Are you curious about how to leverage integrated evidence plans (IEPs) effectively?
Do you want to know how statisticians can meet the diverse evidence needs of stakeholders and ensure the right data is available at the right time?
In this second part of our series on IEPs, we continue our exploration with Jenny Devenport, focusing on the practical application and timing of IEPs. Last time, Jenny shared the foundational aspects and significance of IEPs. Today, we dive into the specifics:
Key Points:
- Leverage IEPs: Practical applications and timing
- Stakeholder Needs: Diverse evidence requirements
- Data Customization: Format for different stakeholders
- Efficiency: Steps to minimize duplication
- Regulatory Affairs: Insights for regulatory professionals
- Medical Affairs: Strategies for Medical Teams
- Market Access: Enhancing market strategies
- Knowledge Gaps: Bridging information voids
- Informed Decisions: Data-driven strategies
- Product Success: Lifecycle management
Join us as we bridge knowledge gaps, make informed decisions, and drive the success of your products throughout their lifecycle.
We’ve delved into the practical strategies and timing of leveraging integrated evidence plans (IEPs) to meet diverse stakeholder needs. By customizing data formats and minimizing duplication, statisticians can play a crucial role in ensuring the success of pharmaceutical products throughout their lifecycles. Whether you’re in regulatory affairs, medical affairs, or market access, the insights shared today are designed to help you bridge knowledge gaps and make informed decisions.
If you found this episode valuable, don’t keep it to yourself! Share it with your colleagues who will benefit from understanding how to effectively utilize IEPs in their work. Together, we can elevate our practices and drive greater success in the pharmaceutical industry. Tune in, share, and let’s continue the conversation on how to make the most of integrated evidence plans.
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Jenny Devenport
Director, Biostatistics at Roche
Agile, results-driven, Biostatistics and Health Outcomes Leader with extensive experience building/ developing teams, encouraging effective cross-functional collaboration, and championing scientific curiosity to improve patient care through rigorous analysis and effective communication. Adept in devising and delivering change management strategies and organizational training to maintain employee motivation and focus in an evolving marketplace. Proficient at articulating and measuring the strategic impact of evidence generation and communication initiatives.
Transcript
How And When To Leverage Integrated Evidence Plans PART 2
[00:18:50] Alexander: Now let’s talk a little bit about this term evidence for a moment. Yeah, so a good integrated evidence plans [00:19:00] meets the needs of a variety of stakeholders. Yeah, that closes their knowledge gaps and helps them in their work. Now, when we as statisticians think about evidence, yeah, we think about the [00:19:20] source data, maybe we think about summary statistics and a table or figure.
[00:19:27] Now, what is for you evidence That actually helps decision makers to, you know, make [00:19:40] decisions within the organization as well as all the different external stakeholders that we talked about.
[00:19:47] Jenny: Oh, I looked at the head of medical affairs 1 reminded me that beauty is always in the eye of the beholder when it comes to evidence.
[00:19:55] And of course, different stakeholders have different questions. Health authorities [00:20:00] want to know, is this treatment safe and effective in the target patient population? Payers want to know, what value does the new treatment add in our patient population? But, beyond health authorities and payers, there are slightly different questions being asked.
[00:20:15] In clinical practice, people ask, which treatment will work [00:20:20] best for my patient? For this patient, sitting right here in my office. Yeah. And patients will ask. Which treatment will work best for me? And if you notice The difference between these questions is that these latter two in this post marketing second section are [00:20:40] predictive.
[00:20:40] They’re prediction problems and not and whereas payers and health authorities are working more at the population level. Now the population level can help to some extent predict what will happen with an individual patient, but it’s within a very [00:21:00] simplistic framework.
[00:21:01] Alexander: Yeah, well, you basically assume you get the average of everybody.
[00:21:05] Jenny: Yeah, you assume you get the average of everybody. Whereas, you know, these people want to know, am I making the right decision here? And so, you know, this can get a bit into decision science. This can get a bit into prediction. And, and this is where [00:21:20] a lot of questions get asked and where a lot of questions get raised.
[00:21:23] Alexander: Yeah. And there will be also lots of questions about data that you may not have collected for regulatory purposes. Yeah. And, and then the last thing is just by having this data. [00:21:40] doesn’t mean that it will actually be used. Yeah. So it not only needs to be there, it also needs to be there in the right format at the right time.
[00:21:56] Just like you can’t just send [00:22:00] over a FDA. Yeah. You need to adhere to their format. You need to write a report and integrated efficacy plan and all these kind of different things. Now, you need to do the same for payers. [00:22:20] And you also need to provide it in a customized way for all the different local affiliates around the world.
