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Real-world evidence generation with David Thompson
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Real-world evidence generation with David Thompson
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T00H12M00S
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Upload Date:
2020-04-21T00:00:00.0000000
Transcript:
Language: EN.
Segment:1 Real-world evidence generation with David Thompson.
[MUSIC PLAYING]
Segment:2 Please could you introduce yourself, and briefly summarize your main roles & responsibilities in your current position?.
DAVID THOMPSON: So my name is David Thompson, and I'm a Senior Vice President in the Real World and Late Phase Research Division at Syneos Health. Syneos is a unique company that combines both contract research organization capabilities with a complete line of commercialization services. So we have a complete lab-to-life model for evidence generation and product commercialization. What I do with our clients is I assist on real-world research design.
DAVID THOMPSON: So I focus on the really difficult problems of ensuring that the studies that we design and conduct for study sponsors actually will meet the evidentiary needs of the health system stakeholders to which they are targeted.
Segment:3 In your opinion, what has been the most innovative way a company has used real-word evidence to receive a regulatory decision?.
DAVID THOMPSON: So the interesting thing here is that FDA has signaled strongly various ways in which they are receptive to the use of real-world data, real-world evidence for regulatory decision-making.
DAVID THOMPSON: So traditionally they've done so for things like ongoing safety surveillance in a post-marketing sense, but now they're actually considering use of real-world evidence for product approval, and in particular, label extensions. And so recently, Pfizer, for example, gained regulatory approval-- or, sorry, a label extension-- for their product IBRANCE, which was brought to market for the treatment of breast cancer.
DAVID THOMPSON: Obviously, all of the clinical trials were performed in women, but there's a small segment of the patient population that are actually men. It's a rare disease in men, but it does occur. And the product has been used in real-world practice in men, and so Pfizer submitted data on those real-world treatment patterns and the safety profile of the product, and FDA actually granted the label extension. So that's a very important development, and we'll be looking for more of those examples as well.
DAVID THOMPSON: The other area is in single-arm clinical trials. So a couple of companies have been submitting single-arm trials, using real-world data sources to identify external control groups, that could be then compared to the patients who received the intervention in the trial. So there's a number of examples of that that have been successful as well, and we'll be seeing more of that in years to come, too.
Segment:4 How important is engaging with stakeholders such as the FDA during the design phase of real-world research?.
DAVID THOMPSON: Yeah, from our perspective, it's absolutely crucial. And the reason is is that historically, real-world evidence generation did not follow the typical way in which clinical evidence is generated, which begins with engagement with FDA and EMA to ascertain what kinds of efficacy measures will be of interest, the patient population of interest, and so forth.
DAVID THOMPSON: Only then is the study designed and executed, and thereby, the results are thereafter shared with the regulatory authorities. Historically, real-world evidence generation did not follow this process. And so study sponsors would meet with their consultants, and they would design studies thinking they knew what the health system stakeholders were interested in, but oftentimes it's a game of hit or miss.
DAVID THOMPSON: And so what we always advocate is that you engage with the health system stakeholders first. It's not just regulatory when you're talking about real-world evidence. It's payers at the very top of the list, health technology assessment agencies as well, patient organizations, and clinicians. So you have five different health system stakeholders that are going to have different evidentiary needs, and it's important to talk to them upfront such that when you develop your body of real-world evidence, you have a good sense that it's going to meet their decision-making needs.
Segment:5 What other factors should be considered to develop regulatory-grade real-world evidence?.
DAVID THOMPSON: This is something that's really interesting, and it kind of cuts both ways. So what this refers to is the greater proliferation of real-world data sources such as electronic health records and health care claims data, and the growing availability of those data sources for use in research purposes. But in addition to that, you have to have the skills to be able to analyze the data.
DAVID THOMPSON: So then you have these data platform technologies that are being brought to the market, and there's a number of vendors out there that are developing these platforms. And what they do is make it easy for those who don't have programming expertise or a lot of analytics training to be able to, with a few clicks of a mouse, dive into the data, start constructing a cohort, analyze a few measures, and look into things.
