Network Meta-Analyses in Dermatology: Can You Trust the Data? Expert Insights with Dr. Aaron Drucker

[Music] Welcome to season 2 of Derms on Drugs, a video podcast brought to you by scholars in medicine that's the best educational platformer dermatology and providing no cost to medical providers. Derms on Drugs is for cutting edge dirt meets theater miscomedy. I'm Matt Zyres from Doc's Dermatology and each week I'm joined by my residency buddies, Dr. Laura Ferris from the University of Derms. I'm Dr. Ferris from the University of North Carolina and Dr. Tim Patton from the University of Pittsburgh where we use our 60 years of combined Derm experience to discuss, debate and dissect the hotest topics in dermatology. It's everything you need to know to be on the cutting edge of Derm and you'll actually have fun listening. New episodes drop every Friday on scholars in medicine, Apple Podcasts, Spotify, and other major podcasts platforms. And a reminder that there is a video component that has some of the key figures and tables from the articles we talk about. So let's go ahead and get into it. This week we are going to have a really interesting topic that sounds boring at the outset. So network meta analyses. So these get published all the time and all the journals. And we're going to go into the main idea of should you believe them? Because a network meta analysis basically takes a bunch of studies, kind of puts them all together with some fancy statistics and then tells you which drugs, you know, which drug works the best is the basic idea, but they don't always have consistent results. So we're going to get into it right now. Dr. Ferris, why don't you go ahead and get us started off. Okay, great. Thanks Matt. So I am going to talk about an NMA for Hydrad Nytus, Sepertiva. So this was a midgargs group and this was published recently in JAMA dermatology efficacy and safety of medical interventions for moderate to severe. Hydrad Nytus, Sepertiva, a living systematic review and network meta analysis. Okay, so what was this? What they did was they basically looked at several clinical trials of like 39 25 trials, 39 treatments, 6000 patients are almost 6000 and looked at efficacy and safety. So what did they focus on phase two, phase three trials looked for 12 to 16 week end points. And the main one that they were looking at was high score 50 and then in addition to that safety. So what they're really trying to do. We're going to get into what they're really trying to do as we go forward. Just give us the, what's the answer? What's it? What did it tell? What did it say? What did it say? Okay, what did it say? You know what it said? I thought was kind of like not what we expected, but it was like, at a lima map, it's pretty good and it's hard to beat. So part of that is a good catch word. Adalimates good, a good commercial. Adalima map. It's pretty good. It's pretty good. That's right. My marketing career, this whole academic thing doesn't work out for me. So once a week, Adalim and Mab was kind of the stand out here. So what does that mean? Nothing really beat it, but then direct, you know, comparison. So Bimikizimab, which I think we all know an anti-L17 AF dual inhibitor, was also a strong contender, Saki Kinimab, I'll 17 A inhibitor also showed strong efficacy, but did not beat Adalima Mab. So the other one that looked really good here, so they didn't just look at FDA approved drugs. They looked at ones that are under investigation was Sonaloka Mab. So you might think, what is that? Well, that is a nanobody that is kind of like Bimikizimab and that it targets aisle 17 A and aisle 17 F, but it's small. So I think the idea with these small nanobodies is that maybe they can penetrate tissue better. So that one actually looked like numerically beat Humeera, but the confidence intervals were wide because these are small phase two studies. So you can't really show superior superiority. The other one that looked good was ludicizimab, aisle one alpha and aisle one beta inhibitor and then poversittinib, which is a jack one inhibitor. Overall, generally low discontinuation rates around 5%. So I thought that this was interesting. So I tend to say I'm going to start with Adalima Mab and see how patients do with that. And from there, I'll move to an aisle 17 inhibitor and I still feel good about doing that. The thing that I thought was interesting was in Fliximab, which I also think is something that tends to be pretty effective for HS and that we use because we go to high doses didn't do so well. But you know, I think that was also based on sort of the size quality and control of those clinical trials. So I think that you know, Humeera still holds the crown. So the question I had was that I FX one I effect. I don't think that that was in Fliximab. Oh, okay. Well, that would explain a lot. It's kind of got to be in Fliximab. I mean, it's just when you look at the dosings of it, it's like. So we, but we dose that on Mix per Kig and if you actually, because I AI this and I'm like, what do they mean by I FX one in this paper? And he came up with this antibody that binds to C5a. Oh, I mean, that is something that is under and they're probably I don't have there been any phase three clip phase two or three clinical trials have been Fliximab. I don't think they ever really pursued. No, I don't think so. I was assumed that this was yeah, that's a good. Yeah, I was I was confused about that. I meant to text you guys before. Yeah, and then you thought you just make us look