Quality Tolerance Limits (QTLs) represent a significant advancement in clinical trial research, aimed at proactively identifying and managing risks that could jeopardize study validity or patient safety. Mandated by the International Council for Harmonization (ICH) in its guideline for Good Clinical Practice, E6(R2), QTLs were introduced in 2016 as part of a broader shift toward Risk-Based Quality Management (RBQM). Their primary purpose is to establish pre-defined thresholds for key trial parameters, enabling early detection of systemic issues and facilitating prompt corrective action to protect the integrity of trial results.
Since their introduction, the adoption of QTLs by Sponsors and Contract Research Organizations (CROs) has steadily increased.1 However, questions persist regarding the optimal methods for selecting QTL parameters and setting meaningful and operationally feasible thresholds. Key considerations include identifying critical variables most likely to influence trial outcomes, such as patient recruitment rates, protocol deviations, and adverse event frequencies. Additionally, setting thresholds requires a balance between statistical rigor and practicality, ensuring that limits are neither so restrictive as to trigger unnecessary alarms nor so lenient as to allow significant risks to go undetected.
During a session at RBQMLive 2022, CluePoints’ Chief Scientific Officer, Steve Young, led a session featuring prominent experts in clinical trial design and operations. Discussions focused on their motivations for adopting QTLs, personal experiences, and perspectives on the future evolution and impact of QTLs in clinical research.
QTLs: An Ethical Imperative
In addition to fulfilling regulatory requirements, Sponsors are increasingly adopting QTLs as a proactive measure to enhance the quality and reliability of clinical trials. According to Joanna Florek-Marwitz, Data Management, Data Science, and RBQM Senior Leader at UCB, this adoption reflects a commitment to ethical responsibility and best practices in clinical research. “The primary motivation for implementing this is to conduct studies safely and to generate reliable evidence,” said Florek-Marwitz.
Marion Wolfs, Senior Director of Risk Management and Central Monitoring at Janssen, concurred, emphasizing the significant ethical dimension inherent in adopting and implementing QTLs. “We want to make sure we work in the safest possible way, that we develop medications that are as safe as possible, and that they do what we intend them to do,” said Wolfs, adding that the process of selecting QTL parameters and thresholds compels study teams to engage more deeply with their scientific objectives and critically evaluate their underlying assumptions.
It ensures that study teams remain focused on the trial’s most critical and impactful aspects, explained Steve Gilbert, Pfizer’s Senior Director of Statistics. “We work in partnership with our patients. We’re responsible for keeping them safe and ensuring their commitment to our trial isn’t wasted. Running a study but can’t get a clean readout because of quality issues is not ethical or fair to our patients.”
Selecting QTL Parameters
In response to questions regarding the selection of QTL parameters, participants emphasized the importance of tailoring parameters to the specific study and conducting a comprehensive risk assessment to identify critical variables.
“We have whiteboarding sessions where everybody gathers to find and rank all the study risks. Then, we generate the study-specific Key Risk Indicators (KRIs), and the QTLs will come from those,” said Gilbert, adding that the process begins with identifying critical-to-quality factors, which serve as the foundation for parameter selection.
All three speakers noted that their organizations maintain a library of commonly used QTLs. Florek-Marwitz explained that this approach facilitates implementation and minimizes the burden on study teams. However, these pre-defined QTLs are typically more generic and require further refinement by the study team to align with the specific needs of each trial.
“The QTL has to be connected to the study’s scientific question,” stated Wolfs. She explained that while the library may propose a general parameter, such as monitoring missed endpoints, it’s ultimately the study team’s responsibility to identify a study-specific endpoint that contributes meaningfully to the final analysis. “The QTLs we select should help us answer our scientific question,” she added.
All three speakers noted that their organizations typically implement between two and four QTLs per trial, complemented by numerous KRIs to monitor risk at the site level.
Setting QTL Thresholds
Florek-Marwitz characterized the establishment of QTL thresholds as a collaborative, cross-functional process led by subject matter experts.
At UCB, study teams utilize a comprehensive database of historical data, which can be searched by key categories such as therapeutic area, compound, study design type, and study phase. This data serves as a foundation for discussions with medics, statisticians, and other subject matter experts, who collectively establish QTL thresholds through a collaborative, multidisciplinary process.
A similar approach is employed across all three speakers’ organizations. Wolfs explained that Janssen complements this process by running various scenarios to evaluate how variations in the identified QTL parameters impact data quality at specific breakpoints, ensuring robust and meaningful thresholds.
However, all three speakers underscored the limitations of relying exclusively on historical data. One key challenge is that the data may be based on assumptions that differ from those guiding the current study’s scientific questions. Additionally, historical data may become outdated over time. Wolfs highlighted the importance of assessing how relevant and applicable historical data remains in the current context, stating, “things change over time,” and it’s essential to evaluate whether the data is still appropriate for use.
Regulatory requirements further influence QTL selection and threshold setting. Gilbert noted, for example, that in certain therapeutic areas, regulatory agencies like the FDA may have strict expectations, such as not accepting results if more than 20% of patients are lost to follow-up. In such cases, a “lost to follow-up” QTL could be particularly relevant and valuable for ensuring compliance and maintaining data integrity.
QTLs & the Future
Implementing QTLs represents an ongoing process of continual learning within the clinical research industry. Experts express confidence that organizations will progressively refine their methodologies for identifying and defining the most impactful parameters and thresholds, ultimately maximizing the effectiveness and value of QTLs in enhancing trial quality and reliability.
As the sector gains proficiency in applying QTLs, Florek-Marwitz anticipates that this approach will expand into additional areas of clinical research. “We’ve established risk-based monitoring and have all this experience. I see an opportunity to translate that into other areas, such as clinical data management. It could change our thinking from fixing issues to preventing and mitigating risk.”
Similarly, Gilbert envisions QTLs becoming integral to routine operations, describing the eventual transition as “business as usual.” He further suggested that advancements in automation will streamline the process, enabling QTL and KRI parameter selection and threshold setting to evolve into “one seamless process.”
The full on-demand RBQMLive session is available here for a more in-depth exploration of this topic.
Clinical Trial Risk Detection & Research Analytics Solutions
QTLs are transforming how clinical trials identify and manage risk, enabling teams to uphold the highest patient safety and data integrity standards. At CluePoints, we understand that successful implementation of QTLs requires not only sound methodology but also the right tools to ensure systematic monitoring and accountability.
Our QTL module provides a robust, automated solution for early detection of threats to trial validity and patient safety. By monitoring critical metrics such as the rate of patients prematurely discontinuing study drug treatment, patients lost to follow-up, or early terminations before endpoint milestones, our platform empowers study teams to identify and address issues before they escalate.
Comprehensive documentation within our platform ensures that deviations from pre-defined QTLs, along with the corresponding justification and evidence, are fully recorded and easily accessible. This streamlined process supports compliance, facilitates corrective action, and enables a proactive approach to risk management in clinical trials.
As the clinical research industry evolves, CluePoints remains committed to providing the tools and insights to ensure trials are conducted safely, efficiently, and with the utmost integrity. Download “The Ultimate Guide to RBQM” to deepen your knowledge, and don’t hesitate to contact CluePoints here with any questions or feedback.
Reference:
- Bhagat, R., Bojarski, L., Chevalier, S., Görtz, D. R., Le Meignen, S., Makowski, M., … & Turri, S. (2021). Quality tolerance limits: framework for successful implementation in clinical development. Therapeutic innovation & regulatory science, 55(2), 251-261.
