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Trial Design and Scientific Leadership: How to Boost Patient Recruitment in Asia and Around the World
Welcome to Xtalks webinar and today’s talk is entitled – Trial Design and Scientific Leadership: how to boost patient recruitment in Asia and around the world. My name is Jen and I will be Xtalks host for today and today’s webinar will run for approximately 60 minutes and this presentation includes a Q&A session with our speakers and this webinar is designed to be interactive and webinars work best when you’re involved so please, feel free to submit any questions or comments you may have for our speakers for the presentation and you could do so by using the questions chat box and we’ll try to attend to your questions during the Q&A session and this check box is located in the control panel on the right side of your screen and if you require any other assistance, please contact me at any time by sending a message using this chat panel. At this time all participants are in listen-only mode and please note that this event will be recorded and made available for future download.
At this point I would like to thank George Clinical who helped develop the content for this webinar. George clinical is a leading independent clinical research organization in Asia with over 260 staff operating in 11 countries. George Clinical provides the full range of clinical trial services to pharmaceutical medical device and biotech customers for all trial phases registration and post-marketing trials. George clinical combines scientific and clinical leadership with expert trial delivery capability to create a distinctive world-class service. Their parent organization, The George Institute for Global Health, is a leader in chronic disease research with a global network of experts with whom George Clinical engages and George Clinical delivers and operationally supported internationally-recognized scientific leadership service bringing together an extensive series of investigator networks that allow them to provide customizable clinical trial excellence from trial design through all aspects of delivery.
And now I would like to introduce our speakers for today’s webinar and our first speaker today is Professor Vlado Perkovic. Vlado is the Executive Director of The George Institute Australia, Professor of Medicine at the University of Sydney and a staff specialist in Nephrology at the Royal North Shore Hospital and his research focus is in clinical trials and epidemiology, in particular in preventing the progression of kidney disease and its complications. He leads several major international clinical trials, serves on the steering committees of several others and has led the development of George Clinical, the global clinical trials arm of The George Institute. He has been involved in developing Australian and global guidelines in kidney disease, cardiovascular risk assessment and blood pressure management and he holds a doctor of philosophy from the University of Melbourne and completed his undergraduate training at the Royal Melbourne Hospital. And our second speaker is Emma Field.
Emma is a global project manager with George Clinical with a massive science in clinical research and over 10 years of experience in the clinical trials industry and she has worked within small CROs and large pharmaceutical companies across phase I-III clinical designs. Roles include: data management; senior project management and operations management from initial design concepts through to marketing authorisation submissions. And within George Clinical, she leads a team of regional scientific managers within a robust infrastructure of operational support who collectively manage and coordinate the global scientific leadership of national leaders within a large program of phase III clinical trials. And Emma’s role also includes the coordination of executive steering committee and all subcommittees ensuring clear peer-to-peer communication pathways are established and maintained and leveraging the framework of national leaders and scientific committee members coordinated centrally, allows the delivering of George Clinical’s scientific model.
Okay at this point, I would like to hand over the mic to our first speaker for today, Vlado, and Vlado you may begin when you’re ready.
Thank you very much Dianne and it’s my great pleasure to be with you and to have the opportunity to speak to you and thanks for the kind introduction as well. These are my interests. I do have leadership roles in a number of trials and also a number of organizations that are involved in developing trial infrastructure and trial process.