bad on air. Thanks, man. Yeah, that was good. I like that. I'm going to I'm going to put that in my back. It's not making you look bad. It's making me look good. I think there's a distinct difference there. Fair enough. So we're all right. Let's move on to our next study. I'm just going to make the one comment that it is often interesting. I believe that there's a little bit of uniqueness to the first study in a new indication. So that you know that the it's kind of a unique patient population at that point. And so maybe that makes out a little bit different, but we'll get into that with our guest and for everybody. I'll say one other thing. I think H.S. Studies are really hard to do. I think you know, Pazzy scoring me versus you this week next month is pretty consistent. I think it's really hard with H.S. to do these, you know, scores. So I I kind of attribute some of the variability in outcomes to that across all H.S. Studies not just here. All right. So let's get into our second one. This is what really got me interested in network meta analyses. So in 2022 March and April to network meta analyses came out for a topic dermatitis one title systemic immunomodulatory treatments for a topic dermatitis update of a living systematic review and network meta analysis. The other comparative efficacy of targeted systemic therapies for monitors very topic dermatitis without topical corticosteroid systematic review and network meta analysis. Right. So you kind of I assumed an Iively at that time that basically if if you did two network meta analyses for the same disease, you should get pretty similar results. And while the results were like vaguely pretty close, you know, there were some big differences. So the biggest one that really jumps out at me is that in the one that was published and that jumped out at me at the time was it a one that was published in jammed dermatology said that ever sit and able 100 milligrams was statistically significantly not as good as duplomab. Whereas the second one that was published in a different journal said that a bro 100 milligrams was essentially identical to duplomab. And so it really made me start to be like, well, how do these network meta analyses work? And it in its as you start thinking about these things, it really does make you go like this. This is open to a lot of sort of depending how they design it, what studies they decide to put in and now what endpoints they use, how they do all of the statistical mumbo jumbo. It really affects the outcomes of these. And so that's actually why we're doing this episode is to try and give people an idea of like how do you try and decide if you should listen to these things or not. Let's go ahead next to Dr. Patton who is going to give us an article that sort of touches on this. Well, yeah, the other thing I wanted to say was because the one NMA was funded by Abbey. Yes, it was and shocking it did show because the other thing that stood out was a patissette and of 15 milligrams. And the Abbey NMA was better than Dupy. And then in the jammer one, it was like, it's about the same as Dupy, the 15 milligrams dose. So that was the other thing that jumped out of me given the who funded the. Yes, I you are you are correct that that did that does sort of jump out at you as well. [BLANK_AUDIO] So that's one of the topics that we'll get into is I've kind of learned that if a network met analysis is funded by a drug company, I generally paid no attention to it at this point. And I can't wait for our guest to come on and tell me if that's barking up the right tree. All right Dr. Patton, go ahead. All right. So yeah, my paper was titled the Effect of Methodological Choices. An Inclusion Criterion Network Metanalysis Results. And psoriasis, this was by Gilemi Adal and the BMC Medical Research Methodology, April 2025. So we talked about NMI, NMAs and what they are. You know, you talked about that NMA funded by Abbey. One that don't always think out of me was it was a metanalysis on hair loss treatments published in 2023. So it do test drive, finaster, all those. And it came out with the best thing was this natural product formulation, ALRV5XR. And I'm like, what is that? And it turns out the metanalysis was done and funded by the company that makes ALRV5XR. So just that that was one that jumped out of me. I just remember thinking, right, if it's done by a company, you don't trust it. So the authors acknowledge in the background part of the paper overlapping NMAs can provide sometimes discord and results. They took a bunch of psoriasis studies. They specifically took them from the Cochrane Review. So those studies have already been kind of vetted. So you would say that they did the metanalysis on previously vetted studies. It was from the Cochrane Review. They changed analytic methods, outcomes they were looking at, which studies to include and exclude and they ran 560 network metanalyses on about 20 different psoriasis drugs. I didn't understand a whole hell of a lot about what these guys were talking about. I mean, this is the part that's confusing about metanalysis as, I mean, I am a clinician clinician. I don't understand what it means. So it's like, do I trust this, don't I? Are there things that you can look for? Are guests hopefully can help us there? But maybe you don't need to understand it. There were very slight, I don't understand how AI works, but I can still use it. Right? So it doesn't matter if you know, but yes, I would like somebody to, I would like to have a way to tell