Data quality and integrity are key aspects of Good Clinical Practice (GCP) and sponsors are expected by regulators to demonstrate oversight of clinical studies to ensure proper conduct, safety of study subjects and accuracy and completeness of the clinical study data. Risk Based Monitoring is an approach that aims to implement improved and more efficient approaches to clinical trial design, conduct, oversight, recording, and reporting, while continuing to ensure human subject protection and reliability of trial results.
Oversight of a clinical study has traditionally included on-site data monitoring and performing Source Data Verification (SDV), i.e. confirming that the data in the CRF accurately reflects the source notes.
Evolutions in technology and risk management processes offer new opportunities to increase efficiency and focus on relevant activities. The Risk Based Monitoring approach identifies those areas of the trial at greatest risk and implements targeted measures and controls to monitor and address the quality of the trial.
Key Risk Indicators (KRIs) consist of critical data and other study variables or operational data (e.g. query metrics) and are used to detect potential issues at a site, country or trial level, which require mitigation. They use operational data (e.g. data management queries response rate) to highlight site level concerns but may have limited direct impact on subject safety and data integrity at the trial level. They may evolve as the trial progresses. KRIs can be visualized in a dashboard format for ease of monitoring. An example of a query metric monitored by Phastar is duration of Open Queries.
A Quality Tolerance Limit (QTL) is a level, point, or value associated with a trial variable that should trigger an investigation if a deviation is detected, in order to determine if there is a possible systematic issue (i.e. trend has occurred). QTLs are monitored at the trial level and are pre-defined before the trial from a review of historical data from similar trials and where possible, using statistical methods and modelling.
Phastar has developed a method of creating a QTL for protocol deviations using simulated data based on previous studies. We created simple, easy to understand limits which are effective in detecting unusual levels of protocol deviations at one site or at one visit. Although unusually high levels of deviations may indicate an issue at that site which may need to be quickly addressed, it is also important to be able to detect unusually low levels of deviations as this may indicate underreporting. Our limits were able to do both. Using such limits, any issues identified can be investigated in real time, giving the best chance of determining the root cause.
Easy visualisation of data against the QTL is key to successful implementation of RBM. The example described above may be plotted against the calculated limits so that breaches can easily be seen. Phastar are developing a tool which can visualise the deviation data in a dashboard format, so that data can be monitored and reviewed.
If you require support or advice on Risk Based Monitoring, please do not hesitate to get in touch with Phastar.