So I’m going to go back to basics to start us off today. Why do we do trials? And fundamentally the reason is that randomized clinical trials are the only way, the only way, to reliably assess whether an intervention works and we can only get this sort of information if we do the trials well and to the highest scientific and operational quality. And as I’ll indicate throughout the talk today, a key issue that we need to remember is that we cannot separate the science and the operations; the two are intrinsically linked and each informs the other. Good science makes the operations easier and high quality operations deliver the science. Sometimes during a trial, we run the risk of forgetting that link and that can get us into trouble. So trials are hugely important and no amount of real-world observational evidence can replace the need for higher and for appropriate randomization. And it’s not just about assessing whether an individual treatment works, but sometimes we get surprises. This is a recent example of some randomized controlled trials in diabetes with novel glucose-lowering agents that have been developed. On the left-hand side is the SAVOR-TIMI trial, which was a trial of saxagliptin, a dpp4 inhibitor, very effective at lowering blood glucose and widely used as a class around the world that’s almost the most widely used diabetes agent. So when we see the large-scale randomized controlled trials that have looked at the effect of this agent on cardiovascular outcomes or for that matter other clinically important outcomes. The drug had no clear effect and so the drug lowers glucose but really doesn’t have any other benefits. On the right-hand side we have a figure from the EMPA REG outcome study, which is another glucose-lowering drug from the sodium glucose co-transporter class, which has a similar path slightly greater effect on glucose and therefore might be expected to have clinically similar effects to saxagliptin and the dpp4 inhibitor class and that was certainly the expectation. But when the randomized trials were done, showed a dramatic almost forty percent reduction in the risk of cardiovascular death. So these are major differences between these classes of agents that were unexpected based on what we understood about the mechanism of effect, but are dramatically important in terms of allowing patients and clinicians to choose the best drug to improve their outcomes if they’re affected by diabetes. So we cannot replace the need for high-quality randomized controlled trials but they have to be high quality and we have to do them well to achieve the outcomes that we want. So at this stage we’ll go to the first polling question and Dianne?
Thanks very much Dianne and we often forget what a relatively new field clinical trials is and the fact that this is still an area that’s evolving and developing rapidly and as part of that, it’s important that we look at what we’re doing and how we’re doing it and think about how we can do things better. And this is my own area, the area of kidney disease and some recent work that’s been done looking at the types of trials that are being done in kidney disease and as the results show that nephrology trials are generally smaller with the largest number of trials having fewer than 50 participants. They tend to be shorter, they’re more likely to be un-randomized and un-blinded than other specialists. Suggesting that we really need to work on improving the rigor of trials in nephrology and I think the same is true in the number of other areas.
So some of the challenges that we face, when we try and run clinical trials today probably top of the list, I think for most people in clinical trials, is challenges in recruitment; how do we get the right patients into of trials quickly enough and efficiently enough. But beyond that there are a number of other challenges that we face: keeping people in the trials and avoiding dropout from our trials but also drop-in where patients who are enrolled in trial start taking open label treatment if those treatments are on the market. The growing complexity of the trials we do and the processes that we undertake to manage those trials, the regulatory burden associated with that and the burden that this creates on both participants who are enrolled in the study, with the study visits the amount of data that’s collected, the drugs that are used etc. But also on the study sites with the visits and the processes that they need to undertake and all of the paperwork that needs to be documented, monitoring visits, the audits etc. It’s increasingly complex. The burden is growing and this is producing some challenges for sites who have limited capacity and this is an area which often interferes with their ability to perform to the best.
Another area is that in many parts of medicine, we are actually improving the care of our patients, which is a great thing but that means that the risk of them achieving some of the outcomes that we’re trying to prevent is reduced, again a good thing clinically, but it does make clinical trials potentially more challenging leading to larger trials being required or a never narrowing population being selected to try and maintain appropriate of end rates. With the growing need to enrol sicker patients and also the complexity of the treatment people are receiving, we also need to consider the increased risk of adverse effect, which can impact on the trials that we do and may lead us to failing to demonstrate benefits because for example we may have to stop trials early because of adverse effects and some of this and all of these aspects can be addressed to some degree at least by careful-thoughtful design up front; and this leads to include both considering the science and how we can maximize that, and also the operations and how the two linked together and I’m going to give you some examples over the next little while of how that can happen.
The last point there is, I think a very important one, which is the growing disengagement of trials from the clinical community. The original trials were run by the clinicians who are caring for patients to see whether the sort of treatments they thought worked, should work, did work. And they engaged other clinicians who were likeminded and committed to answering the question and they worked very hard together and that model still exists and it’s likely to be more successful in achieving outcomes. Conversely the danger I think that many of us run is that trials are seen as a business, by many of the clinicians involved that doesn’t necessarily benefit them or their patients, but rather is being undertaken for commercial reasons. I think that in that context sometimes the level of engagement is not as strong as it otherwise is. So trying to link the clinical need, the clinical benefits, and the trial itself is a really important aspect of ensuring a successful trial completion.
