Are you curious about how benefit-risk assessments in healthcare evolve?
Do you wonder how patient preferences shape these evaluations?
Today, I speak with Tommi Tervonen, a leading expert in this field, to answer these questions and more. Tommi, the Chief Scientist at Kielo Research in Zurich, brings nearly two decades of experience in Multi-Criteria Decision Analysis (MCDA), patient preferences, and benefit-risk evaluations.
This episode sheds light on the past, present, and future of benefit-risk assessments and shows how incorporating patient input is transforming healthcare decisions. Join us as we dive deep into this fascinating topic and discover the importance of structured methodologies and patient-centric approaches in making informed healthcare choices.
Key Points:
- Benefit-risk assessments: Evolution in healthcare evaluations.
- Patient preferences: Importance in benefit-risk evaluations.
- Tommi Tervonen: Chief Scientist at Kielo Research, an expert in MCDA and patient preferences.
- Experience: Nearly two decades in benefit-risk assessments.
- Structured methodologies: Importance in benefit-risk decisions.
- Patient-centric approaches: Role in shaping healthcare choices.
- Past, present, and future: Exploration of benefit-risk assessments.
- Healthcare decisions: Impact of patient input and structured assessments.
We explore the critical role of benefit-risk assessments in healthcare, the importance of patient preferences, and how these elements shape informed decisions. Tommi’s insights provided a deep understanding of the evolution and future trends in this field.
If you found this discussion valuable, share this episode with your friends and colleagues who can benefit from understanding the importance of structured methodologies and patient-centric approaches in healthcare. Let’s spread the knowledge and contribute to more informed and effective healthcare decisions.
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Tommi Tervonen
Benefit-risk Leader, Chief Scientist at Kielo Research, Co-founder at Valorem Health
Dr Tervonen is a key opinion leader in patient preferences and benefit-risk assessment. His expertise influences decision-maker opinion and he has been consulted by key agencies such as the US Food and Drug Administration (FDA) and the UK National Institute of Health and Care Excellence (NICE). Dr Tervonen leads scientific initiatives such as the ISPOR Task Force on Good Practice in Quantitative Benefit-Risk Assessment and works with experts from the FDA and European Medicines Agency (EMA) to advance the use of patient preferences in regulatory decisions. Dr Tervonen has served as principal investigator in dozens of patient preference studies, including an IMI PREFER industry case study, and supported sponsors in using the study results in regulatory filings to EMA and FDA. Before founding Kielo Research, Dr Tervonen led Evidera’s patient preference team and held academic positions at Erasmus University Rotterdam, Aalto University in Helsinki, and University Medical Center Groningen.
Transcript
Benefit-Risk
[00:00:00] Alexander: Welcome to another episode of the Effective Statistician. Today I’m speaking with Tommi, who is a well known expert in the overall benefits risk space and especially also when everything about patient preferences, which of course [00:00:20] play a big role there. But maybe Tommi, you can introduce yourself first.
[00:00:27] Tommi: Sure. Thanks, Alexander. Nice to be hopefully having a lot of people listening to, to this episode of the effective statistician and spawning more interest in the, in the benefit risk in here. My name is Tommi Tervonen, chief scientist at Kielo [00:00:40] Research based here in Zurich, Switzerland.
[00:00:42] We’ve been working in MCDA patient preferences and, and the benefit risk assessment for, for close to two decades now. Yep. That’s me.
[00:00:49] Alexander: And you have also published quite extensively on these topics. So you attend lots of conferences, give lots of presentations and yeah, you have a growing team [00:01:00] working on that.
[00:01:01] Tommi: Yeah. Yeah. It is a great time to be working in this domain. I have to say it’s been, it’s been coming for some time the benefit risk and, and I, I think with with the need for more patient preference information into feeding to benefit risk enhancement, it has been, well, increasing quite drastically the need for such work [00:01:20] recently.
[00:01:20] And the people also coming from to work in the domain.
[00:01:23] Alexander: Yeah. And I love that. I should not go there because then I would spoil the content of the episode. So Today we want to talk a little bit about the past, present, and future of benefit risk. And if you [00:01:40] think about the start of benefit risk, where has that been in, in the past?