DAVID THOMPSON: So the reason I say it cuts both ways is that that democratization and the widespread usage of the data by a greater number of people is a good thing, right? We don't want to restrict it to a set of scientists who can only analyze the data. But at the same time, if you don't have the required training on data analysis, then these tools might be able to get you into some trouble. It's like handing the keys to a Ferrari to a 16-year-old who's just passed his driver's test. Trouble's going to follow.
Segment:6 What are your thoughts on whether real-world evidence can become 'regulatory-grade'?.
DAVID THOMPSON: The key thing here is to ensure that the data are collected in the right way, that they match with the therapeutic area and the intervention being considered, and that the analytic techniques are appropriately sophisticated and rigorous such that the data will have as much quality and validity as possible.
DAVID THOMPSON: The trick, though, is to recognize that the typical standards from regulatory include two things that you can never get from real-world data. Number 1 is randomization to treatment assignment. So in real-world data sources, treatment assignment is done by process of a doctor and a patient meeting together and deciding upon the best treatment course depending upon the patient's presentation. And a trial, of course-- it's a coin flip.
DAVID THOMPSON: You're randomized to Drug A or Drug B, or Drug A and placebo. So there's a lot of benefits to randomization from an analytics perspective that you'll never be able to recreate with sophisticated methodologic techniques for analysis. So that's a problem.
DAVID THOMPSON: Blinding is another one. So in real-world practice, patients know what drugs they're on. So do their doctors. Everyone knows what drugs they're on. Blinding is something you can't replicate in real-world practice either, and so those two aspects of clinical research in the traditional sense are something that is going to be of the final hurdle that prevents regulatory bodies from using real-world data sources-- as much as they might like to-- because the comfort level is going to be tough to get to that point.
Segment:7 What is virtual research and how is it being used in real-world research? .
DAVID THOMPSON: Virtual research is something that I'm very, very interested in at this point in time. I'm doing a lot of presentations on this. And what it refers to is the use of technology to essentially alleviate the burden of traditional research approaches on study sites and investigators. So it takes the power of the patient and places it at the center of the research process, and essentially says, look, we're not going to have to require patients to come into a clinic whereby they get poked and prodded and measured and various things happen to them.
DAVID THOMPSON: We could simply send measures out to their connected devices, whether these are their smartphones, whether these are Fitbits or other kinds of wearables, whether these are sensors in the home, including scales and so forth. There's a variety of ways in which data collection can be removed from actual research sites and take place out in the real world, and that's a really exciting development.
DAVID THOMPSON: So we're going to see more of this approach as things progress. Why? Because, first of all, it's trendy, and there's a lot of ability to make use of these novel technologies, and so everyone's interested in it just from a curiosity standpoint. But it's also the case that site management and investigator management in trials is a really expensive aspect of the research process, and so to the extent that we could eliminate-- or at the very least dramatically reduce-- the burden on sites and investigators, the costs of doing the research will fall as well.
Segment:8 How do you see real-world evidence generation developing over the next 5 years, particularly in light of the FDA RWE guidance expected in 2021?.
DAVID THOMPSON: No one has a crystal ball, but there's some clear trends that we can identify. And I think there's this confluence of the greater proliferation of real-world data sources and the widespread availability of these data sources coming into the marketplace for researchers, for study sponsors, for patients themselves to analyze the data, the technology element with these data platforms coming into vogue, and using all these things with cloud-based computing.
DAVID THOMPSON: I think we're going to enter a realm in which we have continuous research and implementation of results as part of this, quote unquote, "learning health care system." So imagine going to your physician and being in the context of a discussion with that doctor in which you have a particular health problem, and the diagnosis is rendered. And as that doctor is looking at his or her iPad and clicking things into the electronic medical record, that information, along with all of your other information-- your demographics and your clinical history and your health-seeking behaviors and so forth-- they go up to the Cloud, where they interact with some risk-based modeling-- predictive analytic modeling-- such that decisions are made based on a whole lot of data-- not just your own case study, but all the patients that are like you-- and the predictive analytics contribute back to that interaction, in real time, a suggestion on what therapy you would receive.
DAVID THOMPSON: So the doctor might say, typically I prescribe Drug A in this particular situation, but the data and the methodologies are suggesting that maybe we should try Drug B instead. It might be better for you as an individual. So all this comes together with things like all the buzzwords out there-- so the learning health care system, patient centricity, precision medicine. All of these things can come together such that there's not health care and research, but they all blend together and become one.
