients, with no marked monitoring effect in any of the trials. The estimated monitoring effect is −0.04 on the logit scale with two-sided 95% confidence interval (−0.40; 0.33), demonstrating that risk-adapted monitoring is non-inferior to extensive on-site monitoring. At most, extensive on-site monitoring could reduce the frequency of major Good Clinical Practice findings by 8.2% compared with risk-adapted monitoring. Conclusion Compared with risk-adapted monitoring, the potential benefit of extensive on-site monitoring is small relative to overall finding rates, although risk-adapted monitoring requires less than 50% of extensive on-site monitoring resources. Clusters of findings within trials suggest that complicated, overly specific or not properly justified protocol requirements contributed to the overall frequency of findings. Risk-adapted monitoring in only a sample of patients appears sufficient to identify systematic problems in the conduct of clinical trials. Risk-adapted monitoring has a part to play in quality control. However, no monitoring strategy can remedy defects in quality of design. Monitoring should be embedded in a comprehensive quality management approach covering the entire trial lifecycle.
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                Author and article information

                Contributors
                AmyAdams@parexel.com
                AAdelfio@acrohealth.org
                Brian.Barnes@3DS.com
                Ruth.Berlien@ppd.com
                Danilo.Branco@labcorp.com
                ACoogan@remarquesystems.com
                Lauren.Garson@veeva.com
                Nycole.Ramirez@iconplc.com
                Nicole.Stansbury@biotexconsulting.com
                Jennifer.Stewart@premier-research.com
                Gillian.Worman@oracle.com
                paulajo.butler@iqvia.com
                Debby.brown@ppd.com
                Journal
                Ther Innov Regul Sci
                Ther Innov Regul Sci
                Therapeutic Innovation & Regulatory Science
                Springer International Publishing (Cham )
                2168-4790
                2168-4804
                9 January 2023
                9 January 2023
                : 1-9
                Affiliations
                Association of Clinical Research Organizations (ACRO), 601 New Jersey Ave NW #350, Washington DC, 20001 USA
                Author information
                http://orcid.org/0000-0003-0618-1028
                Article
                496
                10.1007/s43441-022-00496-9
                9829217
                36622566
                6828123c-bb55-4e4f-82b8-f97984ae9916
                © The Author(s) 2023

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 8 October 2022
                : 24 December 2022
                Categories
                Analytical Report

                risk-based monitoring,risk-based quality management,centralized monitoring,clinical trial quality,rbm,rbqm

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