[00:01:48] Tommi: Well, it’s probably been there for, for longer than, than I’ve been alive, but I mean, I started working on the benefit risk around mid to late two thousands of the first [00:02:00] decade of the, of the two thousands. And around, around those times you had the European Medicines Agency’s benefit risk project.
[00:02:07] You had the IMI protect kicking off and doing the work during those days. I think that’s when it really started being exciting the benefit risk assessment space because there was, there was a recognized need to do more [00:02:20] structured benefit risk assessment specifically from the, from the regulatory side, they recognize the need for that to just have more, let’s say, transparent benefit risk decision making.
[00:02:32] And transparency in this also then translates into better decision making. So you had the interest from the EMA. You [00:02:40] had the public private partnerships within the IMI protected. And I think from that onwards, it’s been just increasing the amount of benefit risk that has been done. So I would say that as a starting point.
[00:02:53] Alexander: Yeah. I think that it gets interesting for statisticians because it’s more structured and with [00:03:00] that is also more data driven and not just kind of, Oh, we have a discussion about it and then we decide. Yeah. Which is really kind of, as you said, some kind of in transparent and not very reliable way of assessing the risks of the benefits.[00:03:20]
[00:03:20] Because that’s in the end, one of the fundamental things. And especially as Risks come with so many different problems as the benefits. Yeah. We do lots of statistical analysis around what the treatment effects are and we [00:03:40] have, you know, some specified as primary objectives. They are studied, you know, multiplicity adjusted.
[00:03:49] With everything that you need, yeah, whereas the safety side is much more problematic because very often, well, you don’t know what you will get. [00:04:00] Pre specification is very difficult. Also, you know, you look the other way around, kind of, you don’t want to show a difference you want to kind of assess that there’s not so much of an issue.
[00:04:13] Thank you. And so the problem is much more about estimations and about [00:04:20] testing. You probably get data from, but also uncontrolled settings. Yeah. Like long term follow up over time. Yeah. When it comes to later in the life cycle, maybe you get also good observational data and all these kinds of things where, where it’s about is the benefit for us still positive.[00:04:40]
[00:04:40] Yeah. So getting more structures there is really important. What do you think was the biggest change in terms of said structural setup that happens there?
[00:04:54] Tommi: I think just actually describing what are the key benefits and [00:05:00] key risks or in ordinary terminology of the European Medicines Agency, the key favorable and key unfavorable effects.
[00:05:07] I think, I think that was. If you now look at it, how is the, what is the called the FX table in the, in the ePars, it seems like it seems like so, so trivial and so [00:05:20] simple that everybody probably now who comes to the field now thinks that this is how it always has been, but that’s not the case. This is not how it always has been.
[00:05:29] It was not always that way. The assessors were required to describe the key favorable and unfavorable effects that are driving their benefit risk decision. And, and it’s still not [00:05:40] like that at the FDA side. So there, the benefit risk framework that the FDA uses in their evaluation is much more unstructured.
[00:05:49] Alexander: Yeah.
[00:05:50] Tommi: Yeah. So I mean, if we Talk to any statisticians about this as probably going to be all the listeners of most of the listeners of this podcast. They think that, yeah, that’s how you [00:06:00] should be doing it. But I mean, when you go to the field, there are a lot of people who are making these decisions who are coming from a less quantitative background.
[00:06:08] Alexander: And kind of deciding what actually goes into this effects table of what are the most important favorable and unfavorable events or end [00:06:20] points. It’s not trivial, you know, it is you can’t just say, well, we have our primary and secondary endpoints there, and then we put kind of the most frequent adverse events or something like this.
[00:06:32] It’s absolutely not that. So just kind of having a good discussion around this is already very, very [00:06:40] beneficial. And then it often is not just a table where it’s kind of effect sizes and confidence intervals and p values. That also comes with some graphical displays. And so these can also have various forms from forest plots to waterfall [00:07:00] plots or, or anything like that.
[00:07:03] Tommi: Yeah, yeah, absolutely. And it’s, essentially the effects table is a, is a communication device. So it is somebody’s view of the world. I mean, you could call it a model. I mean, it’s not a model in the way that statistical models are models, but it’s a model that matters for the [00:07:20] decision making.
