RQM ’s Post

Transcript

No, it's there. Ladies and gentlemen, welcome back to Part 2 of what we're calling key aspects of clinical trial design for medical devices. I'm going to pick up right away from where we left off in the previous video and this question this time is at rest to carry now Kerry, I'm aware of our best practices and ability and ongoing efforts directed at delivering the highest efficiency and effectiveness of clinical trials that RQ plus. So for the audience, how does the CRO. The RQ Plus help with barriers such as those related with handling ethics and regulatory systems. Effective and efficient patient recruitment and skilled staff and robust infrastructure, all important pillars of the conduct and design of a trial. Great. Thanks Jay. The one word answer to that is experience, but. Experience and what so you know on the last session we talked about all of the things you need to keep in mind when you're designing your trial, whether it's for real, whether you're heavy reimbursement set, whether you're regulatory strategies are set, whether you're design matches real life situations and our Q plus has all of those people here so we can as you're designing your trial we can work with you on reimbursement and not just look at your your product but. The the landscape surrounding your product, what does that reimbursement look like? Are your expectations realistic? We can help that piece of it. We can help with the regulatory aspect from a global perspective, we have experts in. Across the world that we can help make sure that those trial you're designing, the data you're collecting is going to be harmonized across all of those countries. So you know that you can use this for, for your multiple submissions. And like Guido talks about last time from the clinical aspect and really, you know, he gave a great example of us seeing a protocol, seeing a study and saying this is a really probably not gonna work this way and working with the sponsor to go back and and make it something that would actually work. And so you have that clinical experience in the team of people who have been doing trials, specifically medical device trials for a very long time. So we can take all of that experience. And whether you've designed it or not. Already we can look at it and say, you know, we have concerns about XY and Z. Let's sit down and talk about it and see if we can come up with a way that maybe would be would be better. So really, you know, being able to have all of this together and, and guide you, your, the sponsors study the sponsors product to to meet their end goal is really a great defining factor of our QM plus. Guido, any thoughts to add? Yeah, you said it very well already. But I would say the advantage specifically of requirements, we have physicians in house. So I myself, I'm a physician and experienced project and study manager and we have other experienced subject matter experts in house. So we are able to review study protocols. We are able to ask the sponsor the right questions as Carry already said and as we discussed. Storm. So for example, regarding the patient population indications. And we have regulatory experts in house, so. And reimbursement experts, so we can you know, discuss the, the strength of evidence required, you know, because I mean, one thing is to say, well, ideally we would like this, this, this and this, but then you might get a very large study and it will be very expensive and it will take a very long time. So it is always a balance between, you know, what is essential and what is, you know, really nice to have, but not essential and maybe you are not able to. Reported for whatever reason, either time or money. So this is a process and yeah, we are sure we can help the manufacturer, the sponsor of the study with getting. The most out of his investment by de risking, you know the study designed by de risking the whole the whole project basically. Yeah. OK, well I want to get into one of my more favorite parts, which is the statistical weeds. Now that said, you know, it's, it's, it's widely recognized that increasing the speed of enrollment leads to faster completion. It's logical, makes sense, right? And it also may be associated with fewer dropouts, better statistical power and increased confidence and results. Now, there's also an aspect of loss to follow up or LTF to contend with at the later stages of a study. Now, Ralph, given your extensive experience with study design. From a bias statistics perspective, how best? Do we avoid situations associated with four recruitment dropouts, missing data, and underpowered trials? I'm trying to get to aspects of statistics that may cause studies to fail, hence the question. The illustrated, yeah, in my experience, a clinical trial shouldn't be too complex. I have seen a lot of studies in the past which were definitely too complex. If a participation in the study is very time consuming for the patients, there is a high risk of course that the patient will either not participate at all at the study or that they will drop out early. So that we have a lot of terminations. So termination patients and a lot of missing data in the study database. So another important point from my point of view is a feasibility which is carried out before the study starts. In most cases the possible patient numbers are heaved here are too optimistic. And therefore I would suggest to recruit more centres than necessary, depending on the feasibility to increase