DAVID THOMPSON: So that's something that we might be seeing in the years ahead.
Segment:1 Real-world evidence generation with David Thompson.
[MUSIC PLAYING]
Segment:2 Please could you introduce yourself, and briefly summarize your main roles & responsibilities in your current position?.
DAVID THOMPSON: So my name is David Thompson, and I'm a Senior Vice President in the Real World and Late Phase Research Division at Syneos Health. Syneos is a unique company that combines both contract research organization capabilities with a complete line of commercialization services. So we have a complete lab-to-life model for evidence generation and product commercialization. What I do with our clients is I assist on real-world research design.
DAVID THOMPSON: So I focus on the really difficult problems of ensuring that the studies that we design and conduct for study sponsors actually will meet the evidentiary needs of the health system stakeholders to which they are targeted.
Segment:3 In your opinion, what has been the most innovative way a company has used real-word evidence to receive a regulatory decision?.
DAVID THOMPSON: So the interesting thing here is that FDA has signaled strongly various ways in which they are receptive to the use of real-world data, real-world evidence for regulatory decision-making.
DAVID THOMPSON: So traditionally they've done so for things like ongoing safety surveillance in a post-marketing sense, but now they're actually considering use of real-world evidence for product approval, and in particular, label extensions. And so recently, Pfizer, for example, gained regulatory approval-- or, sorry, a label extension-- for their product IBRANCE, which was brought to market for the treatment of breast cancer.
DAVID THOMPSON: Obviously, all of the clinical trials were performed in women, but there's a small segment of the patient population that are actually men. It's a rare disease in men, but it does occur. And the product has been used in real-world practice in men, and so Pfizer submitted data on those real-world treatment patterns and the safety profile of the product, and FDA actually granted the label extension. So that's a very important development, and we'll be looking for more of those examples as well.
DAVID THOMPSON: The other area is in single-arm clinical trials. So a couple of companies have been submitting single-arm trials, using real-world data sources to identify external control groups, that could be then compared to the patients who received the intervention in the trial. So there's a number of examples of that that have been successful as well, and we'll be seeing more of that in years to come, too.
Segment:4 How important is engaging with stakeholders such as the FDA during the design phase of real-world research?.
DAVID THOMPSON: Yeah, from our perspective, it's absolutely crucial. And the reason is is that historically, real-world evidence generation did not follow the typical way in which clinical evidence is generated, which begins with engagement with FDA and EMA to ascertain what kinds of efficacy measures will be of interest, the patient population of interest, and so forth.
DAVID THOMPSON: Only then is the study designed and executed, and thereby, the results are thereafter shared with the regulatory authorities. Historically, real-world evidence generation did not follow this process. And so study sponsors would meet with their consultants, and they would design studies thinking they knew what the health system stakeholders were interested in, but oftentimes it's a game of hit or miss.
DAVID THOMPSON: And so what we always advocate is that you engage with the health system stakeholders first. It's not just regulatory when you're talking about real-world evidence. It's payers at the very top of the list, health technology assessment agencies as well, patient organizations, and clinicians. So you have five different health system stakeholders that are going to have different evidentiary needs, and it's important to talk to them upfront such that when you develop your body of real-world evidence, you have a good sense that it's going to meet their decision-making needs.
Segment:5 What other factors should be considered to develop regulatory-grade real-world evidence?.
DAVID THOMPSON: This is something that's really interesting, and it kind of cuts both ways. So what this refers to is the greater proliferation of real-world data sources such as electronic health records and health care claims data, and the growing availability of those data sources for use in research purposes. But in addition to that, you have to have the skills to be able to analyze the data.
DAVID THOMPSON: So then you have these data platform technologies that are being brought to the market, and there's a number of vendors out there that are developing these platforms. And what they do is make it easy for those who don't have programming expertise or a lot of analytics training to be able to, with a few clicks of a mouse, dive into the data, start constructing a cohort, analyze a few measures, and look into things.
DAVID THOMPSON: So the reason I say it cuts both ways is that that democratization and the widespread usage of the data by a greater number of people is a good thing, right? We don't want to restrict it to a set of scientists who can only analyze the data. But at the same time, if you don't have the required training on data analysis, then these tools might be able to get you into some trouble. It's like handing the keys to a Ferrari to a 16-year-old who's just passed his driver's test. Trouble's going to follow.