if the AI I'm using is likely to be hallucinating and lying or not. Yeah. They, the study showed so all these different analyses, there were very, very slight variations and efficacy of individual drugs, even when different methods were used. And if you were using any of the NMAs to make a treatment algorithm for psoriasis patients, what are going to be my six top choices? What are the six most effective? It didn't change much, right? Your top six were biny, exor, rizza, and flixamab, which that one surprised me a little bit. Seki kinamab, which that one surprised me a little bit too. Sonalika Mab, Geselkin Mab, or Dalimab, kind of right in that mix. That's what that figure three shows. It's like looking at Pazzy 90 from weeks eight to 24 and there's all these dots of where each drug ranked. Bimy was always ranked first or second. And the bigger circle in with the one ranking means it ranked first more often than it ranked second. The inflixamab data is interesting, right? So there were NMAs run where inflixamab, and they don't specifically go into the dose, but inflixamab was number one, that ranked the highest. So that's where you could have a drug company where Jansen made the first, like Remake had, right? Yeah. So they could say, all right, we're going to hire a bunch of PhDs. They're going to say we did an NMA, NMA, like they could easily run 560 cherry pick the one that showed it to be the best. And this is where I think the problem lies in trying to interpret NMAs. Yes. And so, yeah, let's also find the trust. Let's finish up. It's a Kizamab was anywhere from first to seventh, right? Inflixamab. Inflixamab, yeah. With anywhere from first to seventh, it had the biggest sort of spread. Yeah, right. Now, they said that a lot of the poor results of inflixamab was when they looked at one particular study where it was compared against methotrexate. For some reason, in the paper, they call that versus placebo, and they refer to that trial, there was no placebo. It was inflixamab versus methotrexate. Maybe they meant like comparator or something, but they were in methotrexate naive patients. And so inflixamab and methotrexate were, like, inflixamab wasn't much better than methotrexate in that trial. And that was the one study where they said that was the outlier. That's where it got kind of dinged for not being as effective. Okay. So it was a pretty small study, right? That wasn't a major study. So you got sample size issues. I also think inflixamab, the real inflixamab, not my fake inflixamab from my paper, is also hard because there's not a lot of standardization to, sometimes that's not standard how they're dosing it, right? Dosing frequency. Yeah. Frequency or constant, yeah. Yep. All right. Well, I think with that, yeah, we can bring in our guests. So we have got our special guest, Dr. Aaron Drucker, from the University of Toronto, who is the NMA guru of the world as far as I am concerned. He actually was the person who published that NMA that I talked about in my part. And we are going to have him on to try and help the Derms on Drugs figure out how to assess meta-analysis. Dr. Drucker, great to have you on the show. Thanks for having me. It is one of my favorite things to talk about. So looking forward to talking with NMA with you. All right. So we thought you were going to say we were one of your favorite podcasts to listen to. Well, that too. That's obvious. That's obvious. So first, let me give for our listeners my super simple, non-expert sense of what a network meta-analysis means. It means that you take all the studies and you decide, you put in whatever criteria you want to use for which studies you're going to include. And then the network is like, okay, drug versus placebo. But then if you've got this drug versus that drug, you plug that into and then that drug versus a third drug, now you can kind of link these drugs together in a network. And my guess is that they are very robust when you have multiple trials comparing different drugs to another, but that whenever the network is really everything versus placebo without many head to heads, my guess is they lose some of their robustness. Is that a generally accurate assessment? Dr. Drucker. I think for a lot of the things we're going to talk about today, the answer is, you know, it's not that simple. Yeah. A network meta-analysis that is most drugs connected to each other through placebo is going to make you question whether it's robust or not, but it's not necessarily that it's no good. So for example, our network meta-analysis, our living network meta-analysis for a topic dermatitis started out with mostly all these new biologics, jack inhibitors connected to each other through placebo, and we put out results and we could make some assessments that we thought were pretty robust about how these drugs compared with each other, but breathe a huge sigh of relief when we start to see some head to heads and they lined up with our network meta-analysis. So it ended up that it had been robust all along, but you need those head to head studies, those connections to things that aren't just through one comparator, usually placebo, to really assess that. So that's one of my first questions is, you know, you get sometimes you've got a network meta-analysis and then you get a head to head study and if they differ in their results, do you, it seems to me like you should put more weight on the head to head study than on the network meta-analysis, but we're always told that meta-analysis are the