Introduction to Personalized Medicine Part 1: Exploring the Future of Healthcare

In this episode, Thomas Debray and I explore the fascinating realm of personalized medicine, a field poised to revolutionize the way we approach healthcare. Thomas Debray, a seasoned expert in precision medicine, shares his insights to shed light on this innovative approach.

We’re exploring these core questions

  • What is Personalized Medicine?
  • Why Should Medicine Be Personalized?
  • How Does Personalization of Medicine Improve Intervention Results?

Key Highlights

  • Addressing Individualized Benefit: The personalized medicine approach recognizes that each patient is unique and strives to optimize treatment strategies based on individual characteristics and needs.
  • Enhancing Efficacy: By leveraging personalized data, healthcare providers can fine-tune interventions to better target diseases, resulting in improved treatment outcomes and patient well-being.
  • Balancing Pros and Cons: While personalized medicine holds tremendous promise, it’s essential to weigh the potential benefits against ethical, social, and economic considerations to ensure responsible implementation.

As we reach into the realm of personalized medicine, it’s evident that we stand at the start of a transformative era in healthcare. With its emphasis on individualized care and tailored interventions, personalized medicine offers a beacon of hope for patients and practitioners alike.

Stay tuned for the next episode of our exploration, where we’ll dig in deeper into the practical applications and prospects of this groundbreaking approach.

Thank you for tuning in to the Effective Statistician Podcast. Until next time, stay curious and keep exploring the frontiers of statistical science in healthcare.

Thomas Debray 

Founder and Owner of Smart Data Analysis

Thomas offers biostatistical consulting services in the design and conduct of post-marketing studies. He also leads various innovation projects focusing on meta-analysis and risk prediction.

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Transcript

Introduction To Personalized Medicine Part 1 

[00:00:00] Alexander: Welcome to another episode of the effective statistician. I’m super happy to have Thomas DeBray again on this podcast [00:00:10] and this will be the first in a series of episodes where we talk about personalized [00:00:20] medicine. Hi Thomas, great to have you back on the show. 

[00:00:24] Thomas: Hi. Well, thanks for having me. It’s a great opportunity to yeah, to have this conversation and talk a bit [00:00:30] more about personalized medicine. 

[00:00:31] Alexander: Yeah. Personalized medicine is something that is very close to your heart and you are doing a [00:00:40] lot of things on that already. Since, since we last talked on the shows, a couple of things have evolved and maybe you [00:00:50] can give a little bit of an overview of where you are at the moment especially with your company.

[00:00:57] Thomas: Yeah, that’s right. So I think last time we spoke I [00:01:00] was still a scientist working academia. And I mean, yeah, I think at some point I realized that, okay, I, I’m not sure academia is the place I fit in very [00:01:10] well. So I, I started looking around what else is what other type of environments I could, I could thrive in where I could, you know, Contribute to innovation while at the [00:01:20] same time also be more, you know, on the, on the applied side.

[00:01:25] Thomas: And so I realized that I really enjoy being a consultant, you know, solving [00:01:30] problems, but also like adopting like innovative methods, but also tailoring them to, you know, to local needs and local circumstances. kind of taking them out of the, you know, the [00:01:40] theoretical paradigm and really, you know, putting them in a context where they can actually, you know, be used to, to derive answers.

[00:01:46] Thomas: And that’s, you know, it’s quite tricky in the sense that it, [00:01:50] it requires to understand like complex methods and, and complex settings. And, and at the same time You know, packaging, packaging them in a way that, you know, they can actually [00:02:00] be used to, to address, you know, more real problems, basically. And so, yeah, so I, I founded a company recently I think by now it’s probably 1 or 2 years ago where [00:02:10] I, I both provide individual consulting.

