Pathway to a Statistical Risk-Based Monitoring Approach

With the onset of the new ICH E6 R2 regulatory guidelines in mid-June this year, clinical trial sponsors and CROs are gearing up and tightening the framework for launching new risk-based management approaches to onsite monitoring activities. The new addendum represents the largest revision to ICH guidelines in 20 years and places all responsibility of conformance on the Sponsor.

Risk Based Monitoring Centralized Statistical Monitoring

Why Risk-Based Monitoring

The increasing complexity of clinical and post-market trials has put pressure on all involved to focus on activities that will ensure the accuracy necessary to meet regulatory approvals.  As the clinical phase is the most complex part in a drug development process, it requires keen attention on efficient planning, conducting and monitoring of trials to achieve reliable study data appropriate for submission.  And, along with the complexities associated with data collection has come higher costs to the sponsor in both trial management and clinical monitoring to achieve that higher data quality and thus, better patient safety.

As a guidance for the industry, regulatory authorities have recommended that sponsors and CROs rely more heavily on centralized monitoring practices to promote risk mitigation and early issue detection to improve data quality and patient safety in a more cost-efficient manner.

This recommendation comes from studies that indicate the traditional method of on-site monitoring using source document verification (SDV) has been discredited in that it is not as effective as once perceived.  In fact, according to statistics, SDV only touched about 2 percent of the data and only changed 1 percent.  With the costs involved in manual verification and the disappointing results, industry regulators are encouraging a new look at how data is collected and verified.

Centralized Statistical Intelligence

The new regulation asks both sponsors and CROs to raise perspectives on the whole study – from the beginning and throughout the trial – to conduct a more holistic valuation of risk and then adjust methods of oversight at each site accordingly.

Centralized Statistical Intelligence (CSI) does just that.  It enables sponsors to identify issues such as a lack of variability or implausible values that may go undetected using other approaches.

  • Based on actual clinical data: all patient-related variables and endpoints in a study are deemed to be indicative of quality.
  • A risk score can be assigned to each site with an advanced and sophisticated statistical approach.
  • Results may be presented both with a statistical report or by using an interactive software (such as SAS Visual Analytics) to summarize anomalies detection with plots.
  • An application of the analysis that can be implemented in Visual Analytics is presented next

CSI is an unbiased approach, fully data driven, which means that any “red flags” are governed by statistical tools that have detected discrepancies in the data.  No arbitrarily fixed thresholds are involved in contrast to other risk-based monitoring systems.

Cutting through the Clutter

clinical trial analyticsChange can be disruptive whenever there’s dramatic change but approached systematically and strategically, a pathway can be established that results in new learning patterns and a more streamlined, cost efficient, and successful outcome.

CROS NT, as a full-service CRO, has demonstrated expertise in data-driven solutions to deliver quality monitoring and data from all sites, all of which is essential in the successful pathway to this new regulatory compliance.

Agile and responsive, CROS NT fits into the world of its clients – not the other way around.  The company has created a tool that is vendor agnostic, which means that sponsors are able to use their existing software technologies.  They can collect their data however they want, use whatever labs they want and it will be delivered into an enterprise system –  the required holistic approach.

This tool uses Principal Component Analysis (PCA) which is a multivariate analysis that converts p-value observations using statistical analysis to identify problematic sites rather than just subjective thresholds.  CROS NT integrates with a data analytics platform that has an alert function and allows sponsors, or CROs to track follow up of corrective actions.  It also offers comprehensive data visualization, including patient profiles and drill down capabilities.  And being vendor agnostic means that it can visualize datasets regardless of software application.