gold standard evidence. So that makes me quite like am I right that we should, that the head to head should trump the network meta-analysis or what do you do in that situation? So if I'm doing a network meta-analysis and I have a result that the term for this is incoherence where that doesn't coherent. That's also the term for Dr. Patton on Friday after 8 p.m. That's not to wait before 8. So if the direct evidence doesn't agree with the indirect evidence that's a big problem. You know, I'm going to have a big problem feeling good about the network meta-analysis that I'm going to try to publish and get out there and I think people who are reading it are going to have a problem believing it because if you have a well done randomized control trial of two drugs head to head, that does trump the result of indirect network meta-analysis and it doesn't mean you necessarily did something wrong statistically. But probably there was something different about that trial's population than the populations of all the other trials or the dose was different of the drugs that you're studying or maybe most of the trials in your network are out to 8 weeks and that was a 16 week study, something like that because really they should line up if they don't, it suggests there's important differences. And if you can't find any of those important differences, it's still a major problem in terms of believing the overall results of the network. So can I ask you just that? So the endpoint you pick has to really matter, right? So like with psoriasis, Pazzy 100 is going to look different than Pazzy 75, right? So there's, you know, now as like drugs get better and better, there's lots of things that can get a lot of people to Pazzy 75. So Pazzy 100 is a higher bar. So is it better to pick like the highest bar or something intermediate or does it not really matter when you're doing these? I think that's more of a clinical question than a network and analysis question because ultimately like you guys have been talking about the end users of these network and analyses are going to be clinicians and patients who are trying to decide between different treatments. And if Pazzy 100 is more important to them when choosing between drugs and that's the more important outcome that you should be using as a main outcome for your network and analysis. So we should really be thinking about that when we look at them, like what were they looking at? And if I really just care about safety or I really care about, you know, Mott Pazzy 75 and I should be looking at that. And what do you do with, you know, like if they have a high score 50 and 75, like how do you put all that into a model? So you have to treat each outcome separately. I mean, you kind of could combine, you care about some statistical way to combine them, but I'm a bit of a purest there where I like the outcomes to really be the same outcome of I'm going to combine them in one analysis. What we do in our living network and analysis is we have all these different outcomes that we assess separately. So we run an analysis for easy score as a continuous outcome and for the DLQI, the quality of life measure as a continuous outcome. We also run an analysis for easy 75 and easy 90 and I invest together global assessment IGA success. So we run all these different outcomes. We decided on them at least our most important outcomes before we ran any version of the network and meta analysis. We started out with the continuous outcomes. We added on some of those binary outcomes because people were asking for them. But that's not nothing that's really important is you want to make sure that all these outcomes that you're choosing. You're choosing them before you go about starting to run the analysis and not picking which outcome you're prioritizing based on what the results look like. Do you think the drug companies do you think they do run like like that one paper, Sirized Sirized's paper did you think they run hundreds of NMAs and cherry pick or is that something you would never comment on because they come after you? I'm not worried about them coming after me. I've never been in those rooms. He's Canadian so he's probably safe. I'll certainly be polite about it. I think it's possible that they run multiple versions of it but I think it's also possible is that if they let's just take the example in a topic dermatitis of concomitant topical therapy. So some trials they allow concomitant topical therapy patients are encouraged to use triamcynallone or whatever once or twice a day while they're using their new biologic. And other trials are not allowed and if they do they're considered to be using rescue therapy and they're treated differently in the analysis, considered treatment failure. So if you have a biologic and you ran one study with topicals and another study without and you notice that your difference against placebo looked better for the drug in the trial with topical steroids and it didn't trial without topical steroids, you could say, well maybe let's do our network of analysis just using the trials that include topical steroids. You don't necessarily need to run the NMA beforehand. You might have some idea going in of what are the better parameters to choose. Again, I've not been in those rooms. I don't know how they do this. I don't know how they make these decisions. But that's where a protocol is really important. You want to see a protocol out there before an analysis is done so you can decide, you know, did they decide on these important