[00:22:31] So that they internally can work with it. and as well externally.
[00:22:38] Absolutely, and I think this is [00:22:40] really important. We tend as statisticians to focus on producing outputs and more recently we focused on defining and addressing estimates. But clinicians need A little bit different format to see the evidence is [00:23:00] meeting their needs. And so they need a bit more of a direct link between research question and research answer.
[00:23:06] And specifically, not just the question that you studied, but the question that they have. And how, how those two might or might not be related. So this is where scientific [00:23:20] publications are really important. This is not just an academic dinosaur exercise. This is, this is really important for clinicians.
[00:23:27] It’s also important from a regulation perspective because a lot of marketing efforts are regulated based on the fact that the science has been peer reviewed. If it’s, if it’s not [00:23:40] in the label, it better be peer reviewed before you discuss it among physicians.
[00:23:45] And these regulations actually vary quite a lot locally.
[00:23:49] Yeah. So, so if you’re a statistician, yeah, what can you do to add [00:24:00] value to the integrated evidence plan?
[00:24:03] Jenny: The number one thing you need to do is get to know the functional stakeholders and their needs. And I have some statisticians who say, well, how do I do that if I’m not invited to the meeting? That’s the first step.
[00:24:15] You get the invitation to the meeting. You approach the medics who you [00:24:20] probably know and you say, hey, I understand you’re working on the integrated evidence plan. I think I could help. Could I come to your meeting? And they might very well tell you, well, we don’t think we’re going to be talking about statistics yet, and you should tell them, well, hey, but I’d like to learn, and I bet you’re going to discuss some research questions, and it would be really [00:24:40] interesting to learn about and maybe participate in honing those research questions.
[00:24:48] I think the other thing that you really need to do as a statistician assuming that you can get to the table, and we’re very pushy, so we do, is, is help identify what are real evidence gaps. So [00:25:00] the statistician needs to know what data are already out there, whether it’s their companies or other companies.
[00:25:06] They need to know what evidence is already out there. They need to help. Teams select the right data sources based on their budgets, based on their research questions. They need to help with trade off considerations, you know, [00:25:20] how sure do you want to be in this answer? If you tell me you have 10, 000 francs, you probably don’t want to be very sure, but we can work with that.
[00:25:28] And, and also, how can we reduce complexity? Sometimes people come with very complex Ideas that are unnecessarily complex, and we may be able to help [00:25:40] them simplify or develop a research package that can be executed in the amount of time that they need.
[00:25:47] Alexander: Yeah, and all of these things. Yeah, if you can demonstrate that you can help with these things, then this will get you the ticket [00:26:00] to these tables and these discussions.
[00:26:02] Because then people will actually see, hey, it’s quite nice to work with the statisticians.
[00:26:10] Jenny: So that’s the other thing is when you show up, you have to be curious, you have to work on building relationships, and you cannot just sit [00:26:20] quietly the whole time. You need to be an active participant, you need to take notes, you need to connect the dots.
[00:26:27] And and present ideas. Now it’s not going to be a simple one and done exercise, and I would say that there is too many people surprised just as much, if not [00:26:40] more discussion before finalizing a research plan in the launch of life cycles. versus the clinical development plan space. But without building those relationships, you are left with the autopsy at the end.
[00:26:55] Alexander: Yeah. And so you can help people [00:27:00] understand, okay, you can get this type of evidence from this study. You can’t get that type of evidence from those studies. Yeah, if they say, Oh, yeah, we want to have real world evidence about our product compared to the others. By the [00:27:20] way, we actually don’t run an observational study and for claims databases will not have any efficacy variables in there and definitely not any Patient reported outcomes in there.
[00:27:37] And by the way, it will actually take a [00:27:40] couple of years after launch, if that we get enough patients there. And we will also not get comparative data from a single arm observational study. So helping people to understand what they can get. With which tools, for what price, at which time, is [00:28:00] absolutely critical.
[00:28:02] And this is something that nobody can do as well as statisticians. However, of course, that requires you to be open minded about more analysis than just phase 3. predict [00:28:20] confirmatory analysis. Yeah.
[00:28:22] Jenny: I also think again, depending on the size of your organization, you may be able to as a statistician and list some help from your PCOR statistician, from your patient reported outcome statistician, from a real world data scientist.
[00:28:36] You may be able to come with, you know, [00:28:40] a three person strong team to be able to say, here are the advantages of these different approaches. What would you like? Yeah, you know, giving people pros and cons so that they have a choice in what they do, but they really need to understand what they’re choosing is something that statisticians and data scientists are well practiced at.
