Looking to the Future with RBQM
Controlling Risk Across the Entire Study Lifecycle

Patrick Hughes

efore, it was about Risk-Based Monitoring (RBM). Now, it is about Risk-Based Quality Management (RBQM), an ICH- and FDA-advocated approach to managing risk for the entire clinical trial lifecycle. Improving data quality and patient safety, while controlling the spiralling costs of drug development research, were the primary objectives behind the shift towards RBM over the last eight years. The model’s success, combined with advances in clinical trial technology, has seen the approach extended to cover the whole trial execution, a methodology widely referred to as “RBQM.” But what is the difference between RBM and RBQM? What are the benefits? And how can sponsors and CROs harness the power of risk-based trial management?

Key Takeaways

  • In the case of risk assessment, earlier risk detection and intervention prevents significant permanent damage.

  • Central Statistical Monitoring (CSM) plays a crucial part in RBQM, helping to drive down the cost of randomized clinical trials while improving their quality.
  • Elements of RBQM can be implemented individually and independently to great success, making clinical trials better, faster, and cheaper for sponsors and CROs – and safer for patients.

Spiralling Costs

The traditional way of conducting trials is not fit for the 21st century. Regulators presented a gift to the industry in the form of RBM to enable sponsors and CROs to take a more streamlined and efficient process, reduce risk, and reap significant cost and timeline benefits into the bargain. However, clinical trial costs are still rising. Recent estimates suggest that pivotal clinical trials leading to FDA approval have a median cost of US$ 19 million; such costs are even higher in oncology and cardiovascular medicine, as well as in trials with a long-term clinical outcome, such as survival.

In industry-sponsored trials, vast resources are spent on making sure that the data collected are error free. This is typically done via onsite monitoring (site visits) including source data verification (SDV) and other types of quality assurance procedures, together with centralized monitoring including data management and statistical monitoring. While some onsite activities make intuitive sense, their cost has become exorbitant in the large multicenter trials that are typically required for the approval of new therapies. It has been estimated that for large, global clinical trials, leaving aside site payments, the cost of onsite monitoring represents about 60 percent of the total trial.

The Contrast Between RBM and RBQM

RBM, which requires sponsors to use technology and real-time information to proactively monitor risk, was written into US and European regulations in 2013. In its simplest form, RBM strategies use software, data, and analytics to monitor risk and support critical thinking and decision making. By giving sponsors the ability to identify and correct issues as and when they arise, RBM can improve data quality and patient safety as well as reduce costs.

The latest version of the GCP quality standard extends this approach to every aspect of study execution. The model, commonly known as RBQM, applies the principles of RBM to all areas of quality management. RBQM methodology is a very timely development that sponsors and CROs are now embracing to address the growing crises in research complexity, duration, and cost.

Indeed, there has been a dramatic increase in demand for remote and centralized site monitoring amid the ongoing coronavirus outbreak. With sites increasingly proving to be inaccessible across the globe for patients, site staff and/or site monitors, and regulators halting inspections, the biopharmaceutical industry is facing many challenges to ensure patient safety, compliance, and data quality and integrity. In clinical trials, the mounting restrictions on site visits means that sponsors and their CRO partners are actively seeking alternative, remote approaches to monitoring study conduct, compliance, patient safety, and data quality across all participating sites. Fortunately, eliminating or reducing onsite monitoring visits can happen without significantly impacting trial oversight.

The industry has been moving towards central monitoring; the current crisis is forcing more rapid change now. Indeed, a specific COVID-19 risk assessment can help identify central monitoring activities with increased focus on the critical data, critical risks, and critical processes during these difficult times.

Focussing on What Matters

If onsite monitoring activities significantly impacted patient safety or results, then high costs could be justified. Yet, there is little evidence showing that extensive, intensive data monitoring has any major impact on the quality of clinical trial data. The most time consuming and least efficient activity is SDV, which can take up to 50 percent of the time spent on onsite visits. The monitoring of clinical trials needs to be reengineered. To instigate and support this much needed transition, regulatory agencies worldwide have advocated the use of RBQM, including central statistical monitoring (CSM).

The central principle of RBQM is to “focus on things that matter.” Risk management underpins the overall quality of the trial by identifying, controlling, and communicating. RBQM encompasses all elements of the study, from planning right through to execution:

  • Critical process and data identification
  • Risk identification
  • Risk evaluation
  • Risk control
  • Risk communication
  • Risk review
  • Risk reporting

The Power of CSM

CSM is part of RBQM. As shown in Figure 1, the process starts with a Risk Assessment and Categorization Tool (RACT). CSM helps quality management by providing statistical indicators of quality based on data collected in the trial from all sources. A “Data Quality Assessment” of multicenter trials can be based on the simple statistical idea that data should be broadly comparable across all centers. This idea is premised on the fact that data consistency is an acceptable surrogate for data quality. Other tools of central monitoring can be used in addition, to uncover situations in which data issues occur in most (or sometimes all) centers, such as “Key Risk Indicators” and “Quality Tolerance Limits.” Taken together, all these tools produce statistical signals that may reveal issues in specific centers.

