Quality Tolerance Limits: A General Guidance for Parameter Selection and Threshold Setting.

The past years have sharpened the industry's understanding of a Quality by Design (QbD) approach toward clinical trials. Using QbD encourages designing quality into a trial during the planning phase. The identification of Critical to Quality (CtQs) factors and specifically Critical Data and Processes (CD&Ps) is key to such a risk-based monitoring approach. A variable that allows monitoring the evolution of risk regarding the CD&Ps is called a Quality Tolerance Limit (QTL) parameter. These parameters are linked to the scientific question(s) of a trial and may identify the issues that can jeopardize the integrity of trial endpoints. This paper focuses on defining what QTL parameters are and providing general guidance on setting thresholds for these parameters allowing for the derivation of an acceptable range of the risk.

Risk-Based Monitoring in Clinical Trials: Increased Adoption Throughout 2020.

With the emergence of new technologies for data collection, the continued impact of the COVID-19 pandemic, and the increasing number of partially or fully decentralized clinical trials (DCTs), the importance of risk-based monitoring (RBM) and the larger risk-based quality management (RBQM) framework in clinical trial management is increasing. RBM and RBQM focus on the detection of events or trends that impact trial quality in terms of participant safety and data integrity. In 2019, the Association of Clinical Research Organizations (ACRO) began a landscape survey of RBM/RBQM implementation in ongoing clinical trials. Initial results of this survey, representing full-year data for 2019, were reported previously. Here, we present full-year landscape data for 2020 drawn from 5,987 clinical trials ongoing at the end of 2020, including 908 new studies started that year. Of these trials, 77% implemented at least one RBM/RBQM component, an increase from 47% for studies ongoing at the end of 2019. We also observed increased implementation for three of the five RBM components included in the survey. Centralized monitoring decreased nominally in 2020 compared with 2019. Although the percentages of 2020 trials incorporating reduced source data verification (SDV) and reduced source data review (SDR) increased from 2019 to 2020, these numbers are still low considering the large percentage of trials implementing at least one RBQM component. In the current clinical trial landscape, as more DCTs are launched and new data collection technologies are implemented, there remains a pressing need for greater use of centralized monitoring coupled with reductions in SDR/SDV and, ultimately, greater adoption of RBM and RBQM.

Risk-Based Monitoring in Clinical Trials: Past, Present, and Future.

Risk-based monitoring (RBM) is a powerful tool for efficiently ensuring patient safety and data integrity in a clinical trial, enhancing overall trial quality. To better understand the state of RBM implementation across the clinical trial industry, the Association of Clinical Research Organizations (ACRO) conducted a landscape survey among its member companies across 6,513 clinical trials ongoing at the end of 2019. Of these trials, 22% included at least 1 of the 5 RBM components: key risk indicators (KRIs), centralized monitoring, off-site/remote-site monitoring, reduced source data verification (SDV), and reduced source document review (SDR). The implementation rates for the individual RBM components ranged 8%-19%, with the most frequently implemented component being centralized monitoring and the least frequently implemented being reduced SDR. When the COVID-19 pandemic emerged in early 2020, additional data were collected to assess its impact on trial monitoring, focusing specifically on trials switching from on-site monitoring to off-site/remote-site monitoring. These mid-pandemic data show that the vast majority of monitoring visits were on-site in February 2020, but an even higher percentage were off-site in April, corresponding with the first peak of the pandemic. Despite this shift, similar numbers of non-COVID-related protocol deviations were detected from February through June, suggesting little or no reduction in monitoring effectiveness. The pre- and mid-pandemic data provide two very different snapshots of RBM implementation, but both support the need to promote adoption of this approach while also highlighting an opportunity to capitalize on the recent shift toward greater RBM uptake in a post-pandemic environment.

Analysis of professional competencies for the clinical research data management profession: implications for training and professional certification.

OBJECTIVE: To assess and refine competencies for the clinical research data management profession. MATERIALS AND METHODS: Based on prior work developing and maintaining a practice standard and professional certification exam, a survey was administered to a captive group of clinical research data managers to assess professional competencies, types of data managed, types of studies supported, and necessary foundational knowledge. RESULTS: Respondents confirmed a set of 91 professional competencies. As expected, differences were seen in job tasks between early- to mid-career and mid- to late-career practitioners. Respondents indicated growing variability in types of studies for which they managed data and types of data managed. DISCUSSION: Respondents adapted favorably to the separate articulation of professional competencies vs foundational knowledge. The increases in the types of data managed and variety of research settings in which data are managed indicate a need for formal education in principles and methods that can be applied to different research contexts (ie, formal degree programs supporting the profession), and stronger links with the informatics scientific discipline, clinical research informatics in particular. CONCLUSION: The results document the scope of the profession and will serve as a foundation for the next revision of the Certified Clinical Data Manager TM exam. A clear articulation of professional competencies and necessary foundational knowledge could inform the content of graduate degree programs or tracks in areas such as clinical research informatics that will develop the current and future clinical research data management workforce.