Key risk indicators in clinical studies

Guest post by Safeer Khan, Lecturer at Department of Pharmaceutical Sciences, Government College University, Lahore, Pakistan

Clinical trials are essential for advancing medical science, yet they are inherently complex and involve a wide range of risks. As a result, effective risk management in clinical trials is crucial to ensuring their successful completion.

Among the various approaches to managing these risks, clinical trials Key Risk Indicators (KRIs) have become essential tools. KRIs are precise, measurable metrics that serve as early alerts for potential risk exposures in clinical studies. They provide objective data points that help identify when certain aspects of a trial are trending toward risk, allowing for timely corrective actions.

It’s important to distinguish KRIs from Key Performance Indicators (KPIs); while KPIs measure achievement of predefined goals—such as meeting patient recruitment targets or completing scheduled monitoring visits—KRIs focus exclusively on identifying deviations or risks that could negatively impact study outcomes.

Why KRIs Matter in Clinical Studies

1. Enhancing Patient Safety: KRIs are essential for safeguarding participants by enabling the early detection of safety risks. For example, continuous monitoring of adverse event rates and serious adverse events (SAEs) allows clinical teams to promptly identify emerging safety concerns and facilitate timely interventions.
2. Ensuring Data Integrity and Reliability: Maintaining high-quality data is critical for the credibility of clinical trials. KRIs such as data query rates and unresolved inconsistencies help detect errors early, improving data accuracy and completeness.
3. Supporting Regulatory Compliance: Regulatory bodies require sponsors to demonstrate robust risk management practices. KRIs help flag potential compliance issues well before inspections or audits, minimizing findings that can delay approvals or require costly remediation.
4. Preventing Trial Disruptions and Delays: Clinical trial risk assessment through KRIs enables trial teams to act proactively, preventing costly disruptions such as site closures, patient recruitment failures, or data integrity breaches.
5. Optimizing Resource Allocation: By concentrating efforts on high-risk areas flagged by KRIs, sponsors can optimize the allocation of monitoring and operational resources. This focused strategy minimizes redundant site visits and oversight activities, resulting in significant cost savings within clinical trial management budgets.
6. Improving Overall Performance and Success: Incorporating KRIs into a clinical trial risk assessment enhances clinical trial success by directing oversight to the most critical areas. An analysis of 212 clinical studies with 1,676 sites demonstrated that KRIs led to measurable quality improvements in 82.9% of sites. This study also showed that targeted interventions based on KRI alerts improved data quality metrics by over 70% on average at affected sites [1].

Characteristics of Effective KRIs in Clinical Trials

1. Specificity and Sensitivity: Effective KRIs must strike a balance between being sensitive enough to detect emerging risks early and specific enough to offer actionable insights. For instance, tracking particular types of adverse events can reveal treatment-specific safety issues.
2. Measurability: KRIs should be based on clear, quantitative data that can be reliably collected, analyzed, and interpreted. Metrics related to patient demographics, adverse event rates, and protocol adherence need to be well-defined and standardized to provide meaningful insights.
3. Relevance: KRIs must align closely with the trial’s specific goals and endpoints to ensure focus on the most critical risks. This relevance helps stakeholders prioritize their attention and resources efficiently. For example, a patient recruitment rate is a key indicator in studies facing enrollment challenges but may be less important in bioequivalence trials.
4. Simplicity: While comprehensive monitoring is important, KRIs should be easy to understand and interpret by all stakeholders involved. Overly complicated indicators can hinder communication and delay necessary actions.
5. Consistency: KRIs need to be defined and measured uniformly across different sites and studies. Consistent application enables comparison and aggregation of risk data, supporting benchmarking and ongoing improvements in risk management in clinical trials.

Step-by-Step Process of Developing KRIs

1. Gain a Deep Understanding of the Clinical Trial: Start by comprehensively reviewing the trial’s design, objectives, participant demographics, regulatory requirements, and operational processes. This detailed understanding helps pinpoint where risks are most likely to emerge and which areas require heightened monitoring.
2. Classify Risk Categories: Organize potential risks according to the different phases and functions of the clinical trial. Typical categories include patient safety, data quality, operational efficiency, and compliance with regulations. This classification sets the foundation for identifying targeted KRIs.
3. Involve Relevant Stakeholders: Work closely with multidisciplinary teams such as clinical operations, data management, safety oversight, quality assurance, and regulatory affairs. Their expertise is critical for recognizing pertinent risk factors and ensuring KRIs address practical challenges encountered during the trial.
4. Specify Measurable Risk Factors: Break down broad risk categories into precise, quantifiable risk factors that relate directly to the study. Examples might include rates of patient dropouts, frequency of protocol deviations, number of adverse events, or volume of data queries.
5. Define Thresholds or Alert Triggers: Set explicit thresholds or triggers for each KRI to indicate when a risk level requires attention. These benchmarks should be informed by past data, regulatory standards, or expert opinion to prompt timely and effective responses.
6. Identify Data Sources and Metrics: Determine trustworthy sources for KRI data collection, such as electronic data capture systems, safety reporting databases, monitoring logs, and site performance reports. Establish protocols for when and how data will be gathered and analyzed to ensure accuracy and relevance.
7. Perform Ongoing Data Analysis: Regularly review KRI data to uncover trends, irregularities, or deviations that suggest rising risk. Use statistical tools and data visualization techniques to aid in interpreting the information and guiding decision-making.
8. Document the Comprehensive KRI Framework: Assemble all components of the KRI program—including risk categories, specific indicators, threshold values, data sources, and action plans—into a detailed framework. Proper documentation enhances transparency, supports regulatory compliance, and facilitates continuous refinement of risk management strategies.