[00:28:59] [00:29:00] And it really is to your advantage to do that in integrated evidence planning. It helps you with avoiding duplication. It helps you come up with robust evidence plans where you don’t have to get red faced with what’s out there in the marketplace.
[00:29:15] Alexander: Yeah, and I love the points that you talk about. Connect [00:29:20] with the, with your fellow other quantitative scientists.
[00:29:24] Yeah. They may sit in all kinds of different departments at local level, at regional, at a global level. They may sit in the health outcome department or in the PRO department, however, these departments [00:29:40] are called within your organization. So it’s really, really super important. To work with these and one other thing, sometimes they actually don’t work in your company, but on the vendor side, yeah, because lots of these things are [00:30:00] outsourced completely to to vendors and then having their good relationship helps quite a lot as well.
[00:30:09] Jenny: Absolutely. Because again, they may see more than what’s going on at your own company. And so they may have some interesting insights to provide if you can include them in the [00:30:20] discussion. Of course, you have to manage the risks yourself, but it can be really interesting and also important when you’re in a room with 20 positions to have three or four data scientists present who can come up with the pros and cons of various approaches [00:30:40] together.
[00:30:40] Alexander: Absolutely. So when you think about when, when you are, for example, not talking to someone that works mostly on the regulatory side, yeah, and it’s mostly content with FDA email approval and so on. What would you [00:31:00] tell these persons about the integrated evidence plan? Because, you know, They are mostly concerned with the clinical development plan, which is a sub plan.
[00:31:11] Why do they need to know about all the other things, and how can they help with these other things, or why should they actually care? [00:31:20]
[00:31:21] Jenny: It’s a great question and one I have had to address countless times over the last decade and, and what I would simply say is this, in a perfect world, first to market, there’s no competitors.
[00:31:36] You have a very simple, Nursing administration [00:31:40] and a very effective drug that works in most patients that has no safety signals to speak of. And as I said, you’re first to market and the FDA’s decision is easy. The EMA’s decision is easy. Payers are so glad to have a treatment option. There’s no standard of care and the drug [00:32:00] sells itself and that’s lovely. And that’s just not the environment we’re in. We’re in the better than the Beatles. situation where there is usually a standard of care, except in very rare diseases. There are competitors working on the same MOA frequently, the same mechanism of action frequently. And it’s [00:32:20] really hard to say who is going to be first.
[00:32:22] And maybe they all get health authority approval, but how do you persuade people to pay for and use your drug and how do you make sure that it benefits patients? We statisticians tend to be very snobby about marketing, but we do want patients to benefit from our work if our medicines [00:32:40] really work. And that’s why you should care.
[00:32:42] You should care about what evidence these other stakeholders need in order for your drug to get to patients. And that’s why it is worth spending some hours at the integrated evidence planning table talking about what evidence we have and listening to stakeholders to find out what [00:33:00] additional evidence is needed.
[00:33:02] Alexander: And I know people working in that space are very often quite busy. Yeah, especially at the time when work around the integrated evidence plan also piles up. Yeah. So kind of this, [00:33:20] let’s say 1 year before data readout of, you know, your first phase 3 study or whatever around these kind of times. Yeah. You are probably quite busy to making sure that.
[00:33:35] Or you have all the ducks in a row for your submission thing. [00:33:40] And then also these meetings come up about, you know, speaking about the German affiliates wants, and what the Canadians wants, and why there’s additional things that Japan, and China, and Korea, and whoever wants. Yeah. And maybe you think like, I really don’t have time [00:34:00] for that.
[00:34:00] Well, okay. But then at least bring in another statistician that has capacity and knowledge about all these different areas and sets your colleagues up for success. It is about all the statisticians working together here [00:34:20] effectively. To make this change and to have that impact.
[00:34:25] Jenny: Nobody wants to file a drug that doesn’t get used. Yeah. And I think that’s, that is the most specific example that I can give is that nobody wants to file a drug that patients will never [00:34:40] receive. And so it is worth it to figure out how to divide up the work so that statisticians and data scientists are at the table. To help with prioritizing and identifying needs and addressing them.[00:35:00]
[00:35:00] Alexander: And the better you work together there, the better it will be. Yeah. And both sides, people that work on the regulatory side and people that work on the post approval side can benefit from learning from each [00:35:20] other. Absolutely.
[00:35:21] Jenny: Absolutely. And these are, these are connections that will help you in every area of your work. To be remembered as a useful statistician, as an effective statistician, even. That, that you helped with the research question, [00:35:40] that you helped generate a design that was useful to them. And they’ll even forgive you when the research doesn’t get approved or doesn’t get cancelled, or doesn’t, It doesn’t pan out the way they expected it.