So this is clinical trials 101, and apologies for getting back to basics but I just want to highlight a few of the sort of themes that I want to talk about as we move on; about how we can improve the trials that we do and sometimes we forget about first one in particular that high quality clinical trials begins with asking the most important question that you can and sometimes that’s not necessarily the one that looks most easily answerable; sometimes that means we need to get more ambitious than we might have started off as but not always. But I think asking a very important question we can engage not only the clinicians but also the participants and optimize engagement and therefore clinical trial conduct.
Making sure we design the study reliably, should go without saying but is often an area where shortcuts might be taken to try and facilitate timely trial completion or keep costs down and typically that’s a mistake; maximizing the reliability of the study is also crucially important. Of course ensuring the studies well run, looking at rigger in operations and trial delivery is crucially important but also ensuring that the scientific aspects of the study are run well is very important because the two again go hand in hand. Getting patients on appropriate background therapies and understanding what those should be and developing a process for tracking, monitoring and intervening if required are crucial in modern clinical trials, particularly if we’re seeking to develop and register new treatments.
Finally, all of this needs to be done efficiently and the danger of growing complexity and growing costs means that many of the sorts of trials that we might want to do, might never get done; so keeping efficiency optimized is clearly important. What do I mean by asking the most important question just to start off with the top thing there and there are a range of considerations here thinking about the condition that we choose sometimes we tend to be overly niched by choosing a very narrow population, which is obviously important to those affected but maybe less important to the clinicians involved in running your study. Alternatively, choosing a condition which has a higher burden of impact on people affected is clearly one that’s more likely to get engagement from both the patients and the clinicians. So spending some time thinking about that is usually time very well spent.
Making sure we’ve got a good rationale for the intervention and that we explain that well to the clinicians in particular who are going to be involved in running the study. We need them as much as possible to believe that what we’re proposing to test is plausible and reasonable and ideally likely and exciting. Thinking about the sorts of treatments that we use, placebo control design, making sure background of care is optimized is important and one thing that we often forget. Choosing the most important outcomes and we often think about this from a regulatory perspective and have a check list of previously approved studies but perhaps we should spend a bit more time thinking about what’s most important to the people who are included in our studies and sometimes those outcomes can actually help us substantially; if we do all of this we can maximize investigator and participant engagement. If we think about patient priorities sometimes we can be surprised when we actually ask our outpatients what’s important to them. This is a recent initiative undertaken in kidney disease, again my area just as an example, where people who are receiving dialysis for kidney failure were asked what’s most important to them and the top issues that they identified were fatigue and energy; but very few trials are looking at this as an outcome. Mortality, which is typically being the clinical outcome of greatest interest to trialists was number 14 on the list for patients affected by kidney failure so there’s a clear disconnect between the trials that were doing and the priorities of the patients. Particularly when we start looking at the sort of trials that are undertaken in dialysis, actually the largest number of them look at biochemical parameters which of course is of no interest directly to patients themselves. So by thinking about some of these outcomes we can actually make sure the trials are most relevant; but also they can help us in fact make the trials smaller, a trial looking at the effect on fatigue or energies likely to be substantially smaller than one looking at the effect on mortality.
So getting the trial design right, getting the question right is crucial and can make a huge difference to our ability to live successfully. just a few minutes about trial rigour if you like in design and what we’re really trying to do and the answer is when we design a trial, we’re trying to avoid two major errors. One is systematic area or bias, and the other random error, which relates to power. So if we look at the four targets on the screen what we’d all like to do if we were shooting at these target is to achieve what’s in the bottom right where we’ve got a very accurate outcome with all of the shots clustered around the target and also a very precise outcome with little scatter. If we move to the bottom left figure there we’ve got still an accurate outcome, the average of all those spots is the centre of the target but we’ve lost precision, we get much more scatter from the individual points.
