Risk-Based Quality Management (RBQM) solutions allow Sponsors and CROs to confidently detect and effectively manage risk in clinical trials. It’s an evidence-backed approach with regulatory backing, meaning wide-scale adoption is becoming increasingly inevitable.
But how do organizations embed this new way of working into their processes? Laura Trotta, VP of Research at CluePoints, says technology is the answer and shares her expert advice on harnessing the power of Central Statistical Monitoring (CSM) to drive RBQM.
Table of Contents
- The Growing Need for RBQM in Clinical Trials
- Data Quality Challenges: A Key Driver for RBQM Adoption
- Defining RBQM: Technology-Driven Trial Oversight
- Implementing RBQM: A Step-by-Step Guide Using QbD Principles
- Key Components of RBQM: KRIs, QTLs & Data Surveillance
- CSM in RBQM: Proactive Risk Detection Across All Trial Data
- CluePoints’ RBQM Solution: Cloud-Based CSM for Superior Data Oversight
- The Path to Full RBQM Adoption: Industry Trends & Progress
The Growing Need for RBQM in Clinical Trials
At the turn of the century, drug makers faced growing pressure from multiple directions. Highly publicized safety issues with marketed drugs, a slowing of innovation coupled with patent expirations, and a continual increase in the complexity of clinical trial designs had steadily increased the cost and duration of clinical development. At the same time, profit margins dwindled.
Research from the Tufts Center for the Study of Drug Development (CSDD) highlighted a significant increase in clinical trial complexity from 2005 to 2015, including a 68% rise in procedures per patient, an 88% surge in data volume, and twice as many countries involved per study.
This significantly increases the risk to the operational success of research, jeopardizing patient recruitment and retention, the generation of reliable results essential for marketing approvals, and, most critically, the integrity of an ever-growing dataset.
This was evident in a review of marketing submissions to the FDA from 2000 to 2012, which revealed that one-third (32%) of all first-cycle review failures—representing 16% of submissions overall—were due to quality issues. It became clear that the traditional approach to clinical trials was no longer suited to the demands of the 21st century.
Data Quality Challenges: A Key Driver for RBQM Adoption
There is no denying that data quality is the cornerstone of a successful clinical trial. Issues with data quality can signal problems in trial conduct, potentially compromising participant safety. More critically, they can hinder the drug or device appraisal process, leading to delays or even denial of approval.
On-site monitoring of clinical sites is a key method for ensuring that studies reliably assess the safety and efficacy of investigational treatments. Traditionally, this has involved source data verification (SDV), where data recorded in case report forms are cross-checked against patients’ medical records.
However, this process is time-consuming, often accounting for more than half of each monitoring visit, and places a significant workload on on-site personnel. As the volume of data collected continues to grow—particularly with the rise of wearable technologies for continuous patient monitoring—this challenge is only expected to intensify. More time- and cost-effective approaches, such as RBQM, have been proposed to address these challenges.
Defining Risk-Based Quality Management (RBQM): Technology-Driven Trial Oversight
Risk-based quality management (RBQM) takes the principles of Risk-Based Monitoring (RBM) and applies them to a clinical trial. It uses technology, analytics, and statistics to detect real-time data quality issues. This enables Sponsors and CROs to take corrective action before problems can impact the integrity of a clinical trial.
RBQM boosts both participant safety and the likelihood of trial success and has been backed by the US Food & Drug Administration (FDA), the European Medicines Agency (EMA), and the International Committee on Harmonization (ICH).
RBQM can serve as a powerful catalyst for risk management. By enabling researchers to identify and prioritize the most critical data, tech-driven RBQM solutions offer a comprehensive safety net—safeguarding both patient well-being and trial success. While the reasons for adopting RBQM are well-documented, the practical steps for implementation have received far less attention.
Implementing RBQM: A Step-by-Step Guide Using QbD Principles
RBQM applies the principles of Quality by Design (QbD) and RBM to all study elements, from planning to execution. It underpins the overall quality of the trial by identifying, controlling, and communicating risk. ICH E6 R2 sets out what a gold standard RBQM system would cover:
- Critical Process & Data Identification
- Risk Identification
- Risk Evaluation
- Risk Control
- Risk Communication
- Risk Review
- Risk Reporting
Within the RBQM paradigm, QbD and RBM are two phases of the same framework. Both are focused on improving the operational success of clinical research, and both apply the core risk assessment and mitigation process.
QbD becomes RBM once a study protocol is finalized; at this point, risk assessment is repeated to mitigate any remaining operational risks. Mitigation plans are then applied during study execution, which includes ongoing risk monitoring and a more targeted approach to site monitoring.
CluePoints’ blog outlines a comprehensive, ten-step process for practical RBQM implementation in clinical trials, emphasizing clear objectives, cross-functional teams, data literacy, and QbD. It emphasizes the importance of assembling the right team and securing buy-in before streamlining processes. Below is a snapshot of the ten essential steps—click here to dissect them further.
