Trial complexity is the new normal

Clinical trial complexity is no longer exceptional. Protocols increasingly involve larger data volumes, more endpoints, more procedures, narrower eligibility criteria, and a wider range of data sources. Recent research found that Phase III protocols collect an average of approximately 5.9 million data points, with data volume increasing by around 11% annually since 2020.[1]

This growth creates additional burden for participants, sites and study teams – and makes uniform approaches to oversight progressively less effective. At the same time, personalised medicine, real-world data integration, decentralised trial elements, and greater geographic diversity are changing how studies are designed and conducted.

The first response to complexity should be to remove unnecessary procedures and data collection through quality-by-design principles. For the complexity and risks that remain, sponsors and CROs need an oversight model that can adapt to the study’s critical-to-quality factors, evolving risk profile and operational reality.

Risk-based quality management, or RBQM, provides this framework. It is a systematic approach to identifying the factors most critical to participant protection and reliable trial results, assessing the risks to those factors, and adapting oversight as risks evolve.

The benefits go beyond regulatory compliance. When operationalised effectively, RBQM can support earlier risk detection, more targeted quality improvement, and more intelligent allocation of clinical trial resources.

Regulatory expectations have evolved

ICH E6(R3) reinforces a long-running shift in regulatory thinking. It calls for quality to be designed into clinical trials and for trial processes to be proportionate to the risks to participants and the importance of the information being collected.[2] The guideline emphasises the identification of critical-to-quality factors, proactive risk management, and adjustment of controls when new risks or issues emerge.

Earlier FDA guidance similarly encouraged sponsors to move away from routine reliance on intensive on-site monitoring and 100% source data verification. Instead, monitoring should focus on the critical data and processes that have the greatest potential to affect participant protection and the reliability of trial results. The FDA also encourages greater use of centralised monitoring where appropriate.[3]

In Europe, the same principles are reflected in the adoption of ICH E6(R3) and recent MHRA guidance. The MHRA emphasises that risk proportionality does not mean reducing oversight indiscriminately. It means applying effort intelligently – reducing unnecessary activity where risk is low and strengthening controls where trial activities are critical.[4]

The regulatory direction is therefore clear. A uniform, checklist-driven model is increasingly difficult to justify when study risks, site performance, and data quality signals vary over time.

However, RBQM should not be viewed simply as a compliance exercise. Its value depends on whether it improves the way risks are detected, evaluated, and addressed.

Earlier detection and more targeted quality improvement

Traditional monitoring has often relied heavily on source data verification, with the assumption that reviewing more individual data points will produce higher-quality results.

Evidence challenges this assumption. In a retrospective analysis of 1,168 clinical studies, a median of only 1.1% of electronic case report form data was corrected following source data verification.[5] This does not mean that source data verification has no value. It indicates that applying the same level of verification to all data may be an inefficient way to identify the issues most likely to affect participant safety or the reliability of trial conclusions.

Risk-based oversight instead uses multiple sources of evidence to identify where attention is most needed. These may include key risk indicators, quality tolerance limits, central statistical monitoring, data-review findings, protocol deviations, and operational performance measures.

In a separate analysis involving 1,111 sites across 159 clinical trials, 83% of sites identified as being at risk through central statistical monitoring showed improvement in predefined quality metrics following investigation and follow-up.[6] The observational design means that the results should not be interpreted as definitive causal proof. Nevertheless, they provide quantitative evidence that centrally detected risks, when connected to targeted investigation and remediation, can support meaningful quality improvement.

The important distinction is that analytics alone do not improve quality. Value is created when a signal results in timely review, an appropriate action, and confirmation that the underlying issue has been addressed.

A growing financial case for RBQM

Quality and participant protection must remain the primary objectives of RBQM. Nevertheless, sponsors and CROs also need to understand whether investment in new technology, processes, and capabilities creates measurable operational value.

A 2023 Tufts Center for the Study of Drug Development impact report found that 78% of surveyed sponsors and CROs expected RBQM to improve clinical trial quality. Confidence was lower regarding efficiency and cost savings, at 63%, and timeline reductions, at 53%.[7] One possible explanation for this gap was the limited quantitative evidence then available on the financial and timeline effects of RBQM.

Recent research has started to address this evidence gap. An analysis using data from 18 oncology clinical trials estimated trial-level returns on RBQM investment of between six and 23 times the investment and development-programme returns of between four and 14 times the investment.[8]

The estimated value was primarily driven by time savings. Trials using RBQM were associated with reductions of between 8% and 19% in clinical-phase duration. The analysis also estimated monitoring-cost reductions of up to 18% under a scenario using 10% source data verification, compared with a baseline assumption of 100% source data verification.[8]

These findings should be interpreted in context. The study focused on oncology trials and combined observed trial data with benchmark data and modelled cost assumptions. The results are therefore scenario-based estimates, rather than guaranteed outcomes that can be applied uniformly across all studies.