And this is a bit like what happens with clinical trials that are underpowered; we still on average will get the right result but each individual study potentially likely to give us the result which may not be entirely right. So, this is a question of random error or power. If we move up to the top right-panel, what we’ve got here is a very precise result, so this is one in the clinical trial context that would be adequately powered but where we have a systematic era, a bias, there’s something that’s affecting results and making them inaccurate that’s usually related to some aspect of trial design and that’s clearly something that we don’t want and the worst of all outcomes is top left where we’ve got both systematic error and bias, we’ve got a wide range of results that are not giving us the actual results even if we take the average of all of them. So, it’s important to think about these concepts when we’re designing our trials. And at this point we’ll move to polling question number two.
So, there’s a lot of trials going on and we have to minimize systematic area which will give us the wrong result it might give us a very precise wrong result but it’ll still give us the wrong result and the sorts of issues there that can affect results are: adequate randomization; allocation concealment is a crucial one; blinding; follow-up an intention-to-treat analysis and I’ll just give you a couple of examples here. This is a systematic review that we published in annals of internal medicine several years ago and highlights the fact that there are several types of studies which tend to overestimate the benefits of an intervention, particularly those that are small and inadequately powered shown by the number of events number of participants those that are shorter rather than have adequate duration and most importantly those that did not have allocation concealment. So, these factors can lead us to get the wrong results from the trial which is not only a waste of resources but it’s obviously exposing people potentially to risk for no purpose.
In addition, it’s not just small studies which we get trouble I’m just going to briefly talk about this example of the EVOLVE study which is a large 4,000 person randomized control trial running people with kidney failure requiring dialysis and randomizing them to cinacalcet medical or placebo and assisting the outcome on the effects on mortality and cardiovascular outcomes. EVOLVE was a very unlucky trial unfortunately; you can see there the baseline characteristics on the slide which generally look pretty similar but I’ll draw your attention to the median age which was one year older in the cinacalcet than in the placebo arm and these trialists, very reasonably, did what most trialists do and they did an unadjusted probable analysis and these are the results on the top you see the unadjusted primary outcome which showed a non-significant 7% reduction in the risk of the primary outcome – so a negative study overall. But the really disappointing thing is that when the results were adjusted for the difference which happened to occur by chance in age at baseline, in fact it suggests that there was a significant show in reduction in the primary outcome that was missed because of that unfortunate difference in age – and this could’ve been overcome here by using an adjusted primary analysis of the primary outcome for the study so it was an important learning experience I think for many of us who run these sorts of trials but highlights the sorts of challenges you can get into and the importance of learning from the science of previous trials.
What about recruitment? So, recruitment is the biggest challenge many trials face and we’ve talked about the importance of trial design in helping to facilitate recruitment from the point of view of engagement but also clearly it’s important in terms of affecting feasibility directly related to the inclusion criteria and the inclusion criteria need to be clearly as broad as possible and often we get too narrow with our inclusion criteria particularly for late phase studies. We can consider enrichment factors and one of the things that we’re doing more and more of course is considering the populations that we enrol and particularly expanding them across geographical areas to overcome some of the capacity challenges; and this is a really important opportunity for us. This slide just highlights the world’s population and where it sits, there’s my own country at the bottom, Australia, unfortunately we only have 0.3% of the world’s population but if we look at China, India and other parts of Asia, we have more than half of the planet living in that region yet most studies would involve a minority of patients from this part of the world; so clearly there’s an opportunity to grow infrastructure and grow capacity here. And just to highlight this issue more directly this is a hypothetical scenario that highlights the differences in population. Australia’s a relatively small country on the left, so if we were to recruit 100 patients to a clinical trial, we might expect the UK purely based on population relativity to recruit 300 and the US 1500 given that it’s 15 times as large.
But if we include China and India purely based on population relativity’s each would be expected to recruit over 6000 participants to this hypothetical trial now there are capacity issues that means that’s probably not realistic at the moment but this is the potential of the Asia region that we need to work to develop and realize. And it’s not just about the number of people but also the burden of disease again looking at people with kidney disease here and those requiring dialysis more specifically to some work that we did calculating the number of people with kidney failure requiring dialysis and already the majority of people receiving to dialysis around the world and expensive treatment are living in Asia – even today, and the gap between Asia and other parts of the world is projected to grow further over the next 20 years so by running more trials in Asia.