[00:07:21] And I, I mean, but, but. A lot of people, when they come to the benefit risk field, that’s what they see in the tutorial educational materials that we use for teaching people benefit risk. We take them to the effects table, but then coming up with an effects table, as you say, is not easy. And it can also [00:07:40] be useful, for example, for the manufacturers of the drugs in the development phase to use such a format to make sure that everybody internally in the development team is aligned on what actually are the key benefits?
[00:07:53] What are the key risks? Where are the evidence gaps? How much do we expect there to be uncertainty and so forth? So the [00:08:00] effects table is, should be kind of, Kind of, kind of like a living format. So it’s not just a snapshot, but it should be a, a format that is accompanying drug development all the way from the early, early target product profile setting to the post authorization setting.
[00:08:17] Alexander: Yep. Yeah. Completely agree. [00:08:20] Yeah. And it also can, for example, include. unfavorable effects, not talking about side effects or risks, but unfavorable effects where there’s actually no difference to placebo because it might be, you know, different for other products. Yeah. So maybe the current [00:08:40] products on the market, actually, have the side effect and therefore kind of by including that, that’s also important.
[00:08:49] So just, just a couple of things. To speak about that. Yeah. So it has come from a very, very unstructured way and [00:09:00] IMI protects. And other things have led quite a lot in that regard. By the way, if you go to the PSI, you will benefit from the risk. Special interest group. You’ll also find lots of resources around this and we can put some, some links to this documents also in the, in the podcast.[00:09:20]
[00:09:20] Where are we currently at benefit risk when it comes to support, especially discussions with, with let’s say the email.
[00:09:29] Tommi: Well, I think we’re at the, we are. Currently in a situation where we have the methodology lined out to [00:09:40] the extent that it can be used on, let’s say, supporting pretty much all regulatory decisions when there is any uncertainty about the benefit risk.
[00:09:50] And a key. But getting there was not just having the structured benefit risk assessment in there, because that was pretty much done when the FX table was [00:10:00] introduced as being mandatory at the EMA side in 2015 onwards, if I remember correctly. But then once you have this. The effects table, in order to say something about the benefit risk balance, you still have to weigh the benefits against the risks, right?
[00:10:15] So that was still the key missing point after the introduction of the effects table. [00:10:20] So you have the data in there, but you still need some value judgments to be incorporated to say, let’s say this change in risk is, is less bad than this improvement. These improvements in the benefits. So for that, we needed some sort of waiting information and I think that at some point it [00:10:40] started being like consensus among the regulators and academics and people working in the field that patient preferences are a good way around this problem.
[00:10:49] Because patients are the ultimate stakeholders and, and, and also their data can be collected by the sponsors in a reasonably objective way [00:11:00] using the methodological advancements on that side of the, that side of the health economics field. So. Once you input patient preferences there, we start getting this what we call quantitative benefit risk assessment, a comprehensive benefit risk assessment using some weighting data.
[00:11:17] And that is now, let’s say [00:11:20] acceptable to be used in regulatory submissions since the EMA qualification of the patient preference methodologies was it earlier in 2023.
[00:11:31] Alexander: That is interesting. So Years ago, I still struggled to explain to people within the [00:11:40] organization, even those that kind of worked in the benefits risk area, that we should collect these patient preferences.
[00:11:50] Early on because there was all this kind of, oh, we don’t do we really need it or yeah. Can’t we, you know, get away [00:12:00] with it because it also costs money and resources and time. And I was always thinking like, well, I rather have these and then don’t need to use them because everything is pretty obvious, then not having them, but then actually needing [00:12:20] them because there’s something in there that yeah, it’s much more kind of difficult and less obvious.
[00:12:25] So what is your recommendation of when to run such patient preference studies?
[00:12:34] Tommi: I would say that start early but develop it also in [00:12:40] an iterative manner, because I mean, I would like all the sponsors to run the biggest possible patient preference studies at very early development, but obviously the resources are concerned there.
[00:12:52] So it’s a little bit like the rest of the clinical development. I mean, you, you have a, have a, have a smaller trial earlier on proof [00:13:00] of concept. And then you later on have a, have bigger trials to establish actually that the effect size and and, and truly the safety or to a certain extent is the safety of the product.