[00:02:12] Thomas: So I both, you know, work as an individual consultant on various projects, but I also embark on On projects that are [00:02:20] outsourced to my company where I have them, you know, different specialists that are helping me working on, on fire solutions that range from, you know, specialized art packages for precision medicine [00:02:30] to other type of programs as well as projects where we try to, you know, address more complicated questions by leveraging, you know, recently developed methodologies.[00:02:40] 

[00:02:40] Alexander: Good. Yeah. So if you’re working in this area and you need help in terms of observational research, as you need help in [00:02:50] terms of network meta analysis, these kind, all these kind of different areas, yeah. Feel free to connect with with, with Thomas. Yeah. One way is [00:03:00] probably LinkedIn. What are, what are other ways people should, contacted you? 

[00:03:06] Thomas: Good point. Yeah, so I’m, I mean, I think my most active platform is probably [00:03:10] LinkedIn, but I mean, I also do have a website. The URL is from data to wisdom. com. At the moment it’s in it’s being updated, but it should be, I think by the time this podcast is [00:03:20] released, it should probably be online.

[00:03:21] Thomas: Other than that, yeah, I intend to, I mean, I’ve not attended conferences for the last few years due to COVID and other reasons. But I intend to, you know, to [00:03:30] get back to it at some point in the, in the near future, just to find a bit, which time of the year is a good time to attend a conference, but so it will be a bit conditional on that to [00:03:40] figure out which, which ones I will be attending and otherwise by, by email I suppose.

[00:03:44] Alexander: Yeah. Will you be do you already plan to attend the PSI conference in Amsterdam [00:03:50] in, in June? 

[00:03:51] Thomas: Yeah, that’s in June. Yeah. So I have to check if I’m in Netherlands in June, but if that’s the case, yes, I would be happy to attend that one. 

[00:03:58] Alexander: Yeah. Yeah. That would be [00:04:00] definitely a great one. So yeah, reach, reach out to Thomas either via his website, which we’ll link to in the show notes or to his [00:04:10] LinkedIn profile, which we’ll also link in to the show notes.

[00:04:14] Alexander: Okay, let’s dive into personalized medicine. It’s it’s, it’s [00:04:20] probably a very, very important concept that we go to since many years and in the recent [00:04:30] years, it has I think got a pretty big boost but before we talk about it, What is actually personalized medicine? [00:04:40] 

[00:04:40] Thomas: Yeah, so that’s a good question.

[00:04:42] Thomas: I think a lot of the, I mean, the name gives away some indication, right? So it’s medicine and personalized, right? [00:04:50] It’s medicine tailored to individuals. I think, I think that’s a general kind of gist of it. And, and the main ideas, the main background that is a lot of medical [00:05:00] research that is happening, that is being conducted.

[00:05:02] Thomas: It’s focusing on populations, right? And there is a good reason why that is the case, because it’s a lot more effective to focus on populations. [00:05:10] And so clinical trials, of course, you know, they, they study the safety and efficacy of interventions on, you know, for a specific type of [00:05:20] populations with the expectation that whatever findings you obtain from this type of studies, they would generalize, you know, to, to somewhat different populations that you encounter in [00:05:30] clinical practice.

[00:05:31] Thomas: And this has been the status quo for, for a long time. And, you know, it works. For various good reasons, it’s it’s a status quo that is, you know, that has been [00:05:40] maintained for a long time. The only point is that I think we come to the realization and probably we were already at, you know, for a long time, but that that is effect estimates that we obtained from [00:05:50] clinical trials, you know, this average treatment effects even though that on average, you know, they, they give expectations that are, you know, realistic and that are, you know, valid and, and, you know, it can be [00:06:00] used for decision making.

[00:06:01] Thomas: For individual persons and for individual patients, these estimates may not always be, you know, very applicable and that’s because of, you know, treatment [00:06:10] response can vary quite a bit between individuals and some of this variation can be, you know, just to, to, you know, natural variation, right, or just to chance, I mean, there might not necessarily be a [00:06:20] reason behind this type of variation but there’s also a lot of variation that might be related to, for example, treatment effect modifiers, for example, treatments, treatments, treatments.