[00:35:54] The results are disappointing. What they remember is that you were there and that you [00:36:00] listened and that you gave your scientific best to their problem.
[00:36:06] Alexander: And also be available for all the things that is around the communication aspect later, you know, help with publications, help with the. Education [00:36:20] around these publications, set up your colleagues for success.
[00:36:25] That is a very, very important thing.
[00:36:28] Jenny: You know, that’s something that late phase statisticians talk about a lot. It’s not just being statisticians, but being drug developers. And that is still important in the post marketing space is really [00:36:40] understanding the entire drug development process, able to explain the evidence to other people who will, in turn, explain it to customers and being able to design the new evidence.
[00:36:51] So there’s just as much need to be a drug developer in the post marketing space as there is in the pre marketing space.
[00:36:59] Alexander: Yes. [00:37:00] Now, maybe that was quite overwhelming for you. Yeah. Oh, all these kind of different things. And, you know, we touched on higher level on lots of these different topics in terms of all these different plans, communication plans, the [00:37:20] evidence plans, the clinical development plan, the and so on.
[00:37:27] Yeah, it’s quite complex. Now, what can a person that works in this area, yeah, irrespective of [00:37:40] whether they work on as a contractor at the CRO side or within the pharma companies, what can they do to become better in that medical affairs space?
[00:37:53] Jenny: I think relationship building is a big. Part of it. If you don’t know what your [00:38:00] stakeholders needs are from talking to them, you don’t really know.
[00:38:03] You can read all the papers in the world and that can absolutely generate some insights about what your competitors are doing. But if you don’t know what your medical or commercial counterparts think about that, you may head in the wrong direction. So building relationships is a big, [00:38:20] big part of what it means to be an effective collaborator in this space.
[00:38:25] Alexander: Absolutely. The other thing is learn from other statisticians that already have experience. And here’s the launch and life cycle six that I talked about at the start of this episode [00:38:40] is a great place. Yes. There’s lots of very experienced people. From all kinds of different companies. And I love the atmosphere of sharing insights, sharing experiences sharing best practices from these different people.
[00:38:59] Jenny: [00:39:00] You know, that was, of course, a big motivating factor for us forming this safe. And I think certainly that is what has kept it going, is the fact that there are a lot of non competitive conversations that we can have about how to be more effective in this space by sharing our concerns [00:39:20] and realizing that we all share them we can figure out solutions.
[00:39:25] We can figure out, you know, how to address concerns about running single arm trials when there’s an opportunity to do more. We can talk about when real world data should be used [00:39:40] and when real world data is just not available. And how to convert real world data into evidence and not just more piles of data.
[00:39:48] Alexander: And last but not least, If you want to deep dive even deeper into this we also have have a program for people working in that space. [00:40:00] So if you’re interested about learning more about the launch and lifecycle space, check out the Effective Statistician Academy, and there’s also support there that can help you in that space.
[00:40:14] Both from the leadership aspect that we talked about, the influencing [00:40:20] aspects, there’s the leadership program, and there’s also specifically the launch and lifecycle program that Jenny and I have designed that will give you support to be more effective in that area. So with that, Thanks so much, Jenny, [00:40:40] for all the discussions about the integrated evidence plan.
[00:40:44] Last thing is, where will you talk about this next so that people can learn more about it?
[00:40:52] Jenny: We don’t have anything yet, so we’ll see. very much, though.
[00:40:57] Alexander: Okay, let’s see. [00:41:00] I’ll definitely also be at the PSI conference. Yeah, and so if you want to meet me there then definitely reach out and we can have a chat about these kind of different things.
[00:41:14] Jenny: We’ll talk about evidence generation at upcoming conferences in Basel in [00:41:20] September, so keep your eyes open.
[00:41:21] Alexander: Yes. So that is CFS Bioregulatory Workshops. That will be awesome as well. I’ll be there.
[00:41:27] Jenny: Definitely recommend that conference for a variety of reasons. It’s a fantastic opportunity to interact with academia, with industry, with regulators, and, and really tackle strategic [00:41:40] discussions in an interesting environment.
[00:41:42] Alexander: And for Very, very reasonable price, by the way. So, okay. So happy to connect with you in face to face situations like these conferences, or just connect on LinkedIn. And we are also [00:42:00] very, very happy to have you as a new member of the Gentle Life Cycle SIG. So these were lots of, lots of call to actions.[00:42:07] There’s one thing, if you don’t know what we all talked about, Just go to the effective statistician. com and you’ll find the links to all these kinds of different things that we talked about.
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