things before they went about doing the analysis? So how much work is it to do a network met analysis? Is it like you've got a computer program, you type in, okay, here was the percent of people who got the easy 75, here were the number of people in the study. You do that for, you know, the 15 studies and then you hit go in it tells you I assume it's not that easy. But I have no idea how much, you know, it goes into it. It can be that easy. There are statistical packages out there where you can just plug in those simple numbers like you said and you can actually get some pretty good results depending on what you're looking at. It can be pretty robust. But if you're doing anything more complicated, then you often need something more than that. Particularly for our analyses where we're looking at continuous outcomes, not just, you know, these yes or no, you know, did they meet easy 50 or not, you know, where we're looking at things on a continuous scale. It's a little bit more complicated, needs some more detailed coding. So you could pick, do a network met analysis, just using some web-based computer program that you could probably find, you know, an online doing a Google search. But for the more complex ones, you probably need something more. And you, when the NMA came out, the one done by Abby, we were kind of maligning them earlier. But in fairness, the study that they showed, Patissette and M30 milligrams, probably the most effective therapy, your met analysis came to the same conclusion, right? Against dupliab, probably more effective. But it was that 15 milligram where their paper said the 15 milligram was better than dupe whereas your paper showed 15 milligrams equivalent to dupe. When you saw their NMA and you saw it was Abby, could you read through the paper and be like, oh, I know why they showed that better. Or is that like somebody who knows all about met analysis, is that like impossible? You just don't know. So I, you know, I don't remember the specifics of that paper and the ins and outs of their methodology and how it differs from ours. But a lot of this is interpretation. And, you know, network met analysis, one of the big advantages of it that you guys have been talking about is that you can rank the treatments. You can say this is one, this is two, this is three, this is four, you know, across whatever efficacy parameter you're looking at. But those ranking statistics are really oversimplified. There's no confidence interval. There's no way to assess how certain you are that that treatment is one. If you just look at those face value ranking statistics. And, you know, off the top of my head, I think who patissette at 15 milligrams in our network met analysis is better in terms of its ranking statistic than you know. And, you know, the, you know, the, whether you're looking at easy 75 or the absolute change and easy, the difference between who patissette, sitting at 15 and doopy is so small that even if a ranking statistic says this one's two and this one's three, the difference isn't big enough that I care that one's two or one's three. They're, they're so similar. So again, it's all about interpretation. Some nuance, you know, there's going to be some spin. There was a nice article from that Cochran psoriasis team who've done a lot of great methods work in NMA that showed that that network met analyses that were published sponsored by industry were more likely to have spin in their abstracts and in the manuscripts than ones that were either not funded or were funded by academic funders. So that's another thing. Even if the statistics are kind of the same, you can spin it one way or the other. So how would you recommend? So for our readers or listeners who maybe are going to be reading some network meta analyses, you know, as somebody who's got as deep an expertise in this as it is possible to have, how would you recommend like trying to decide should I listen to, you know, I think I saw one of these a few months ago and it said, you know, XYZ, oh, here's a new one like or just no, I haven't, oh, here's one of these. I haven't seen them before. Like how would you recommend people go about trying to figure it out? Sure. Well, I think I've talked about a protocol a few times, a protocol is super important, right? An randomized trial, we expect there to be a protocol, we expect it to be on clinical trials.gov and we can follow it along and see, you know, what where their planned outcomes and what do they report in their paper. The same thing should happen for a network meta analysis where you know what they're going to do before they do it and then the results, in terms of how they report them, should match up with what they said they plan to do. That's one thing that's really important. I think industry sponsorship is a bit of a red flag. It's not necessarily going to be bad, but I think, you know, for network meta analysis, there's no good reason for an industry group to do a network meta analysis if there's already academic groups doing them. They're not that hard to do like we talked about before. Industry is great at doing clinical trials and they have the money to do clinical trials, but they don't need to be the ones doing network meta analysis necessarily. And then I think a third thing to look at is looking for that nuance. It should not be the conclusion is this drug is number one period, right? There's usually more nuance to it. You want to know about the other drugs. You want to know by how much better drug one was