Figure 1: The Risk-Based Quality Management process.
Figure 1: The Risk-Based Quality Management process.

Actions must then be taken to address these issues, such as contacting the center for clarification, or in some cases performing an onsite audit to understand the cause of the data issue. Although it is a simple idea to perform a central data quality assessment based on the consistency of data across all centers, the statistical models required to implement the idea are necessarily complex to properly account for the natural variability in the data. Essentially, a central data quality assessment is efficient if:

  • data have undergone basic data management checks, whether automated or manual, to eliminate obvious errors (such as out-of-range or impossible values) that can be detected and corrected without a statistical approach;
  • data quality issues are limited to a few centers, while the other centers have data of good quality;
  • all data are used, rather than a few key data items, such as those for the primary endpoint or major safety variables; and
  • many statistical tests are performed, rather than just a few obvious ones such as a shift in mean or a difference in variability.

The last two points are worth highlighting. It is statistically preferable to run many tests on all data collected than on a few data items carefully selected for their relevance or importance. Volume rather than clinical relevance is crucial for a reliable statistical assessment of data quality. The power of statistical detection comes from an accumulation of evidence, which would not be available if only important items and standard tests were considered. In addition, investigators pay more attention to key data (such as the primary efficacy endpoint or important safety variables), which, therefore, do not constitute reliable indicators of overall data quality. Nevertheless, careful checks of key data are also essential, but such checks generally are not statistical in nature.

RBQM and Business Intelligence

As the industry realizes the benefits of RBQM, users are looking for ways to extend the way they drill down into risks and focus on what matters most within their studies. Pioneers in the field of RBM are responding to industry demand, ensuring sponsors, CROs, and Clinical Technology Partners benefit from constant innovation and transparency. The industry is now incorporating business intelligence platforms into the mix to encompass more than just early risk detection. Novel visualization solutions take RBQM beyond the interrogation of data and allow sponsor and CRO users to explore areas of interest in one integrated business intelligence system. The comprehensive nature of the analytics offered within such business intelligence platforms gives all users the data exploration tools and personalized visualizations they need to test hypotheses and drive critical thinking. Within one platform, risk signals can be identified, and all evidence can be fully documented and accessible. This guarantees full compliance with ICH E6 (R2) and other global regulatory requirements throughout the lifecycle of the clinical trial.

Business intelligence solutions facilitate a deeper understanding of what is happening within a trial, whether it be focusing on a specific area of the clinical trial (such as the safety profile and the status of adverse and serious adverse events), a wider focus on clinical data (such as plotting vitals and lab results and exploring connected data, and tracking enrolment and screen failures), or even looking at the operational data in a trial (such as missed assessments or query turnaround). All-encompassing platforms offer an exclusive combination of both supervised and unsupervised data surveillance techniques that enables users to test what they think they know, as well as what they could not possibly know or suspect. The ability to select or create the appropriate visualization to explore that data in its most powerful format offers actionable insights across safety, clinical, enrolment, and operational aspects. Combining business intelligence, analytics, and reporting in one platform enables greater knowledge sharing for data driven success.

RBQM and Industry Need

In the wake of COVID-19, many sponsors and CROs were struggling to keep up with the need to assess risk on new and ongoing studies. In response to customer demand and the specific challenges that the clinical trials industry was facing because of the pandemic, COVID-19 risk management solutions have been made available. With the FDA, EMA, PMDA, and MHRA issuing specific guidelines due to the coronavirus pandemic, such packages have helped meet these new recommendations and address additional study risks. Online risk assessment and control solutions can be leveraged for performing risk planning and mitigation activities addressing inherent risks during this crisis.

The Future of RBQM

The rising cost of clinical research is in part due to extensive trial monitoring processes that focus on unimportant details. Conversely, RBQM focuses instead on “things that really matter.” The adoption of statistics to assist with quality oversight in trials is well documented. Cloud-based solutions, driven by CSM, employ a unique set of algorithms that interrogate clinical and operational data in real time centrally to conveniently illuminate data outliers and anomalies. CSM plays a crucial part in RBQM, helping drive down the cost of randomized clinical trials while improving their quality.

In clinical trials, timing is everything. In the case of risk assessment, earlier risk detection and intervention prevents significant, permanent damage. Risk assessment and central monitoring is doubly important during the current global COVID-19 crisis. Now more than ever the clinical trials industry needs to focus on what matters most to gain insights that advance life sciences. Looking to the future, RBQM can be applied across studies, programs, and organizations alongside machine learning and artificial intelligence to identify program, organization, or industry trends. Learning from historical data can help make future decisions to support study setup, risk assessment, analytical tools, and signals management. Elements of RBQM can be implemented individually and independently to great success, making clinical trials better, faster, and cheaper for sponsors and CROs – and safer for patients.

Regardless of global crises, central monitoring offers extended benefits, including increased operational efficiency, reduced cost, and decreased regulatory submission risk. In a sea of non-compliance, it is important to focus on what matters most–the critical risks and critical data–to give the industry confidence to succeed.