[00:06:27] Thomas: might be more effective in, let’s say, younger [00:06:30] patients or in older patients or in more diseased patients or in less diseased patients. You know, over the last few years, we’ve seen some medications that are, you know, that are given on indication, right, based [00:06:40] on, let’s say, like genetic profiles or other type of markers.

[00:06:45] Thomas: So this is also an example where, you know, where you provide medication or [00:06:50] treatments to, you know, not to every individual who is diseased, but to only to specific. type of individuals who are more likely to benefit from the treatment. And so this is also where, you know, [00:07:00] personalized medicine comes in, right?

[00:07:01] Thomas: Where you try to understand, okay, who will benefit from treatment? Can I identify who will benefit from treatment? Can I identify how much they will benefit from treatment? And can I [00:07:10] kind of tailor, you know, my strategy for treating individuals in such a way that patients receive the treatment that is most likely, you know, to be of benefit of them.

[00:07:19] Thomas: And so when I [00:07:20] talk about benefit, I’m not only talking about you know, efficacy, but it could also relate to, you know, like side effects, safety and support. And so, of course, it’s not a [00:07:30] black and white discrepancy, right? Because, I mean we have often, of course, investigated subgroups, right, where we are saying, like, look, we understand the treatments.

[00:07:38] Thomas: may not be equally [00:07:40] effective or equally safe in all individuals. So there is, of course, already for a long time being interested also in investigating subgroup effects, right? For example, comparing, you know, [00:07:50] treatment effects in males versus females or some other you know, type of biologically driven subgroups and personalized medicine is basically, you know, stretching further in that direction, [00:08:00] right?

[00:08:00] Thomas: Where you’re trying to get even, you know, more towards more smaller subgroups, you know, subgroups that are defined by multiple covariates, multiple characteristics, rather than [00:08:10] just, you know, one binary, let’s say threshold deciding whether, you know, you belong in subgroup A or in subgroup B. So it’s really About trying to get, I mean, it’s like an [00:08:20] event horizon, right?

[00:08:20] Thomas: You will never get to the individual treatment effect for one specific person that is named, you know, Thomas Bray or, you know, or Alexander Skagg, but you can try to get in [00:08:30] that direction, right? You can try to identify what is the treatment effect in individuals who are, you know, relatively young. have a limited number of comorbidities have this [00:08:40] specific variation of the disease and support.

[00:08:42] Thomas: So that’s the ambition, right, of personalized medicine, to really try to understand how specific type of individuals could [00:08:50] benefit from, from specific treatments. Yeah, 

[00:08:54] Alexander: yeah, it is when I listen to this, for me, it’s a typical trade off [00:09:00] between bias and precision, yeah, so when, when we think about average treatment effects for all patients [00:09:10] with, let’s say, diabetes.

[00:09:11] Alexander: Yeah, then of course, we can get the biggest precision because we have the biggest sample size very [00:09:20] easy to find many patients for that. And that we get a very, very precise. [00:09:30] However, if we go into smaller subgroups of maybe only patients that are younger than 30, that have a BMI [00:09:40] of over 35, and that have been treated for X years, and have these kind of comorbidities, and the more we go into these kind [00:09:50] of different things, the less patients we have.

[00:09:54] Alexander: And so the in, therefore the, the precision actually goes down[00:10:00] however, the bias. Also goes down, yeah, for the bias that we have for a specific patient in this area, you [00:10:10] know, so, so imagine that we want to predict the real treatment effect for a specific patient, then there will So. So. [00:10:20] You know, the biggest bias on average will always be in the, in the overall population, you know, because that is because then most of the patients that [00:10:30] you look to predict will be very, very different to this individual patient.

