Proactively Identifying and Managing Risks
Quality Tolerance Limits (QTLs) were mandated in ICH E6(R2). Reflecting industry feedback, E6(R3) acknowledged QTLs as a tool to support the control of risks to Critical-to-Quality (CtQ) factors.
In this article, we explore how QTLs support risk management and how to turn acceptable ranges into actionable triggers.
It is the third chapter in our ICH E6(R3) Demystified series which translates each core principle and training module into practical steps for sponsors, CROs, and quality leaders.
Across seven installments, we’ll show how to move from theory to action using data-driven oversight and explainable analytics – anchored in the real-world application of CluePoints’ Central Monitoring Platform (CMP).
Supporting Risk Management
QTLs were introduced in 2016 as part of a broader shift toward Risk-Based Quality Management (RBQM). The aim was to support the proactive identification and management of risks that could jeopardize study validity or patient safety. QTLs 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.
While KRIs monitor emerging issues at the site or country level, QTLs operate at the study level. A QTL excursion indicates a potential systemic issue, which is why regulators often review QTL documentation closely during inspections.
E6(R3) Annex 3.10.1.3 Risk Control emphasized that risk control should be proportionate to the importance of the risk to participants’ rights, safety and wellbeing and the reliability of trial results. It acknowledges QTLs as a useful tool to define pre-specified acceptable ranges and control CtQ risks.
An Ethical Imperative
In addition to fulfilling regulatory requirements, there is an ethical dimension to adopting QTLs.
QTLs are a proactive measure to enhance the quality and reliability of clinical trials. They help ensure studies are conducted in the safest way possible, generate reliable evidence and develop medications with proactive safety and efficacy oversight.
Selecting QTL parameters forces study teams to think deeply about scientific objectives and the assumptions underpinning trial design, reinforcing a culture of quality-by-design.
Selecting QTL Parameters and Thresholds
QTL parameters should be tailored to each specific study. This requires a comprehensive risk assessment to identify CtQ factors and study-specific Key Risk Indicators (KRIs).
A library of commonly used QTLs can facilitate implementation and minimize burden on study teams. However, these generic QTLs require refinement to align with each trial’s unique context and endpoints.
QTL parameters should always map directly to a CtQ factor. A QTL that does not connect to a CtQ adds monitoring burden that is not proportionate to the study risks, a key principle in both E6(R3) and E8(R1).
Threshold setting should be collaborative and cross-functional. While historical data can inform expected values and thresholds, relevance must be critically assessed. Scenario testing can help teams understand where risk levels would begin impacting data reliability.
Regulatory expectations can also guide selection. For example, if the FDA has strict expectations around “lost to follow-up,” this may be a relevant QTL.
Creating Actionable Triggers
A QTL only becomes meaningful when paired with a predefined, actionable response plan. Teams should define:
- What constitutes an excursion
- Exceeding the threshold
- Sustained trends predicting a future breach
- Unexpected shifts that require investigation
- Who is notified and when
- Clear roles for detection, review, escalation, and approval.
- How excursions are investigated. CMP provides:
- Contributing sites/patients
- Underlying variables
- Statistical evidence
- Trend analysis
- Comparative performance across sites
- CAPA actions to consider
- Protocol clarification or retraining
- Targeted site outreach
- Data correction
- Process adjustments
- Documentation updates
- How to document as E6(R3) expects full traceability:
- The excursion
- Root cause analysis
- Actions taken
- Effectiveness evaluation
CMP’s QTL module supports this documentation, ensuring consistency and completeness.
How CluePoints Can Help
CMP includes a robust QTL module that supports systematic threshold monitoring and detects excursions at the study level. Investigation of these excursions is supported through linked KRIs, which provide structured drill-down analytics to understand contributing sites, underlying data patterns, and emerging trends.
QTL visualizations help teams interpret whether observed deviations are meaningful in the context of sample size and study timing. Because QTLs are linked to CtQ factors within the Risk Assessment module, teams can seamlessly connect study-level thresholds with site-level KRIs, enabling targeted follow-up through the integrated Signals & Actions engine.
What Comes Next
In Part 4, we’ll explore how to design proportionate oversight anchored in centralized monitoring and align outputs to plans and inspection narratives.
Catch Up
- Part 1 of our ICH E6(R3) Demystified series focused on the shift to principle-based, proportionate oversight and the importance of building quality in. [Catch up on Part 1 here.]
- Part 2 focused on how to identify and document CtQ factors that drive risk management and create a CtQ register that connects design to oversight. [Catch up on Part 2 here.]
Closing Thought
E6(R3) emphasized the importance of QTLs to proactively identify and manage risk. However, they are more than just a regulatory requirement. They enable teams to uphold the highest standards of patient safety, data integrity, and regulatory confidence.
CluePoints provides the statistical engine, visualizations, and traceability required to implement QTLs successfully, supporting confident, inspection-ready oversight.