[00:13:11] So, I mean, I would recommend doing patient preference studies in a similar way. So doing smaller studies let’s say [00:13:20] more qualitative studies earlier on in the development to make sure that the end points you are selecting for the clinical trials are actually those that matter to the patients, first of all. And then when you start getting some safety data, start going to larger. quantitative patient preference studies to make sure that the benefit risk profile is expected [00:13:40] to be positive. And in many cases, it makes sense to try to run in parallel to the phase three to include in then in a submission dossier or to have at least the data ready for the in hand for the submission dossier.
[00:13:52] Let’s say patient preference, the key, key benefits and expected risks in order to be able to do the quantitative benefit risk assessment [00:14:00] if needed. So. If the, let’s say the benefit risk balance is obvious, you may not want to submit that because it’s just extra effort, extra data can be questioned and so on.
[00:14:14] But if it’s not obvious, you’re going to be too late if you start thinking about that when phase 3 comes. Reach [00:14:20] out. Yeah. So we’re talking about, you know, for a regulatory grade study, we are talking about one and a half years, two years timelines for the patient preference system.
[00:14:29] Alexander: Yep. Yep.
[00:14:31] That’s very good to kind of have said already in your clinical development play all laid out. And because [00:14:40] that will also be very, very important later on beyond the regulatory work, I think. One question I have is in terms of subgroups. Where are we with respect to doing benefit risk analysis across subgroups?
[00:14:58] Tommi: Well, there’s, there’s [00:15:00] two, two parts of the subgroups in there. There is the, there is the, let’s say the, what we know about the, the differential effect in terms of the, of the endpoints in some, some parts of the population. So the relevant subgroups. With respect to the, essentially the trial [00:15:20] data. And then there is the second part, which is the subgroups.
[00:15:23] Where, where we, we expect the patient preferences to differ and this may be the same or they might be different as well. Right? So you could have pretty much the same clinical effect of the drug in. in the whole population, but there might be differences in [00:15:40] the risk tolerance of the patients, for example, depending on their, of their symptom burden and so on. So in some way, the benefit risk assessment with patient preferences gets more complicated because we add this extra dimension of preference heterogeneity. Some, some patients may have just different [00:16:00] preferences in practice often. It’s not so wide there, the preference heterogeneity. There is preference heterogeneity, you have to assess it and so on.
[00:16:10] But in practice, if you think about the phase three filing population, for example, that tends to be reasonably tight. So I don’t tend to be so worried [00:16:20] about having huge preference heterogeneity. You have to plan for it. But usually it’s, it’s pretty okay. So it’s not all around the place, let’s say that way.
[00:16:28] Alexander: Yeah. Also you need to understand. Can you really identify these, these patients that have different, yeah that are heterogeneous in terms of set [00:16:40] preferences. And just to explain that to statisticians that are not used to patient preferences, that basically means that let’s say you have a benefit in terms of a symptom reduction, and then you have a safety risk.
[00:16:56] And for [00:17:00] some patients, you know, if you add things up, it’s clearly in favor of the benefit. But you could have some patients that weigh a risk much higher than the benefit. And then in these patients, the benefit [00:17:20] risk equation, so to say, becomes negative. Now, the problem that I have seen is that it’s very often not explained through any biological data.
[00:17:33] Age or sex or pre-treatment or gene or whatsoever. [00:17:40] But by very, very different things. It’s about their social interactions. It’s about their education. It’s about all these kinds of different things rather than you know, Anything that we have usually in our case report forms. So just from kind of predicting these kinds of things, [00:18:00] it becomes much more difficult.
[00:18:03] Tommi: Yeah. Yeah, it does. And, there is exactly this, let’s say social demographic factors that come into play in there, but I would say that. In many cases, it is actually the patient’s lived experience of the, of [00:18:20] the disease. That is the key factor impacting their preferences, especially benefit risk preferences.