[00:10:35] Alexander: And the more homogeneous you get within your subgroup better you get in terms [00:10:40] of the bias, but for the cost of of precision. And so that is that is something to basically have in the back of your mind. We want to [00:10:50] get as precise as possible while also decreasing the bias. Now, when we think about diabetes, for [00:11:00] example, Now, there’s a, there’s a lot of precision medicine already going on in terms of event driven adjustments for, [00:11:10] for treatment.

[00:11:10] Alexander: If we think about let’s say you look into certain laboratory values and you treat to a specific target there, [00:11:20] or you adjust the dose if you see a specific side effect or you add. Treatments if you see something all these kind of different things. So [00:11:30] this will always be this event driven adjustments.

[00:11:36] Alexander: What do you think about these event driven kind of. [00:11:40] strategies. How can our statistics knowledge help in that regard? 

[00:11:47] Thomas: Well, yeah, I guess the challenge with event driven decisions is that [00:11:50] you have to have the capability, right, to observe these events and to recognize them as an event of, of interest to make.

[00:11:56] Thomas: a decision about. So I’m not an expert in diabetes, but I, I can imagine [00:12:00] that it might be quite challenging to, you know, to keep, to follow up all these patients so intensively, right? To, to monitor the occurrence of these events and then to decide how [00:12:10] treatment pathways or, you know, doses and so forth should be modified accordingly and in a manner that is appropriate with, you know, with the event or sequence of events that has happened.

[00:12:19] Thomas: [00:12:20] So yeah, so how statistical analysis could help in this is first of all, to identify right where events are happening based on data that is available, that is routinely [00:12:30] collected at the patient, but it could also be used to predict. The future occurrence of these type of events and as well to predict how different treatment decisions, for example, you know, [00:12:40] the, the dose or the, the choice of the treatments or the the frequency of, of how often the, the, the treatment should be taken.

[00:12:46] Thomas: How these type of strategies, as well as changes in those [00:12:50] strategies might affect. The, the occurrence of future events that are, I mean, or not be of interest as well as overall health. So how that would correlate to overall health and, and, you know, based on [00:13:00] by, by discrepancy, so not by discrepancy, but by, by contrasting, right.

[00:13:03] Thomas: It’s different strategies on the predicted. Course of, of disease in that patients, you [00:13:10] can decide whether it would be, you know, beneficial to, you know, to, to keep the status quo on a certain strategy, or perhaps to intensify or de [00:13:20] intensify certain medications to, you know, to avoid these type of events.

[00:13:25] Alexander: Yep. I think this is especially where if you look [00:13:30] into real world evidence, yeah, you can learn a lot from your post marketing data when you collect what, how people [00:13:40] actually manage all these kinds of different things. Yeah. When two people increase the dose, decrease the dose add another

[00:13:48] Alexander: treatment to it, to [00:13:50] manage side effects, whatsoever, you know, all these different strategies you can observe in your real world data, given that you have [00:14:00] good real world data. And then, you know, extrapolate and learn from these kind of things, what are good personalized strategies. [00:14:10] Now that is one area to do it.

[00:14:13] Alexander: The other is also model based strategies. What is [00:14:20] that? What are model based strategies for personalized medicine? 

[00:14:24] Thomas: Yeah, I would, I would say so that I would say that you are using statistical models, right, to decide [00:14:30] which treatments are most are likely to be most beneficial in individual patients.

[00:14:35] Thomas: And I mean, a simple model, I mean, we’re already using model based strategies in the sense that, you know, an average treatment [00:14:40] effect is an estimate that comes from a model with, you could say, just one parameter, right, like an overall treatment effect. And that’s the parameter that you’re using and you’re applying it to all the patients, [00:14:50] right, to make your decisions.

[00:14:51] Thomas: So if the if your overall treatment effect is, is let’s say it indicates like relative risk reduction in favor of, you know, of your treatment, then that’s, you know, that’s what [00:15:00] you assume is applicable to everyone. And so that’s the effect that you’re applying to everyone. Now, you can think of more complex models, right?

