When the journey started it was all about monitoring. It was about addressing the inefficiencies, rationalizing resources to reduce costs and tackle potential quality issues that could put the clinical trial at risk. “Focus on what really matters” was born, Risk-Based Monitoring was alive.
As more people embark on the journey, it’s becoming less difficult to convince RBM study teams of the value in deploying a risk-based approach to monitoring. This is primarily down to the early adopters that openly shared their positive (and negative) experiences and framed the path to successful adoption and execution. From a rather vague concept, industry leaders reinvented their approach by redefining ancient processes, inventing new roles and implementing tools to better support monitoring activities.
Interestingly, these leaders are now thinking beyond just monitoring. The ICH E6 (R2) has obviously been a great source of inspiration for those considering adopting a risk-based study methodology to areas other than monitoring. Data Management and/or Data Review are good candidates, amongst a bunch of opportunities.
The key takeaway here is that as you initiate a new clinical trial and you identify, assess and mitigate your risks (section 5.0 of ICH E6 (R2) is pretty clear that Risk Management applies to all clinical trials), your focus should not only be on how your monitoring activities will be addressing the risks. There are actually a number of unrelated activities that may help prevent or detect potential issues earlier. The resource rationalization and its related ‘focus on what really matters’ concept are equally applicable to edit checks, queries, medical review, safety review etc.,
Here at CluePoints, we’ve experienced customers that didn’t adapt their monitoring strategy at all. Their focus was adopting a risk-based targeted approach to better understand where their risks reside, mitigate them and then adopt algorithmic techniques to monitor these and uncover unexpected issues within the trial data. No reduced SDV strategy but an optimization of their resources to focus on atypical sites, patients or regions, by letting their data ‘speak”
We believe that a good Risk-Based approach relies on three fundamental elements: risk, signal and action. While a risk is something that may potentially happen, a signal is something that actually happened. And as you perform an assessment of these risks and signals, you may decide to act: there is a risk so I take an action (or several) to prevent the risk from happening or ensure its early detection; there is a signal, I take the action of investigating the potential issue and further down the road if the signal is indeed an issue, I take an action (or several) to fix the problem.
From that perspective, Risk Management is a great starting point to author your different functional plans (not just the monitoring plan). Fundamentally, a functional plan is telling you who should do what, when and why. The risk assessment and mitigation processes are giving you exactly that. The risk gives you the reason, the action is telling you what to do, who should do it and when. For example, a high safety risk may imply that 1) my monitor will visit the site every 12 weeks and will focus its SDR on SAEs reporting but it could also imply that 2) my data manager will generate an AEs/Conmeds reports every 4 weeks, that 3) my medical reviewer will dedicate more time on the 5% of the patients with a patient profile reporting an atypically high or low number of AEs and finally that 4) my central monitor with perform statistical data monitoring to identify unusual safety reporting patterns at sites.
But then what? You may have the best plan on earth, you still have to execute it and make sure that your plan ‘works’. Did your mitigation actions reveal problems? Were these problems related to risks that you anticipated or not? How did you find out about these unexpected problems? Should you consider these unexpected risks in future clinical trials? How many of these pre-defined actions were actually ‘useful’? By answering these questions, you are closing the loop. Data, metrics, actions are becoming reusable knowledge.
That’s the goal for the CluePoints team – to establish a community of users sharing their experiences by gathering all the knowledge, processing it with novel analytical techniques (i.e. Machine Learning and Statistics) to make your next clinical trial better, faster and cheaper.