Segment:6 What are your thoughts on whether real-world evidence can become 'regulatory-grade'?.
DAVID THOMPSON: The key thing here is to ensure that the data are collected in the right way, that they match with the therapeutic area and the intervention being considered, and that the analytic techniques are appropriately sophisticated and rigorous such that the data will have as much quality and validity as possible.
DAVID THOMPSON: The trick, though, is to recognize that the typical standards from regulatory include two things that you can never get from real-world data. Number 1 is randomization to treatment assignment. So in real-world data sources, treatment assignment is done by process of a doctor and a patient meeting together and deciding upon the best treatment course depending upon the patient's presentation. And a trial, of course-- it's a coin flip.
DAVID THOMPSON: You're randomized to Drug A or Drug B, or Drug A and placebo. So there's a lot of benefits to randomization from an analytics perspective that you'll never be able to recreate with sophisticated methodologic techniques for analysis. So that's a problem.
DAVID THOMPSON: Blinding is another one. So in real-world practice, patients know what drugs they're on. So do their doctors. Everyone knows what drugs they're on. Blinding is something you can't replicate in real-world practice either, and so those two aspects of clinical research in the traditional sense are something that is going to be of the final hurdle that prevents regulatory bodies from using real-world data sources-- as much as they might like to-- because the comfort level is going to be tough to get to that point.
Segment:7 What is virtual research and how is it being used in real-world research? .
DAVID THOMPSON: Virtual research is something that I'm very, very interested in at this point in time. I'm doing a lot of presentations on this. And what it refers to is the use of technology to essentially alleviate the burden of traditional research approaches on study sites and investigators. So it takes the power of the patient and places it at the center of the research process, and essentially says, look, we're not going to have to require patients to come into a clinic whereby they get poked and prodded and measured and various things happen to them.
DAVID THOMPSON: We could simply send measures out to their connected devices, whether these are their smartphones, whether these are Fitbits or other kinds of wearables, whether these are sensors in the home, including scales and so forth. There's a variety of ways in which data collection can be removed from actual research sites and take place out in the real world, and that's a really exciting development.
DAVID THOMPSON: So we're going to see more of this approach as things progress. Why? Because, first of all, it's trendy, and there's a lot of ability to make use of these novel technologies, and so everyone's interested in it just from a curiosity standpoint. But it's also the case that site management and investigator management in trials is a really expensive aspect of the research process, and so to the extent that we could eliminate-- or at the very least dramatically reduce-- the burden on sites and investigators, the costs of doing the research will fall as well.
Segment:8 How do you see real-world evidence generation developing over the next 5 years, particularly in light of the FDA RWE guidance expected in 2021?.
DAVID THOMPSON: No one has a crystal ball, but there's some clear trends that we can identify. And I think there's this confluence of the greater proliferation of real-world data sources and the widespread availability of these data sources coming into the marketplace for researchers, for study sponsors, for patients themselves to analyze the data, the technology element with these data platforms coming into vogue, and using all these things with cloud-based computing.
DAVID THOMPSON: I think we're going to enter a realm in which we have continuous research and implementation of results as part of this, quote unquote, "learning health care system." So imagine going to your physician and being in the context of a discussion with that doctor in which you have a particular health problem, and the diagnosis is rendered. And as that doctor is looking at his or her iPad and clicking things into the electronic medical record, that information, along with all of your other information-- your demographics and your clinical history and your health-seeking behaviors and so forth-- they go up to the Cloud, where they interact with some risk-based modeling-- predictive analytic modeling-- such that decisions are made based on a whole lot of data-- not just your own case study, but all the patients that are like you-- and the predictive analytics contribute back to that interaction, in real time, a suggestion on what therapy you would receive.
DAVID THOMPSON: So the doctor might say, typically I prescribe Drug A in this particular situation, but the data and the methodologies are suggesting that maybe we should try Drug B instead. It might be better for you as an individual. So all this comes together with things like all the buzzwords out there-- so the learning health care system, patient centricity, precision medicine. All of these things can come together such that there's not health care and research, but they all blend together and become one.
DAVID THOMPSON: So that's something that we might be seeing in the years ahead.
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