Risk-based quality management (RBQM) solutions offer sponsors and CROs a better way of detecting and managing risks that may impact the outcome of clinical trials. The US Food and Drug Administration, the European Medicines Agency, and the International Committee on Harmonization all advocate using a risk-based approach to monitoring data quality in clinical trials. While the adoption of statistics to assist with quality oversight in trials is well documented, there are many different approaches to the implementation of RBQM, mainly due to the varying interpretations of the ICH E6 (R2) guidance. In this blog, Laura Trotta offers her expert advice on harnessing the power of Central Statistical Monitoring (CSM) to drive RBQM. In-depth discussions can be found in a recently published article and CluePoints’ whitepaper.
Risk-based Quality Management in Clinical Trials
In clinical trials, there is no denying that data quality is crucial. Any issues identified with the quality of the data may be linked to problems with how the trial was conducted. This could have possible safety implications for any patient subject involved in the trial. Data quality issues also have a detrimental effect on the approval process of the drug or device under investigation. On-site monitoring of the clinical centers participating in a trial is one method to ensure the reliable assessment of the safety and efficacy of experimental treatments. Source-data verification (SDV), where the data collected in the case report form is compared with the original data in the patient’s medical records is a time-consuming monitoring activity. SDV takes over half of the time of monitoring visits and imposes a high burden on-site personnel. The usefulness of this activity has been questioned and more cost-effective ways of conducting trials and checking data quality have been proposed.
Managing risk can be done in a random, supervised or unsupervised way and the corresponding level of risk is either high, medium or low. We discuss this in more detail in one of our publications. With recent findings demonstrating that the Food and Drug Administration (FDA) reject marketing approval for new drugs because of inconsistencies between trials, centres, or endpoints, or issues in study conduct, a strategy for effectively managing risk is crucial. Technology is a catalyst to risk management, helping researchers to focus on what really matters (that may or may not have been anticipated). This is where the unsupervised ‘leave no stone unturned’ approach comes into its own. It is the ultimate safety net and insurance policy for a trial and all research and outcomes will benefit from the approach, both from a patient safety and ultimate success perspective.
A Risk-Based approach to clinical research may include a central statistical assessment of data quality, which is crucial in the investigation of all possible risks and not just the obvious primary endpoints and safety data. CluePoints has recently taken part in an experiment to determine whether a computer-intensive approach that generates large numbers of statistical tests is an effective way to check data quality and manage risk in multicentre clinical trials. A study was implemented to investigate the operating characteristics of unsupervised CSM aimed at detecting atypical data in multicentre experiments. Detailed materials, methods, results and discussion can be found in the recently published paper: Detection of atypical data in multicentre clinical trials using unsupervised statistical monitoring. The approach offers a cost-effective complement to other data management and monitoring techniques.
CluePoints’ solutions, all of which are cloud-based, are driven by CSM, a unique set of algorithms that interrogate clinical and operational data in real-time and centrally to efficiently identify outliers and anomalies in data. In the study mentioned above, a statistical approach for the detection of atypical data in multicentre experiments was used. The approach is unsupervised, i.e. it requires no input from the user, and exhaustive, i.e. it uses all variables collected at the patient level. Using this CSM approach has been shown to be effective at detecting atypical data that point to problems in actual studies. The elimination of SDV, except when triggered by atypical data, would free up a considerable amount of human resources that could be spent on more important risks in clinical trials, particularly those related to patient safety and other key aspects of trial conduct.
While the industry has not yet achieved 100% adoption of RBQM, it is encouraging to see recent survey datapresented by eClinical Forum, where out of 210 respondents only 18.2% had not started their R2 compliance program and over 44% were either piloting their new processes or were already fully compliant. This was followed by key takeaways from a 2019 roundtable organised by The Tufts Center for the Study of Drug Development (CSDD), where companies reported that they have made significant progress responding to the ICH E6 addendum. It looks like RBQM adoption is finally moving from “if” to “how” as innovative software is recognised for the essential role it plays in improving clinical trials. Now that the framework of a Risk-Based Approach to manage the entire clinical trial (a move from RBM to RBx and RBQM) is widely accepted, a key next step is to better leverage the data through more advanced analytics to make informed decisions and guide critical thinking.
Contact us today to find out how our solutions can help drive the quality, accuracy, and integrity of clinical trial data both during and after study conduct, improving patient safety and increasing productivity, efficiency and cost-effectiveness. Our RBQM solutions powered by CSM can help transform your trials.