[00:18:25] You know, how much risk are they willing to take for given benefits? And, and, and. When you follow that line of thought you can, in many cases, at least, you can control for that preference heterogeneity quite effectively by, [00:18:40] by also then fielding PROs within the patient preference study. So if you have disease specific PROs, for example, those can be very useful in, in using the results from them you know, some domain scores for controlling for the preference heterogeneity.
[00:18:54] And that tends to be. Interestingly, better correlated with preferences [00:19:00] than many other factors, such as disease stage that the clinicians might consider to be more relevant, but no, it is how patients actually feel about themselves currently about their symptoms, for example, that impacts on how much risk they’re willing to take, but not always.
[00:19:13] I mean, it is an evolving field also based on what we think is [00:19:20] the best way to characterize the preference heterogeneity for benefit risk. And I think my current thinking is that PROs are the best, but maybe five years on, I won’t think that anymore.
[00:19:30] Alexander: Okay. That’s a nice lead way into the last part of this episode.
[00:19:36] What do you think the future of [00:19:40] benefit risk will be and what trends do you see that could predict this kind of future. Now, of course, we all don’t have a crystal ball, but given that you are attending lots of conferences, you see lots of research, you’ll actually do your own research. Where do you see [00:20:00] trends that are already happening now that could materialize in benefit risk assessments at the EMA or the FDA?
[00:20:09] Tommi: I think it will, first of all, become more commonplace. So it will become more visible and have a more visible impact on the decisions. So we know, for [00:20:20] example, from the past that there have been some decisions at the EMA or FDA side where patient preference and structure benefit risk assessment has played a role, but it has not really been visible at all.
[00:20:30] In the public evaluation reports, zero words to that extent or very, very little mention, which is, which is frustrating [00:20:40] looking at from the outside and, you know, working with the sponsor and we submit this data and then we get zero mention of it sometimes, even though it might, it might have an impact, but they still stay quiet about it.
[00:20:51] We’d have some, we’d have some positives. Positive experience as well latest being the Ritley Citinib submission of Pfizer that I was supporting as [00:21:00] well, where we, where EMA explicitly mentioned the patient preference data and the quantitative benefit risk assessment in the, in the E PAR multiple times and justified that positive approval decision based on the, on those two analysis.
[00:21:15] So, I would assume that we’re going to see. Much more of [00:21:20] a visible acknowledgement of patient preferences and quantitative benefit risk assessment in the regulatory side. So it will become more commonplace to base the decisions or acknowledge at least the preference data there. I also think the methodological work is not [00:21:40] yet completely done.
[00:21:41] So we have a solid set of methodologies. That we can use for supporting benefit risk assessment and collecting patient preference data. But that is kind of narrowing down on the basic benefit risk assessment. But there’s a lot of other decisions on where we could be using these techniques. I [00:22:00] mentioned end point selection earlier on.
[00:22:02] I don’t think there is really an established way of doing this. Thinking about how to understand, for example importance of moves moving around in the PRO scales. The animal can make an important difference. I don’t think the way that’s done by the PRO folks. I don’t think that is very, very, [00:22:20] robust, in my opinion, I think we can do a lot more there with patient preferences, better with patient preferences. And probably there’s other use cases that I haven’t even thought of that will be coming more, more important in the coming years now. So there’s a lot has been done, but a lot of work still to do, let’s say this way.
[00:22:38] And I hope that this podcast [00:22:40] brings more statisticians to the field.
[00:22:43] Alexander: Yeah, so, there’s a lot going on in that space. And if you’re interested and definitely reach out, connect to others we definitely need more statisticians in that space. I want to follow up on this point that you mentioned in terms of [00:23:00] patient preferences and clinically meaningful or clinically ignorant.
[00:23:09] Differences. How do you see that link? That’s a really interesting one because I’ve never heard about that or thought about that. [00:23:20]
[00:23:20] Tommi: Yeah. So, I mean, if, if you think about the word importance, I mean, that just directly reflects that you should be having preference information in the analysis and the more robust preference information, the better, and it’s, it’s [00:23:40] basically like Following the same logic as, as what we do in, in the full, let’s say, benefit risk assessment using patient preferences.
[00:23:49] So how important are certain changes across the endpoints, right? So, I mean, you should be having the [00:24:00] patient preference data, giving you an answer to that question.