[00:15:07] Thomas: Where you say like, look, my, my average treatment effects. [00:15:10] It’s maybe not like one effect that applies to all individuals. So maybe I have a more the, the the model that defines, you know, the, the, the, the risk reduction, for example, [00:15:20] maybe it’s not merely determined by, you know, the effect of treatment, but maybe there’s something else going on.

[00:15:26] Thomas: So maybe there is also like an interaction, for example, between the effect of the [00:15:30] treatment and some, yeah, some, some patient level characteristics. like age or, you know, disease severity or stage of, you know, of cancer and so forth.[00:15:40] And it also be prognostic factors. And of course, I mean, prognostic factors, they do not necessarily lead to any differentiation in, in let’s say people who are treated with an active treatment[00:15:50] or if like an established treatment or if let’s say like a placebo treatment but nevertheless, when you look on an absolute scale on treatment benefits, you know, and because we often [00:16:00] talk about relative benefits, right?

[00:16:01] Thomas: Like relative risk reduction and relative, you know, high saturation and support. So most of the time when we talk about treatment effects, we’re talking about relative effects. But when, when [00:16:10] we are, you know, for individual patients, you know, I, I, me personally, I don’t really care about relative effect.

[00:16:15] Thomas: Like if I’m going to take this treatment, is my risk going to reduce from, you know, by, by [00:16:20] 80 percent or by 60%. I mean, that’s great to know, but I, this information is absolutely useless for me. If I don’t know what is my, my risk, if I don’t take any treatment, right. If I 

[00:16:29] Alexander: is what’s [00:16:30] your baseline risk. Yeah, yeah, exactly.

[00:16:34] Thomas: And so that’s where, you know, the interplay. Between, you know, prognostic factors, treatment effect [00:16:40] modifiers, the relative treatment effects, you know, where all these, all these basically all these characteristics that somehow affect my outcome, right? Whether it’s related to treatment or not, but where all [00:16:50] these All these factors become important to take into account, at least if I’m interested in my, you know, how, what will my absolute benefit be from, from [00:17:00] receiving the treatment and how is my, my risk of developing, you know, favorable outcomes changing.

[00:17:06] Thomas: So is it if I, if I don’t take any treatment, how is it if I take a [00:17:10] standard you know the standard treatment that is recommended for this condition. And how will it change if I take like a newer treatment, right? That promises [00:17:20] certain improvements in, in, for example, in safety profile or perhaps in, in efficacy as well.

[00:17:25] Thomas: Yeah. 

[00:17:26] Alexander: So let’s, let me take [00:17:30] my kind of characteristics, roughly characteristics as an example. Yeah. So 50 years old. Yeah, I’m male. I’m white. [00:17:40] I’m living in Germany. I I’m about two meters tall. I’m a little bit overweight [00:17:50] and now I look into the study report and I want to learn more about What is the treatment benefit for me?

[00:17:58] Alexander: What I would usually [00:18:00] see is, okay, there’s first kind of an overall report that says, okay, across all the patients for the primary endpoints, the [00:18:10] treatment difference is X, and so you have a response rate in active, you have 80 percent response, and in placebo, you have 40 percent response, and then you would [00:18:20] have subgroup tables.

[00:18:21] Alexander: Yeah, for the for older patients that are, let’s say, older than 40, the difference is that. And for patients [00:18:30] that are white, the difference is this. And for males, the difference is this, compared to the females, and so on. And so I would get all these kind of [00:18:40] different subgroup tables. Now the problem is, Well, okay, I can see heavier patients [00:18:50] respond maybe less, and then I see, well, males respond better.

