EMA Guidance Points to Central Statistical Monitoring

By February 1, 2014 News No Comments

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While capable of conducting high-quality trials, the current oversight process can be expensive and inefficient.

February 1, 2014
By Marc Buyse, ScD
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The release of the final version of EMA’s Reflection Paper on Risk-Based Quality Management in Clinical Trials marks an important milestone in efforts to introduce and develop the risk-based monitoring paradigm. As data quality is unquestionably what matters most in determining study success and ensuring patient safety, the latest recommendations have created growing demand for practical solutions to simplify the transition to risk-based techniques and help the industry put regulatory advice into practice.

Current quality systems implemented by sponsors and CROs have been widely acknowledged as time-consuming, while commanding a major proportion of the cost of drug development programs. To minimize the pressure on resources, the EMA reflection paper demonstrates the need for a more systematic, prioritized, risk-based approach to quality management that complements existing quality practices, requirements, and standards. The document draws attention to the fact that the ICH GCP guideline was finalized in 1996 when clinical research was largely paper-based. Since then, the industry, the available technology, and the approach to the conduct of trials, have all evolved considerably necessitating that monitoring approaches follow suit.

Much of the industry would agree that while capable of conducting high quality clinical trials, the current oversight process can be expensive and inefficient. Central statistical monitoring (CSM) could provide the ideal answer as it can help alleviate quality management issues by identifying risk and determining the integrity of clinical data throughout the drug development process. The final version of the EMA reflection paper does not differ much from the draft published two years ago, and essentially endorses the use of CSM. The paper highlights the potential to develop central monitoring systems using statistical methodology to monitor the quality of the trial conduct and data. It supports the use of regular metrics reports to demonstrate that checks are being conducted and ensure compliance with pre-defined monitoring strategies. By doing this, sponsors and CROs will be able to target on-site monitoring visits to address the issues that such visits are better placed to detect.

In light of both the EMA and FDA recommendations, statistical monitoring methods are now proving essential in today’s clinical trials. The use of CSM determines the expected values of each variable by examining the data from all investigative sites involved in a trial to identify statistical outliers. Complex and proven statistical algorithms drill down into individual patient data to detect issues that could put a study at risk and create barriers to successful submissions. The approach is based on the actual clinical data and not subjective indicators. The rationale behind this is that all variables are indicative of quality—whether it is lab data, clinical data, baseline data, or treatment outcomes; everything is analyzed and deemed equally important. In a clinical trial, everything that is collected should be worth collecting, and therefore worth checking. CSM determines the quality and integrity of all data and ensures that monitoring efforts focus on errant sites efficiently.

Looking at the CSM method practically, adopting the approach requires minimal work for study teams in gaining objective information and sponsors who strategically outsource to CROs are also finding increased efficiencies by using the method as an oversight tool to regularly check the quality of their data. Implementing these techniques can not only reduce costs and address the latest regulatory guidance, but can make better use of resources and optimize overall trial success rates.

Marc Buyse, ScD
Founder of CluePoints Inc.
E-Mail: marc.buyse@iddi.com