Risk-Based Monitoring

Risk-based monitoring requires integrated data systems, validated analytics, and strong governance to work effectively across global trials, but sponsors face significant technical and operational challenges that demand strategic solutions and organizational alignment.

Most protocol deviations start as human problems, not operational ones, but risk-based monitoring typically relies on lagging indicators that arrive too late to prevent these problems instead of measuring readiness during training, when intervention still matters.

Quality tolerance limits work best as a small set of focused guardrails that prompt rapid investigation and documented action, not as documentation exercises, with early breaches expected when sample sizes are small.

Sylviane de Viron, data and knowledge manager at CluePoints, shares new data highlighting regional variations in clinical trial risks—and how proactive risk planning based on up-to-date data is needed to overcome them.

As ESG expectations rise across clinical development, sponsors are finding that sustainability efforts gain traction only when embedded into existing vendor oversight and quality management processes rather than treated as a standalone reporting exercise.

Analysis of more than 880 clinical trials shows that while statistical data monitoring and key risk indicator signals close on similar timelines, durability—not speed—is the defining differentiator in effective, risk-based quality management.

Comparator drug sourcing in the EU is shaped by a defined set of availability, regulatory, competitive, and logistical pressures, alongside practical strategies to reduce risk and protect trial timelines.

As clinical trials become more complex and decentralized, the role of the CRA is evolving from site monitor to strategic, digitally fluent partner, requiring expertise in hybrid oversight, risk-based monitoring, and multi-system data management.

How a unified approach to clinical data management, powered by artificial intelligence and advanced analytics, can elevate clinical trial monitoring and redefine how teams assess, act on and learn from data.

By adopting structured practices today, clinical trials can deliver diagnostics that are reliable, impactful, and ready for tomorrow’s healthcare needs.

As clinical trials become more complex, success depends on early collaboration, thoughtful protocol design, and practical strategies to manage risk, reduce delays, and support patients.

Clinical trial failure insurance is turning biotech’s most costly risk into a manageable asset, opening new pathways for funding and innovation.

Exploring stat-based testing for variables that identify deliberate data manipulation in clinical trials.

While randomization is required by regulatory bodies, it is up to the sponsor on how to conduct it.

In this episode of the Applied Clinical Trials Podcast, David Nickerson, head of clinical quality management at EMD Serono; and Arlene Lee, director of product management, data quality & risk management solutions at Medidata, discuss the newest ICH E6(R3) GCP guidelines as well as how TransCelerate and ACRO have partnered to help stakeholders better acclimate to these guidelines.

An increase in protocol complexity is paving the way for machine learning models to optimize trial design.

Study uncovers insights on the impact on safety, patient enrollment/retention, and compliance.

The long-held practice is effective in pinpointing the emergence of known and unknown risks to clinical development, study shows.

In an interview with Applied Clinical Trials Associate Editor Don Tracy, Richard Young, chief strategy officer, Cluepoints, offers his key takeaways from this year's SCOPE Summit in Orlando.

In an interview with Applied Clinical Trials Associate Editor Don Tracy, Richard Young, chief strategy officer, CluePoints, discusses goals of the SCOPE Summit and CluePoints' evolving approach to risk detection.

Recent analysis showed 81% of clinical trial sites using RBQM statistical data monitoring experienced an improvement in quality.

A review of industry methods and adoption trends in those trial segments.

Moving towards the development of mature quality management systems.