[00:18:55] Alexander: And then I see older patients respond [00:19:00] also better. And then I see race may make a difference, but not really, really sure. So how can I derive [00:19:10] actually my personal treatment effects from, from such, you know, 

[00:19:14] Thomas: analysis? Yeah, so that’s, that’s really challenging, I think, because like you say, right, if you [00:19:20] look at some group effects, you will often find that they are, you know, they might give you conflicting, right, recommendations about whether the treatment is going to be beneficial or not [00:19:30] beneficial for you as compared to, you know, a standard placebo treatment.

[00:19:33] Thomas: And on top of that, I mean, some group effects, I mean, it’s well known that they’re often prone to, you know, either selective reporting, but [00:19:40] also more incidental findings, right? Like that they are, you know, you tend to find effects there and that they might not be reproducible basically. So they have to be [00:19:50] taken with, with quite a bit of caution.

[00:19:52] Thomas: So that’s, I mean, that’s a big challenge, right? So, so, I mean, first of all, If you really want to personalize treatment effects, you would have to adopt an [00:20:00] approach, right, that has that ambition, right, in mind. And subgroups, they don’t, it’s not the goal of a subgroup analysis is to personalize treatment effects, it’s not the goal of it.

[00:20:07] Thomas: The goal of a subgroup analysis is to [00:20:10] understand if the treatment effects is different, right, in different subgroups in, in, you know, let’s say males versus females. So that’s, I mean, the estimates you get out of the subgroup analysis is telling you an answer to that question, [00:20:20] right? Is, do you have a different treatment effect in males as compared to in females?

[00:20:24] Thomas: Or even not, perhaps not even in the comparison, right? In some subgroup analysis, you just estimate separately for males and females, and you [00:20:30] may not even answer whether the effect is different. It will just give you two effects with no indication of whether the two are statistically different. So, I mean, [00:20:40] some group analysis can be useful, but they’re not.

[00:20:44] Thomas: As far as I’m concerned, they’re not really intended to give an answer about how individual patients [00:20:50] benefit from treatment. And so, yeah, to just come back on the on the question about model based approaches. So the idea of of model based approaches [00:21:00] for treatment effects of predictive predictive models for treatment effect, basically, so that the concept is similar to what we have with, you know, prognostic moles, right?

[00:21:08] Thomas: With basically these are [00:21:10] multivariable models, right? So most where you have multiple parameters. So one of the parameters is treatment and do I receive. placebo treatment. Of course, in practice, you don’t receive placebo treatments, but in [00:21:20] clinical trials you do. Do you get like an active treatment? Do you get one of the summer care or another treatment?

[00:21:25] Thomas: So that would be one of the parameters. And then there’s several other parameters, right? Are you a [00:21:30] male? Do you have like, what’s your baseline disease severity? You know, depending on the therapeutic area, there may be several other, you know, variables that either affect your [00:21:40] prognosis. So which is, you know, the risk of outcome or the severity of your disease, irregardless of whether you’re being treated or not.

[00:21:48] Thomas: So there’s always [00:21:50] factors that, you know, influence your outcome, right? Like if you have a cardiovascular disease, I mean, regardless whether you’re taking treatment or not, I mean, there’s a certain course of, you know, of events that [00:22:00] is likely to happen. Also if you’re overweight and so forth, you know, like, so there’s, there’s always like what we call prognostic factors or factors that affect the outcome.

[00:22:09] Thomas: [00:22:10] Anyhow, and then some prognostic factors, they might, you know, increase the efficacy of treatment or they might decrease the efficacy of treatment. So they might interact basically right with the treatment itself. [00:22:20] So these are, I mean, these are typically the parameters that we’re interested in, if we’re trying to personalize treatment effect because these so called treatment effect modifiers.

[00:22:28] Thomas: Are the factors that are going to [00:22:30] bring the greatest shift writing in whether to what extent I will benefit from a specific treatment or not. And so a big search or a big effort often in [00:22:40] personalized medicine is trying to identify. Are there any treatment effect modifiers? Can we estimate them?