[00:24:04] Alexander: Yeah, so instead of having what is very often used just kind of clinical global impression, yeah, as a reference, use, use patient data, patient reported patient preference data as an anchor.
[00:24:19] Tommi: [00:24:20] Yeah. Yeah. So if in terms of anchoring, you would anchor it to the other end points. So for example, I’ve done patient preference study where you had the primary endpoint is one of the attributes and then some PRO dimensions as other attributes. So then you’re going to be, you’re able to tell how much patients would be willing to tolerate [00:24:40] a decrease in essentially efficacy in terms of the primary endpoint for improvement on these, on these symptoms.
[00:24:47] Alexander: Yep. Yep. That’s very, very intelligent. Awesome. Thanks so much. We talked about the past, present, and future. and future of benefit risk. [00:25:00] And we only touched on a couple of big things. One is that it went from being completely unstructured, kind of consensus driven and not being very transparent because of that, to something that is Much more structured.
[00:25:18] And yes, there are differences [00:25:20] between different regulators around it. We got to the effects table that summarizes the most important favorable and unfavorable effects. And that also very often comes with a figure. And I know some companies actually started with a figure and then added the table. And.
[00:25:38] That then you [00:25:40] still need to have some kind of measure of weighing effects versus each other, especially if it’s not, you know, clear cut sets, you have a super nice, very effective drugs that has no side effects. Which, of course, we always have, or [00:26:00] in reality, we never have, of course. And then kind of looking into subgroups, looking into clinically important differences with respect to benefit risk.
[00:26:10] And this will be, become more and more important in the future, just looking into the trajectory over time that will become more structured, need to become [00:26:20] more transparent just because I think the regulators want to be able to defend their decisions. very, very clearly and not be, you know, accused to prove something that is, you know, not good or that is, you know, there’s no good solid [00:26:40] data on that, these kind of things.
[00:26:42] So just from that kind of point of view, I think it will make a big difference. And generally, my perception is that patient centricity is generally On the increase and well, [00:27:00] including patient and patient preferences in benefit risk decisions is surely kind of the ultimate patient centric approach that you can have.
[00:27:10] And of course, also making sure that the endpoints that you collect are actually patient relevant. So, lots of great things happening [00:27:20] around patient and patient centricity. What is the one thing that you want? So let’s not to take away from this discussion.
[00:27:29] Tommi: I, I hope getting more interested about doing structured and quantitative benefit risk assessment and, and maybe also listening a little bit more to the patients [00:27:40] as you mentioned the patient centricity.
[00:27:42] I think that would be, My, my hope doing, thinking about the benefit risk profile early and trying to get also external patient centric validation of that profile in terms of getting either quantitative or qualitative patient preference data [00:28:00] into the mix. We be open minded also. I mean, every single time I do a patient preference study.
[00:28:05] I, I, I’m surprised about the results to, to some extent. There’s always pre preexisting ideas that we have about what patients would prefer. And most of the time they are somewhat incorrect, often not completely incorrect, but [00:28:20] there’s often some surprises around the corner when we actually go and ask the patients.
[00:28:26] Alexander: When people need help in terms of these things, how can you help them with, with your company?
[00:28:35] Tommi: Well, we, we, we do patient centered research at Kielo Research for, for [00:28:40] all, all of these phases. Pretty much. We, we do a qualitative interviews. We do a quantity patient preference studies, and we also help some, some sponsors in profiling their products in terms of the, of the benefit risk. Many of the companies. Actually very, very nicely [00:29:00] have set up benefit risk experts and teams in the past, I would say five to 10 years or so, some of them have them even earlier on. So, so that expertise tends to be in-house already. So it’s, it’s less that we need to help with the actual benefit risk profiling.
[00:29:18] It tends to be more that, that we [00:29:20] come and help with bringing the patient voice into the mix.
[00:29:23] Alexander: Yep. Yep. Awesome. So check out the homepage of the effective statistician where you will also get a link to Tommi’s LinkedIn profile and his company and to learn more about what he is doing in terms of research [00:29:40] and how we might help you and your team.
[00:29:42] Thanks so much, Tommi, for being on the show.
[00:29:45] Tommi: Thank you, Alexander, for inviting me.
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