We can also more directly and helped to define treatments that can reduce the large burden of disease in this part of the world and that can help us all so clearly with recruitment. This is an example of a trial – The ADVANCED trial that we ran in diabetes where China and India made a very large contribution we see the typical regulatory delays for both China in the orange and India in the light blue on this slide such that they start at approximately 18 months later than other parts of the world but in this study, they were able to recruit about 3500 patients between them in about eight months, so clearly very large capacity to do a huge amount of work in the right context. So getting the region right is important but thinking about scientific engagement can help us in other ways this is a little study that we did in the ADVANCE trial which was a cluster randomized study that looked at what happened with sites if we gave them the usual levels of communication or if we gave them more direct communication led by our global scientific leaders who are writing directly to investigators at the sites and providing them feedback on their performance as well as overall trial conduct. And what we found as shown on this slide is that the sites who are randomized to get the enhanced engagement tended to reach half the recruitment targeting in this figure about 1.3 months earlier than those sites who just got usual care so by engaging them more directly we can potentially shave a few months over the overall recruitment period for some of our large studies in particular. This is translated into some other studies just to show you it’s not a fluke. This is another trial we were involved in looking at lipid-lowering in people with kidney disease where despite the fact that we were only leading four of the countries Australia New Zealand, Malaysia and Thailand, our sites recruited about fifty percent more participants on average then participants in other countries.
So the model can have a dramatic impact on recruitment and it also relates to the burden of disease as indicated in this slide on the left-hand panel we see the burden of disease according to income of countries in the light colour and you say that the low-income countries around the world have the greatest burden of disease but most of the trials that are being done are being done in the high-income countries almost all of them; meaning that we’re not actually directly studying the greatest burden of disease around the world. Another key issue in running clinical trials is follow up which is often challenging, can be very expensive but it’s crucial in terms of ensuring the reliability and validity of the study. This is again another example of the EVOLVE trial which we’ve talked about earlier, cinacalcet, where as well as being unlucky with the randomization according to age they also have problems because the drug was on the market and in fact by the end of the study only 45% of patients randomized to cinacalcet were still receiving the treatment but at 23% of participants in the placebo arm were receiving an open-label commercial product as well; and that meant again was that there was clearly a reduced opportunity to generate separation between the arms and show a benefit and when a range of statistical approaches we used to try and adjust for this dropout and drop in that strongly suggested that it was again an underlying benefit that wasn’t able to be demonstrated at least in part because of this issue. Adherence is another issue that is highly variable and can be different from site to site as everyone whose run trials would know but also which tends to be less of a problem in Asia than in other parts of the world; perhaps due to cultural factors this is again the ADVANCE trial that we’ve talked about already looking at adherence by region to randomize blood pressure line therapy at the end of the study and you can see that in Asia almost 85% of people were still receiving randomized therapy out beyond four years compared to a little under 70% in all other parts of the world so that’s another clear advantage for the Asia-Pacific region in terms of enrollment in trials and this is the same sort of data from the SHARP study; again showing much better retention and adherence to randomize therapy in the Asian region in other parts of the world and it is often the case in the USA unfortunately lagging on this measure.
So how can we ensure that retention is maintained if people stay in randomized therapy? Again, we need to start with the protocol in the science that needs to be simplified as much as possible to make the trial easier and we can use run in periods, placebo and active or inactive to help reduce this issue and we should think about ensuring that our trials are big enough to give us some room to move around power. So, what about quality? Well all of you know that we have to be able to demonstrate any trial that we to do is being reliably done and it’s critical if we’re going to believe the results and again it starts with the question; get the question right and integrate that into all aspects of the study design and then of course a whole range of issues that help us ensure the trials being done well. A major topic that we don’t have time to go into detail today is around monitoring and doing high quality monitoring. We can use a range of design considerations to help keep costs down keeping complexity as low as possible but no lower using the design to think about every aspect of the study, justifying every study visit, every test that’s being run and being brave enough to drop those things that are not essential to what we’re doing but that just cause clutter and burden on sites. Run in periods that we’ve mentioned can be very useful – thinking about routine data which may have a role in a range of circumstances not always but certainly in a number of circumstances and working with regulators to maximize our ability to use electronic data is the source record wherever possible. There’s a lot of work being said about risk based monitoring and certainly the concept of central monitoring with risk based source verification I think is the future and where we need to go but we need to be weary of using this is a buzzword rather than understanding what it means and using it intelligently to achieve our outcomes.