Nevertheless, the findings broaden the business case for RBQM. Its potential value is not limited to reducing monitoring costs. Earlier risk detection and more focused oversight may also reduce avoidable delays, support faster issue resolution, and improve productivity across a development programme.

Operationalising adaptable, risk-based oversight

Realising these benefits requires more than purchasing a monitoring tool or reducing the percentage of data subjected to source data verification.

Effective RBQM begins before the first participant is enrolled. During protocol development, teams should identify the factors critical to participant protection and reliable study results and remove avoidable complexity wherever possible. They should then assess the risks to those factors and define how significant risks will be prevented, detected, controlled, and communicated.

During study conduct, central data review, statistical monitoring, key risk indicators, and quality tolerance limits can identify emerging issues across participants, sites, and the study. Adaptive site oversight can then direct clinical research associates towards the sites, data, and processes requiring the greatest attention.

This does not mean automatically minimising on-site monitoring or source data verification. It means selecting and adjusting central review, site monitoring, source data review, and source data verification according to the importance of the data and the risks to participants and trial reliability.

To make this approach effective, sponsors and CROs need an integrated operating model that connects:

• Quality-by-design and critical-to-quality factors
• Prospective and continuously updated risk assessments
• Centralised monitoring and cross-domain data analytics
• Proportionate site-monitoring strategies
• Clear signal review, escalation, and decision pathways
• Documented actions, outcomes, and reassessment of risk
• Cross-functional governance and accountability

Technology enables this model, but technology alone is insufficient. Clear decision rights, fit-for-purpose processes, specialist expertise, and timely follow-up determine whether detected risks lead to meaningful improvement.

For sponsors and CROs, the immediate implication is not simply to add another dashboard or monitoring system. It is to redesign oversight around the questions that matter most: What could materially affect participants or the reliability of the trial? How will emerging risks be detected? Who will decide what action is required? How will the organisation demonstrate that the action was proportionate and effective?

Clinical trial complexity is here to stay, but unnecessary complexity and uniform oversight do not have to be.

Regulatory guidance and a growing evidence base support an approach that concentrates attention on the data and processes most important to participant protection and reliable trial results. RBQM provides the framework for making those priorities explicit, detecting material risks earlier and adapting oversight as the study evolves.

The question should therefore no longer be simply whether to implement RBQM – or how much source data verification to remove. It should be whether an organisation can connect quality-by-design decisions, central intelligence, and site-level action within a dynamic and documented oversight model.

The competitive advantage will not come from applying fewer controls. It will come from applying the right controls, to the right risks, at the right time – and demonstrating the impact of those decisions with confidence.

References

1. Getz KA, Botto E, Calduch Arques A, et al. Insights informing strategies for optimizing the collection of clinical trial data. Therapeutic Innovation & Regulatory Science. 2026;60:563–574. doi:10.1007/s43441-025-00899-4.
2. International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use. ICH Harmonised Guideline: Guideline for Good Clinical Practice E6(R3). Final consolidated version. Adopted June 16, 2026.
3. US Food and Drug Administration. Oversight of Clinical Investigations—A Risk-Based Approach to Monitoring: Guidance for Industry. August 2013.
4. Medicines and Healthcare products Regulatory Agency. Clinical Trials for Medicines: Guidance on Quality and Risk Proportionality. Updated April 28, 2026.
5. Tantsyura V, Grimes I, Mitchel J, et al. Risk-based source data verification approaches: pros and cons. Therapeutic Innovation & Regulatory Science. 2010;44:745–756. doi:10.1177/009286151004400611.
6. de Viron S, Trotta L, Steijn W, Young S, Buyse M. Does central statistical monitoring improve data quality? An analysis of 1,111 sites in 159 clinical trials. Therapeutic Innovation & Regulatory Science. 2024;58:483–494. doi:10.1007/s43441-024-00613-w.
7. Tufts Center for the Study of Drug Development. Risk-Based Quality Management Impact Report. 2023.
8. Dirks A, de Viron S, McFarlane K, et al. Quantifying the financial return on investment of risk-based quality management implementation in clinical development. Therapeutic Innovation & Regulatory Science. Published online June 25, 2026. doi:10.1007/s43441-026-01002-1.

About the author

Sylviane de Viron is data and knowledge manager at CluePoints, where she is responsible for creating knowledge on risk-based quality management (RBQM) using CluePoints accumulated data. De Viron worked for the last 13 years in the medical and pharmaceutical sector in various positions. She holds a PhD in Public Health from Maastricht University.