What is Risk Based Monitoring?

Risk Based Monitoring (RBM) is a clinical trial-monitoring technique that fulfils regulatory requirements but moves away from 100% source data verification (SDV) of patient data. It employs various tools, platforms and dashboards to identify signals, which indicate potential issues with (for example) trial conduct, safety, data integrity, compliance and enrolment. The main difference between risk-based monitoring and traditional monitoring is that RBM focuses on identifying and addressing high-risk areas through targeted data analysis and centralised oversight, whereas traditional monitoring relies on frequent onsite visits and 100% SDV across all sites, regardless of risk.

RBM means that the volume and frequency of monitoring is reduced, as data are only verified at high-risk sites based on triggered events or certain pre-defined critical events in the study. The quality of the trial data can be improved by identifying, assessing, monitoring and mitigating risks.

Regulatory authorities recognise the potential of RBM to improve the conduct of clinical trials of all phases and have published guidance documents on RBM, encouraging sponsors to apply risk-based approaches to study management. RBM is not yet absolutely mandated by any regulator; however, both FDA and EMA accept that less data review is appropriate in lower risk studies and in lower risk periods of an initially higher risk study.

The monitoring can be performed centrally and is targeted to patients or sites based on outlying, inlying, erroneous, operationally deficient or potentially fraudulent data.

The Rise of RBM

So, with strong theory and marketing around RBM but companies across the industry still being cautious about implementing the approach, what has the future got in store for RBM? Below shows the increased adoption of risk-based monitoring.

The adoption of Risk-Based Quality Management (RBQM) components in in clinical trials has shown significant growth, with usage increasing from 53% in 2019 to 88% in 2021.

Pillars of RBM

FDA guidance outlines three steps in an RBM approach:

Detection of Critical Data and Processes

For critical data to be flagged as causing a potential risk, a sponsor must firstly identify the expected/acceptable values and parameters. Intelligence from previous studies can be drawn upon to help identify and quantify these metrics as high, medium or low risk. 

The mitigation for a given identified risk can change throughout the trial and the categorisation of the risk does not need to be entered when initially working with the risk assessment categorisation tool (RACT). Examples of high-risk data points include any of those which impact patient safety, and the use of trial sites with little or naive experience of clinical trials or endpoint data categories. 

Perform a Risk Assessment

When risks have been identified they can be visualised using a 'traffic light system' for clinical operations. To assist in conveying these findings to site, a risk-based monitoring assessment must be conducted, which involves investigation of the risk and its origin (source data review), and the implementation of risk mitigation methodologies, enacting corrective action to prevent further risks and resolve current risks. These may include re-education of the site, motivational visits, amending a recruitment plan, escalation to a global level, or in extreme cases, the issuing of warning letters. 

Develop a Monitoring Plan to Incorporate a Risk-Based Approach

At Quanticate we can provide the consultancy required for the change management aspect of RBM. We recommended consideration of RBM strategies for your trial and development of the required monitoring plan early in the development process.

FDA guidance states that a clinical/trial monitoring plan (CMP/TMP) must ‘’describe the monitoring methods, responsibilities and requirements of the trial”. This critical document will stipulate which data points need to be monitored, and the frequency of monitoring coupled with communication and escalation plans for all stakeholders involved in the trial.

Risk-Based Monitoring Template

Below are two example tables for implementing a risk-based monitoring (RBM) plan. The first outlines source data verification (SDV) and source data review (SDR) thresholds based on risk levels and trial phases. The second table identifies potential risks, categorised by issue areas, and provides examples to guide mitigation strategies.

The Role of Statisticians and Programmers in RBM

Statisticians and programmers are crucial to Risk-Based Monitoring (RBM) and Centralised Monitoring (CM), using their expertise to interpret trial data, identify risks, and optimise monitoring. They help detect issues like training gaps, errors, or fraud, improving data quality while reducing onsite visits.

Lab data, such as haematology, biochemistry, and urinalysis, requires thorough analysis to detect errors. Both univariate and multivariate methods are used to identify outliers.

Univariate Outliers: Single-variable anomalies are detected using the Interquartile Range (IQR), Standard Deviation (SD), or Grubbs’ test, which flag data points outside set thresholds. Adjustments, such as Grubbs' test, adapt to the data's distribution.

Multivariate Outliers: For datasets involving multiple variables, techniques like Euclidean Distance (ED) and Mahalanobis Distance (MD) identify outliers by analysing correlations between variables.

Euclidean Distance (ED): Measures an individual’s deviation from the mean.

Mahalanobis Distance (MD): Accounts for variable correlations, with results following a chi-squared distribution, allowing identification of significant outliers.

Scatterplots and boxplots help identify clusters of outliers by site or demographic, guiding onsite monitors. MD's chi-squared distribution offers further insights, highlighting unusual data patterns for deeper analysis.

Cost and Efficiency Benefits of RBM

Risk-Based Monitoring (RBM) reduces clinical trial costs by minimising the need for frequent onsite visits, which can account for up to 30% of trial expenses. By shifting to remote and centralised monitoring, sponsors save resources while allowing CRAs to focus on high-risk sites and critical data points. This targeted approach addresses current CRA shortages and optimises their workload. Additionally, RBM reduces reliance on the traditional 100% Source Data Verification (SDV) model, prioritising monitoring efforts where they are most needed without compromising data integrity.

Challenges in Adopting RBM

Knowing when to use Risk-Based Monitoring technology is critical for trial efficiency. Despite its advantages, RBM adoption remains slow due to concerns about data quality and the perceived reliability of traditional 100% SDV practices. Many organisations are hesitant to shift away from familiar workflows and lack the training or confidence to adopt RBM tools and techniques. Overcoming these barriers requires education on RBM’s effectiveness and alignment with regulatory guidance to build trust in its methodologies.

The Role of Central Data Analytics in RBM

Central Data Analytics (CDA) underpins RBM by enabling real-time analysis of trial data to identify trends, anomalies, and high-risk sites. Tools such as Electronic Data Capture (EDC) systems and Clinical Trial Management Systems (CTMS) collect and organise data from diverse sites, providing actionable insights for targeted monitoring. Integrating these technologies enhances RBM’s efficiency and ensures proactive oversight of clinical trial performance.

Conclusion

The RBM strategies above is becoming integral concepts in pharmaceutical clinical research, which has the potential to reduce clinical costs and improve data quality, while simultaneously reducing time to approval of an Investigational Medicinal Product (IMP). The FDA are now, as an integral part of their approval process, performing statistical analyses on all data sets submitted for approval; hence, it is strongly advised that all studies both ongoing and about to commence, integrate some degree of RBM and statistical monitoring to meet these implemented standards.