- Foundations & Objectives
- The Executive Sponsor, Trusted Advisor & Champion
- RBQM Is Cross-Functional
- Data Literacy Training
- Quality by Design (QbD)
- Pre-Study Risk Planning
- Central Monitoring
- SDV/SDR Planning
- Risk-Based Data Management (RBDM)
- Selecting the Right Technology Partner
Key Components of RBQM: Key Risk Indicators (KRIs), Quality Tolerance Limits (QTLs) & Data Surveillance
Effective RBQM entails three elements:
- Key Risk Indicators (KRIs)
- Quality Tolerance Limits (QTLs)
- Data Surveillance
KRIs and QTLs are designed to monitor pre-identified risk areas, but it’s essential to prioritize quality over quantity when establishing them. For instance, implementing 40 KRIs per study can be difficult to manage, often leading to redundant risk detection and excessive “signal noise.”
Instead, Sponsors and CROs should leverage QbD to identify a core set of meaningful KRIs and QTLs, ensuring they’re optimized. This approach enhances early risk detection while minimizing false alerts.
Central Statistical Monitoring (CSM) in RBQM: Proactive Risk Detection Across All Trial Data
RBQM investigates all possible risks, not just the obvious threats to primary endpoints and safety data. CSM can expose forms of study misconduct that may be difficult to identify or characterize during pre-study risk planning. Through a central statistical assessment of data quality, data surveillance provides an invaluable insurance policy by flagging anomalies that warrant further investigation.
It works on the assumption that data from all centers should be comparable and statistically consistent, other than random fluctuations and natural variations. Unsupervised statistical monitoring, or CSM, consists of performing as many statistical tests on trial data as possible to detect inconsistencies that could point to potential problems.
Running a discreet set of well-designed statistical tests across a broad swathe of study data can spot atypical patterns representing potential intentional or non-intentional misconduct. It can flag issues such as fraud, sloppiness, training needs, and malfunctioning or mis-calibrated study equipment.
CSM is at the heart of RBQM, analyzing all clinical and key operational data to uncover discrepancies that traditional methods might miss. CSM goes beyond calculating statistics on a select set of variables—it’s about processing comprehensive data and directing users to potential issues before they escalate.
CluePoints’ RBQM Solution: Cloud-Based CSM for Superior Data Oversight
CluePoints’ solutions, all of which are cloud-based, are driven by our unique set of CSM algorithms. They interrogate clinical and operational data centrally and in real time to identify outliers and anomalies in data efficiently.
We tested them using data from a large Japanese multicenter trial in advanced gastric cancer. A blinded team was asked to use our CSM software to detect intentionally contaminated data points within the dataset. A Clinical Trials journal study showed that the approach could detect atypical data in a multicenter trial with a specificity of better than 93%.
This approach is unsupervised, requires no input from the user, and is exhaustive in that it uses all variables collected at the patient level. It eliminates the need for time-consuming SDV unless triggered by an atypical data alert, which frees up resources to be diverted into more safety- and success-critical tasks. The study concluded that CMS was a “cost-effective complement to other data-management and monitoring techniques.”
The Path to Full RBQM Adoption: Industry Trends & Progress
The industry has not yet achieved 100% adoption of RBQM, but it’s moving in the right direction.
Recent survey data presented at eClinical Forum found that 18.2% of the 210 respondents had not started an ICH E6 (R2) compliance program. More than 44% were either piloting new processes or were already fully compliant. Additionally, a 2019 roundtable organized by the CSDD, heard that companies had made significant progress in responding to the ICH E6 addendum in the previous two years.
Amanda Hayden, Director of Global Clinical Services at Alkermes, told the meeting that the industry had experienced “significant maturation” with respect to its response to ICH E6 (R2). “While many companies may have approached the ICH update by addressing each subpart separately, the reality is that many of the processes are interconnected,” she said. “Advancing clinical processes in a holistic fashion and thinking of risk in all facets of study planning, execution, and analysis creates synergies and efficiencies and allows for the better use of time, resources, and study data.”
As innovative software is recognized for its essential role in improving clinical trials, RBQM adoption is finally moving from “if” to “how.” The framework for a shift from RBM to RBQM has been accepted and implemented, and the next step is to leverage the data more effectively.
This can be achieved through more advanced analytics, such as those driven by CMS, which can guide critical thinking and informed decisions. Harnessing the power of CMS unlocks the power of RBQM to create safer, more successful clinical trials and shortens the pathway from candidate discovery to marketing approval.
Ready to enhance your clinical trial data’s quality, accuracy, and integrity—during and after your study? Contact us today to discover how our CSM-powered RBQM solutions can improve patient safety, boost productivity, and drive cost-effective results. Let’s transform your trials together.