We’ve talked already about the value of low-cost high-quality regions and then maybe some new approaches and such as randomized registry trials, platform trials, umbrella trials which may offer the opportunity to improve efficiency in our clinical trials. Just a few words about this concept of complexity and the issue of pragmatic trials which are often confused in people’s understanding and really when we talk about pragmatic trials were talking about trying to keep the design of the study as close to routine clinical practice as possible and making it more relevant and the danger is that it may involve a broader range of patients that are less well defined and therefore increase variability and people think of pragmatic trials as being a single entity but of course that’s not true; there’s a spectrum between pragmatic trials and explanatory trials which are the more tightly defined trials often used in early phase research and certainly I think we need to move towards a more modern approach where we think through each aspect of trials and decide what’s appropriate for that individual trial thinking about pragmatic academic approach if you like which could keep cost down and streamlined at one end and a highly tightly defined explanatory approached at the other which is typically considered necessary for regulatory approvals and often is the case. But the majority of our trials should actually fit somewhere in the middle in this hybrid where we think through what we’re trying to achieve, what the risks are and the opportunities and come up with a specific solution for that trial and I think again, engaging the scientific leadership in that aspect has a lot to offer
The last point I’d like to make is that we’re not necessarily just talking about a trial being all pragmatic or all explanatory. In fact, there are a range of aspects of the study we need to think about and they’re listed in this nice figure that I like, the pragmascope, tells you how pragmatic something is and we can think about the different aspects of the study, the eligibility criteria, follow-up intensity, flexibility etc. that each of which may sit somewhere along that spectrum of complexity and if we think about each of these things individually we can minimize complexity and maximize the efficiency of our studies. So to summarize, what we need to think about are the key factors that can help us succeed in our trials. Firstly, making sure our trial’s important by choosing the right population, the right interventions, the right outcome and integrating that into the trial operations; making sure that our trials are reliable by avoiding bias and ensuring power is adequate. Making sure our trial’s rigorous but also to do this in a highly efficient and effective way and I think the keyway to do all of this is by having the scientists involved in the study and the operations group working tightly together both in the design but also then throughout the study in the delivery. And at that point I’m going to hand over to Emma for the next part of the presentation.
Thanks, Vlado. So, the first slide just outlines The George Institute’s strengths and also in it lies within a strong science background and also the knowledge of the large networks which enhances the engagement of the PIs and therefore the patients. Then George Clinical on the right-hand side which prides itself on quality delivery within the full-service area and we’re both side working together that gives an advantage and the uniqueness as it places George Clinical within the academic research organization that can bring those strengths into the scientific leadership model from a strong operational and strong scientific point of view.
And also to say that we are strengthened by science and innovation and not independent practice and that’s why we engage of scientific leaders so that we can use their expertise to achieve the strong science and the quality of delivery. The next slide is an overview of the scientific leadership which I’ll go into detail a little bit later in the slides but essentially scientific leadership is where distinguished researchers and clinicians from a highly respected and impactful Health Research Institute; they reward themselves to lead the clinical trial and due to the high level academic position, they already possess a reputation in that therapeutic area in addition their affiliation with research institute provides them with a level of influence that have the potential to inspire the investigators. The implementation of scientific leadership in clinical trial is also shown to motivate and engage investigators many of whom then maintain that connection with the scientific lead as long after the trial finishes. So, the scientific leadership model illustrates its value through its ability to develop and maintain robust engaged clinical networks.
To be effective, the model must also be carefully structured with a documented plan and supported by an operational team and as Vlado highlighted, the link between science and operations is important for robust trial design and conduct. And the operational support is a key aspect to highlight here as we all know that key opinion leaders, regional leaders are nationally time poor and the operational support that we provide through George clinical ensures that the plan is implemented and information collected and reported in a timely fashion.