the recruitment seed, because I think recruitment speed is one of the most important things here in this area. For sure, for sure. OK. OK. Well, moving on from statistics. And team any, anyone, any, any instances with defining what observations would constitute a clinically meaningful result rather than merely a statistically significant result that you're willing to discuss. I'm sure you've come across these examples, but anything that you're willing to discuss. Well, if I, if I might start, so I have a very good example or well it's not one example. I've been working in the field of AF detection with implantable devices for many years. So this is a prime topic or a prime indication where you where you can achieve many statistical results and still the question is what is what is clinically relevant, what is still have 50% of the patients come back. For reintervention very soon, that's that's also true. Absolutely. So I mean first of all you can say this is ideal you can with very few patients probably show that you have a a certain reduction or a certain detection threshold in AF. But then the right question to us is not OK, yeah, you can say great we've done some therapy and we've received a significant. I don't know, 80% reduction or 50% reduction with a highly significant, you know, statistics behind it. And then you, you of course you will go, let's say out into a physician and say, oh, well, we've seen reduction from 2 hours of AF to one hour. And then you'll say yes, So what, this is the prime example of, OK, you know, you have to really ask the question what is, what is clinically significant and compared to statistically significant? And we have. The company I've worked for a very large company, we're very well known and there's a lot of resources and internal and external subject matter experts. But never nevertheless they have struggled for quite a while with this. And so I mean this is the worst case probably example, but it's still applies to any. Trial design you can, you can think of, you know the same with pharma very, very often you can get into the discussion what is in a relative reduction, relative risk reduction or relative improvement versus. Yeah, the the absolute benefit in terms of, you know, patients improving, so a number needed to treat versus number, you know, benefit. And also sometimes number needed to harm versus number needed. To treat so you know, and this is where you have to look again, it's quite a lot of statistics involved, but you you have to look into that see the relation is appropriate to be able to, you know, get this therapy into the field from farmer. This is very clear that this has been used way back device manufacturers were a bit less used to this kind of analysis, but nevertheless it needs to be done because you otherwise you cannot show that your therapy or device. Will benefit the patient which is ultimately what is required, right You know what I'm hearing is you talk through these examples is to have a real form understanding of what that benefit risk story is going to look like from a patient perspective as well as from a user perspective up front right yes know what clinical outcomes lend to it and what corresponding measures need to be made as part of your endpoints to be able to justify that eventual. Benefit risk story because it it helps you as a manufacturer to, you know, maybe support some of the claims that you want to make eventually and it also helps that benefit risk story when it's the 11th hour. In terms of regulatory submission, there is increased focus on all these items both in the US as well as the EU and I'm sure other geographies are for are following suit. Real quick on this benefit risk story, it's really important we've seen that when it comes to real renovation. In that case, I would like to say lack thereof. But another device type that prominently comes to my mind is embolic protection devices. There are some devices that have failed. The primary endpoints and sometimes when you go and scrutinize the trial design manufacturers have powered the study only on major disabling stroke, right. Sometimes the stroke was not always neurologist adjudicated, it was only cardiologist adjudicated and we only cardiologist adjudicated stroke versus neurologist adjudicated stroke. That rates can be very different and that has a significant impact on the outcome of your trial. So careful thought in terms of what your eventual benefit risk. Or he needs to be and some of the outcomes that you need to be measuring to, you know, to try and help you justify that story both from a patient perspective as well as a user perspective. And in case of those embolic protection device, there is a cost associated with using it. So the whole healthcare system needs to weigh in as well as far as considerations for benefit risk is concerned. The point that we're trying to make folks is that it's multifactorial when it comes to even small aspects such as. The end points that you choose to go after. Now zoom out and think about how multifactorial the entire clinical study process could be. So whether you're choosing to go it alone or you're choosing to partner with a CRO, be very careful and try to recruit in as much experience and cross functional expertise there is available to help you succeed to this end with that. Thank you very much and please join us on the next edition of Talks around Clinical Trials. Thank you very much, ladies and gentlemen, Good bye. Bye. OK.