[00:22:46] Thomas: Is their magnitude meaningful? And if that is the case, you know, Kevin, you [00:22:50] know, can we integrate that? In these models, when we’re estimating the, the average treatment effects I mean, when we estimate average treatment effects, but also when we are [00:23:00] tailoring these average treatment effects again back to individual patients where, you know, that may differ in disease severity age and so forth.

[00:23:07] Alexander: Yep, that is that’s a [00:23:10] very, very good summary and I think the example from the typical study result, yeah, and what we see on technicaltrials. gov and what [00:23:20] regularly gets published in the literature, Is lacking all of that. Yeah, we just do the subgroups. And so my [00:23:30] perception is from a bigger picture.

[00:23:32] Alexander: We are quite far away from this personalized medicine. It is, you [00:23:40] know, when I look into any report, is this very, very hard for me to understand what is my baseline risk in the study? What is the [00:23:50] potential benefit for, for me? 

[00:23:52] Thomas: Yeah, so the questions you will not even be able to answer, right? Like baseline risk is very difficult to, ascertained from clinical trials, because [00:24:00] by, I mean, the selection that you have in clinical trials is, is, you know, the patient that you find in clinical trials usually will not be very representative of, of the patients you encounter in clinical [00:24:10] practice.

[00:24:10] Thomas: So especially if you have questions about baseline risk, these type of questions are very difficult to, you know, to, I mean, you can get estimates, of course, clinical trials on baseline risk. [00:24:20] The key question is whether these estimates are going to be anyhow representative, you know, for, for you as a patient or for, for clinical practice.

[00:24:28] Alexander: That is a good question. That is a good[00:24:30] remark. Yeah, it’s, it’s not, not only is it about, okay, what is the treatment effect that is for [00:24:40] me and how can you reliably kind of extrapolate from the data set you have in the clinical trials to me as a [00:24:50] patient that may not be actually eligible to, to set a particular study because of Any, anything, and how much would that [00:25:00] make a difference, you know, because very often the indication is, is wider than, you know, the, the original studies.

[00:25:09] Alexander: Or [00:25:10] at least to some extent, there are differences. Thanks so much. That was a great first introduction into personalized [00:25:20] medicine. What it is, what it is not. Yeah, where are challenges in terms of what we are currently doing in terms of, for example, [00:25:30] just reporting clinical studies in our study reports into our publications into clinicaltrials.

[00:25:38] Alexander: gov. [00:25:40] And what are ways we can Build models to make better informed decisions for persons [00:25:50] rather than populations. Thanks so much, Thomas, for this first episode. Is there any last things that you would [00:26:00] like the listener to take away from this episode before we, yeah, in the future get into further details on this topic?

[00:26:08] Thomas: Yes, maybe, yeah, one, [00:26:10] one thing I would like to share because I’m currently editing a book on comparative effectiveness and personalized medicine research where we will have like experts on these topics from, [00:26:20] I think we have more than 40 at the moment authors that are contributing to the book.

[00:26:24] Thomas: And so a lot of the the topics we are touching upon today, but also in the. Successive podcasts [00:26:30] are, you know, we will dive into this in much more detail in, in the book. And, and the book is, I mean, is expected, I think next year to be published. So, I mean, it will, it will [00:26:40] take a bit of time still before we get there.

[00:26:42] Thomas: But in the meantime, I mean, I run every now and then I run some, some courses on this topic as well. And so. I mean, if this is basically, [00:26:50] essentially, this is a topic that you would like to be, you know, more familiar with, you would like to dive into a bit more, feel free to, you know, to reach out and, and we can we can [00:27:00] discuss to see what, you know, what is possible, how we can, how we can help with that.

[00:27:05] Alexander: Thanks so much for that great advice. And as I mentioned at the beginning, [00:27:10] you will find all the links to how to contact Thomas in the show notes. Thanks for listening. And thanks for the great discussion, [00:27:20] Thomas. 

[00:27:20] Thomas: Yeah, no problem. Thank you.

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