When the model is also working at its best scientific leadership with integrated medical monitoring to ensure that site query is around eligibility and endpoint and safety events are also answered consistently and that trending information is available to the scientific team. So, the next slide shows the geographic locations; so the global presence for George clinical is that we have at our headquarters in APAC with majority of the studies having a global footprint. We also have operational support and that’s well represented in the Asia-Pacific area but also have project management in Europe and the US.
So, the next slide shows the scientific leadership and the goal of the model. So the key area to highlight here is the importance of the protocol design involving the national leaders, as mentioned earlier there are challenges in recruitment in a narrowing population for inclusion and often protocols are developed a step away from local practice; therefore, having national leaders reviewing and inputting from the current and local practice point of view, ensure that it is going to work you find the right patient population at the right centres. Scientific leadership also promotes the integration of scientific and clinical aspects of the study design into the conduct of the study. The goal is to maintain the communication flow from the steering committee to investigators and also vice versa and it maintains the scientific focus on the study and motivates optimal results in terms of patient recruitment and retention.
It also promotes peer-to-peer communication and the influence the national leaders have also shows operational aspects which is the number of patients lost to follow up are reduced, and keeping the PIs engaged and interested and also committed. The next slide shows the key activities and this shows them GCs involvement and the scientific leadership. So, contribute to trial design and strategy provide publication oversight of writing and GC and the Institute also have the strong network that we work with so we’re able to use the scientific input from national leaders. Also supports sponsor interactions with regulators where required and supports site identification and recruitment activities so there’s an efficiency and site selection through the use of scientific leaders to identify the correct sites and support them through that feasibility process. National leaders have themselves the peer-to-peer context to recommend the right sites for the right study reducing feasibility issues and potential recruitment issues further into the study. Also as outlined earlier, recruitment has proven to be quicker with scientific leadership communication compared to those studies without it and this early engagement helps to establish and promote effective and communication between highly respected leaders and the site investigators, which again enhances the PI engagement, enhances patient engagement to retention which then can lead to low screen failure rates and withdrawal rates.
The next slide shows the management of the committees. So, George Clinical also assists with the selection, appointment and contracting of the committee members, administer committee member payments and assist with the preparation of committee charters. And we’ve found that administrative support is a key aspects and important in management in the committee especially due to them being time-poor as well.
And the last slide shows slide show some of our scientific leaders and also some of the key therapeutic areas the Institute and George Clinical work in. And I hope this brief instruction has been insightful into the structure benefits of the scientific leadership model and I’ll hand back for the question and answer session.
One question is could you comment or any comments on how to preclude country effect due to various differences and the need for covariates analysis?
Yeah thanks Dianne. It’s an important issue and it certainly and comes up in trials and to be perfectly honest is probably given more weight than it deserves there are a number of reasons why different countries may have different results in a clinical trial and the most common of these is chance. If we do enough subgroups we’ll find some that look more effective and others look less effective purely as a result of chance so we need to be a little bit careful about using those sorts of analyses and to interpret them with caution. Having said that, there can be real differences perhaps the next most common cause of differences would be related to differences in background therapy or risk and in some countries there can be quite different practice patterns with regard to the sorts of treatments that people with a particular condition can get and can affect both the risk of having events and potentially also may interact with the treatment that’s being tested and perhaps most likely so that’s an important consideration and thirdly it is possible to have ethnic differences in response to a certain treatment. Again, these are a relatively uncommon and the most common explanation for differences is chance. So, there’s a few things that we can do to help address this.