Common Key Risk Indicators in Clinical Trials

Trial Design Considerations

A well-constructed clinical trial protocol is fundamental to research success, yet risks embedded in the study design can jeopardize outcomes if left unaddressed. Essential elements to monitor include the complexity of the protocol, feasibility of endpoints, accuracy of sample size and power calculations, proper execution of randomization and blinding, and study duration. Early evaluation of these design risks enables sponsors to proactively address potential challenges before they adversely affect trial results.

One way to assess your protocol for design risks is to drag and drop it into the free Clinical Trial Risk Tool. You will see a list of recommendations around the statistical analysis plan, sample size, etc, like in the screenshot below.

Recommendations produced by the Clinical Trial Risk Tool for a protocol

Patient Recruitment Rate

This indicator measures the pace and effectiveness of enrolling participants against the planned recruitment timeline. Timely recruitment is critical; delays can cause costly setbacks and compromise the statistical power needed for conclusive results. Patient recruitment rate is especially important in trials involving rare diseases, where patient availability is limited. You can read more in Allucent’s enlightening post on Rare Disease Clinical Trials: Challenges, Strategies, and Solutions for Success. Tracking recruitment performance across sites helps identify bottlenecks or operational hurdles early, allowing sponsors to adjust strategies by adding new sites or enhancing recruitment efforts.

Protocol Deviations

Protocol deviations occur when study sites or investigators fail to follow the approved clinical protocol. There is (FDA Draft Guidance)[https://www.fda.gov/media/184745/download] offering recommendations to assist sponsors, investigators, and institutional review boards (IRBs) in defining, identifying, and reporting protocol deviations during clinical trials. A rise in protocol deviations may indicate training deficiencies, unclear protocol language, or inadequate site oversight. Monitoring this KRI closely allows for timely corrective measures including retraining, clarifying protocols, or increasing monitoring activities to safeguard trial integrity.

Data Query Rate

This KRI reflects the frequency of data clarifications or corrections requested from study sites. Elevated query rates can point to data quality issues like entry errors, misunderstandings of case report forms, or systemic problems in data capture. High volumes of data queries can delay key milestones, including database lock, and jeopardize the reliability of trial findings. Monitoring data query rates helps identify underperforming sites or problematic processes, enabling targeted corrective actions such as additional training or system improvements.

Patient Dropout Rate

Patient dropout rate tracks the percentage of participants who withdraw from the study before completion. Elevated dropout rates threaten study validity by reducing sample size and potentially introducing bias, especially if dropouts relate to adverse treatment effects. Monitoring this indicator assists in uncovering causes such as adverse events, logistical challenges, or lack of patient engagement. Based on dropout trends, trial teams can implement retention strategies like enhanced communication, patient support programs, or improved follow-up protocols to maintain participant continuity.

Serious Adverse Events (SAEs)

The occurrence and pattern of serious adverse events provide a direct measure of patient safety and treatment risk. Unexpected increases in SAEs require immediate investigation and may prompt protocol amendments, increased monitoring, or even temporary study suspension. Continuous tracking of SAEs also ensures compliance with regulatory reporting requirements and reinforces participant protection.

Site Monitoring Visit Frequency

This KRI monitors how consistently site visits occur as scheduled. Regular monitoring visits are crucial for ensuring protocol adherence, validating data accuracy, and protecting participant safety. Missed or delayed visits can allow risks to go unnoticed and escalate. By tracking visit frequency, sponsors can focus oversight on high-risk sites and optimize resource use, particularly within a risk-based monitoring framework.

Future Trends in KRI Management

The adoption of KRIs in clinical trials has shown a significant and consistent rise over recent years. A detailed survey examining 5,987 ongoing clinical trials in 2020 revealed that nearly 77% of these studies incorporated at least one component of Risk-Based Monitoring (RBM), with KRIs being a prominent feature [2]. This growing trend highlights the increasing recognition of KRIs as essential tools for proactive risk management in clinical trials.

Looking ahead, the future of KRIs in clinical research is intimately tied to advancements in technology [3]. Artificial intelligence (AI) and machine learning stand at the forefront of this transformation, offering the ability to analyze vast and complex datasets far beyond human capacity. These technologies enable more accurate prediction of emerging risks, allowing for dynamic adjustment of KRI definitions and thresholds. This adaptive approach leads to earlier detection of potential issues and more focused interventions, significantly enhancing the efficiency and effectiveness of clinical trial monitoring.

In addition to theoretical promise, several AI-powered tools have already been developed to anticipate risks in clinical studies. For instance, the Clinical Trial Risk Tool utilizes AI to identify risks associated with trial design and the possibility that a trial may conclude without producing meaningful results. The tool also estimates the financial cost associated with the trial. In addition, it also offer the practical recommendations to mitigate the identified risks.

Conclusion

KRIs have become indispensable tools in managing the inherent complexities and risks of clinical trials. By providing early, measurable signals of potential issues, KRIs empower trial teams to safeguard patient safety, maintain data integrity, and ensure regulatory compliance. Their strategic use helps prevent costly disruptions and optimizes resource allocation, ultimately enhancing overall trial performance and success.

References

1. de Viron, S., et al., Does central monitoring lead to higher quality? an analysis of key risk indicator outcomes. Therapeutic Innovation & Regulatory Science, 2023. 57 (2): p. 295-303.

2. Stansbury, N., et al., Risk-Based Monitoring in Clinical Trials: Increased Adoption Throughout 2020. Therapeutic Innovation & Regulatory Science, 2022. 56 (3): p. 415-422.

3. Kolluri, S., et al., Machine learning and artificial intelligence in pharmaceutical research and development: a review. The AAPS journal, 2022. 24: p. 1-10.