Clinical trials are becoming complex day by day, however being in the era of Advance Analytics we can find out the ways to analyse this critical and intricate data.
Problem with Traditional Monitoring: Traditional Monitoring of Clinical trials is always
- Time consuming
- Costly
- Resource intensive
- Sometimes misleading due to personal biases.
Onsite monitoring for 100% SDV was considered as gold standard however as trials becoming more complex — with multiple endpoints, decentralized components and global sites; traditional onsite monitoring won’t suffice alone and must be combined with Risk based Monitoring.
Risk Based Monitoring is modern concept, where Monitoring is based on the
- Identification of various risks
- Assess and prioritize the risks
- Continuous evaluation of risks
- Implement the Proactive measures to mitigate those risks
RBM follows the Data Driven Trial Execution approach that is instead of reviewing/verifying each datapoint it focuses on what truly matters, especially targets the datasets which are impacting trial integrity, patient safety and regulatory compliance.
By reviewing real time data centrally from various source systems, we can detect anomalies, trends, flag inconsistencies, issues, forecast potential risks early
They encourage sponsors to:
- Implement Quality by Design (QbD) principles
- Identify Critical-to-Quality (CTQ) factors during trial design
- Focus on early risk identification to protect participant rights, safety, and well-being
The shift is clear — prevention is better than correction
Now here is how Advance Analytics truly enhancing RBQM.
The first critical step is ‘Integration of various source systems’ such as
- EDC (Electronic Data capture)
- CTMS (Clinical Trial Management System)
- EPRO (Electronic Patient Reported Outcome
- IRT (Interactive Response Technology)
- LIMS (Laboratory Information Management System) and so on…
Once these systems interact seamlessly; we can cross verify.
For examples if there is protocol Deviation at the site related to patient visit window.
- Visit date in the EDC & IRT,
- Relevant Assessment date in EPRO,
- Relevant Lab collection date in LIMS
- Finally, PD description documented by CRA on CTMS.
All of these can be cross verified within the system. This assures the data consistency and transparency.
Leveraging Advance Analytics like AI, LLM
Once we integrate the systems next step is Analytics
We can perform calculations to bring the Various KRI values such as
- Screen failure rate
- Protocol deviation rate
- Missing pages
We can define thresholds, Confidence interval and flag the outliers.
With predictive analytics,
- We can identify trends related to patient recruitment, trial compliance.
- We can forecast the AEs, Anticipate protocol deviations
- We can evaluate potential safety signals
Advance Analytics doesn’t replace human oversight but enhances it.
It allows Clinical Research teams
- To focus on critical aspects of the trials
- To make proactive data driven decisions
- To reduce clinical trial timeline
- To improve the cost efficiency
- To strengthen the Participant’s safety and well-being.
In a nutshell, there are more opportunities in the RBQM to exploit the AI efficiently to accelerate clinical trials while adhering to the ethical and regulatory standards