The first is to be very cautious about the number of country analyses that we perform and also perhaps to group countries into regions that a similar rather than individual countries to make smart to minimize the random variation and that can help us draw real conclusions rather than keep getting distracted by some of the chance findings. But in terms of those real differences is one of the things that we can do is think about the background therapies to understand what sort of treatments are being used in different countries and to think about what that means for your study. You may wish for example to require all people to have statin therapy in a trial if you’re looking at some additional lipid-lowering agent on top of the statin to use an example and that may not be routine practice in some countries and it’s important firstly to be aware of that fact and then secondly think about how you’re going to handle it in your study, whether you’re going to provide background care, whether you’re going to require it and not only include people who are receiving background care or whether you think it’s unlikely to affect the study results and therefore can be can be left to usual clinical practice and all of these things again I think highlight the importance of engaging with some of the clinicians and some of the scientific leaders in each of those countries they can help you anticipate what those problems are going to be and also to develop appropriate strategies to deal with them. Lastly in terms of the true differences in treatment response again these are really important. They’re important if the drugs don’t work in a specific population that’s really important for us to know that. The lack of a blood pressure of ace exhibitors for example in African Americans has been well documented and is crucially important in managing those people appropriately. So, those sorts of differences we actually want to be able to recognize and are real positive. So, to summarize I think there are things that we can do to minimize the risk of distraction by designing the study carefully by engaging with local leadership but some of the regional differences we do actually want to know.
Another question we have: how do you reconcile the pragmatic approach that the scientific community recommend with a specific data that regulators expect or how do you manage these two at times competing aspects?
Yeah so they can often appear competing at first glance and I’ve had a quite a bit of experience interacting with regulators and my first comment is that regulators are smart, sensible, approachable people who are happy to think about issues with you and to think through some of the sorts of challenges that are faced. So, I think some our perceived regulatory issues can turn out to be wrong when we actually sit down and work through them with the regulator’s involved but that’s not always the case. There certainly can be challenges and I think and the answer is to think about this as a bespoke perspective as I say is this there are a range of different aspects of trials we need to think about individually, the degree of data collection can range from almost no data collection to a vast array of data collection and it might be particular areas of data that we’re interested in but thinking about that upfront and justifying every piece of data that we’re going to collect, because that is piece of data not only needs to be collected it needs to be checked, it needs to be monitored it needs to be co-monitored, it needs to be audited, it needs to be reviewed by the regulators; each piece of data generate so many steps of work that drives costs dramatically so by minimizing appropriately the amount of data that we collect by restricting the data to those things which are truly important rather than just being collected just in case, you know we can actually dramatically improve efficiency and that’s one example but there are a whole range of examples of different aspect of trials that if we think through for each individual trial and understand where the risks are we can come up with a plan to manage those risks and in my experience, this is the sort of approach that regulators are actually promoting and in many cases demanding. So i’m not sure that there is such a conflict perhaps as there once was, there are still always challenges and there’s still always a conservative and that means that some of the most pragmatic approaches may not be appropriate for trials that are regulated but that doesn’t mean that we can’t try and be more pragmatic whether it’s appropriate. As I said during the talk, we want to be as efficient as appropriate but no more and that’s I think a key thing we want to make sure is appropriate.
Next question is: how can existing data such as patient registries be used to enrich the datasets of our cities?
Yeah it’s a really interesting question. So, there’s a whole range of ways that we can use existing data both registries but also routinely collected patient data to help us more effectively run our clinical trials. Firstly, clearly by informing the design of the trials, understanding who the populations are, how they’re being treated, what their outcomes are and how we get a sufficiently high risk population. For some of our trials are clearly important benefits and they’re been used widely for many years but what we’re increasingly moving towards is using already collected sources of data to improve the conduct of our trials and that can be done directly for example by using registries or data sources as mechanisms for identifying potential participants for clinical trials and that’s obviously helpful. But also to help us by thinking about whether we can use some of the routinely collected data to actually incorporate as outcomes in our trials, so that’s I think an approach that’s becoming increasingly popular and will continue to do so and taken to its ultimate extent we moved to the randomized registry trials where trials and particular if you like late phase trials are being run entirely within registries where randomization occurs centrally; but in fact all of the data is collected through routine data sources, mostly in registries at the moment but I think over time linkage with administrative data sets is going to be increasingly important way that we can conduct some of these very important trials that could potentially be very expensive in a much more efficient and much more effective and frankly a much more generalizable fashion.
Great thank you very much for those answers. We have reached the end to the question-and-answer portion of this webinar and if you have further questions please our identify the email address shown on your screen and that’s to Scott Clark at email@example.com. And now please join us in thanking both our speakers today: Vlado Perkovic and Emma Field, thank you both.
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