than drug two. And if everybody knows that drug is a drug, you know, it's a drug that's a drug that's everything is just focused on well this one's the best. I mean that sounds like spin to me. Even if in fact it is true that that one's the best. There ought to be more nuance in the interpretation. So the idea that these should come more from academia does make a ton of sense to me. And before I forget will you plug your online living website? It's www.xmatherapy.org correct? Eximatherapy is.com. So very close. And yeah so our network analysis is what we call living. So we run a new search every four months and even before we publish results in a journal we often publish results online. We don't necessarily run the statistical analysis every four months. If there's nothing really important happening, if no new important trials have been published. But we do put results up there as soon as they're available because you know Jammiderm doesn't want to take a new network and analysis every four months from us. So we keep things fresh up there. Is there a reason you guys only do it for Eximatherapy? Why don't you have www.Seriasistherapy.com and www.HStherapy.com and want to help us all out and then we could all just go to them and say this is the guy we trust. I'm flattered but also would point out that the Cochran group who's doing that psoriasis network analysis they're also doing terrific work. So they're living psoriasis network analysis. I put a ton of trust in and I think it would be you know really redundant for us to do something similar. So it doesn't all have to come from for me. There's other people doing great work as well. Can I ask you a question from this is from a more academic perspective and I know not everybody. Probably most people listen to us or not in academia. But you know I'm always trying to think of ways to engage our residents in you know clinically clinical research. I think something like this really would appeal to them right? Like they love treating patients like to think about how are we doing the best. If you had like a two-minute summary of your dermatology resident, your smart but you don't have a PhD. How would you get involved in learning? I think this would be a great you know sort of academic area for them. How would they get involved in learning to do this and doing it? It's a great question and you know for me I got involved with it completely by chance. I was doing a master's degree at Brown and my mentor was a Barcarucia who didn't really do a lot of this network mentalysis or any meta-analysis stuff. But the teacher of my meta-analysis course I did as part of my master's degree was an international expert in network meta-analysis and happened to be just starting when I'm basal cell skin cancer and that's how I got my first experience with it. I think the first step if you're a trainee interested in this area is to read to read some papers on the basics of network meta-analysis to read some of the really well done network meta-analyses in dermatology. And then if you're trying to find your own area and this the key is to find something that hasn't really been done or that hasn't been done well. So you've got to find a disease state that you're not just going to be doing something redundant to what someone else is doing because mostly time the results are going to line up and if you're just you know replicating something it's not going to be all that meaningful. So if you have a disease state that you're interested in that doesn't have a network meta-analysis or it has one but it's old or it has one but you're reading you're like well there's a lot of spin here or I don't agree with you know all the trials they put in here I would have done this differently even just thinking about it from a clinical point of view than that's I think where you can make a real contribution. So you did the NMA and right I think a patissette in a braint well doopy I think is competitive because of its safety profile. Are you ever approached by the drug companies to say hey you want to talk on our drug and do you say I mean would you or would you be like you know what I really want to be independent and not be associated or have people look at my work and think oh I must be biased is there is that hard to do or have they never asked you and so it's been easy. No I get approached you know to do consulting work or even sometimes to have discussions like we're having now with some of the drug companies to sort of explain to their staff you know what a network man also says and it's the easiest thing for me has just been to have a blanket no because you know I'm involved with an American Academy of Dermatology guidelines and I'm a non-conflicted member of that and as soon as you start to do anything then all that gets thrown up and is in question so I think you know there's those concrete examples of how it's been important for my career to to not be conflicted but then also I think just reputationally I think you know part of reason people believe our work is is because I'm not conflicted and I certainly wouldn't want to mess that up. All right I'm going to jump in and say while their discussion was going on I googled living network meta analysis for psoriasis and it did take me to the Cochrane review page and just for anybody who was wandering according to that meta analysis the most effective drug was it was in flixamab and then it went to bimicismab I mean let me find it again here so it was in flixamab let's see here modern certainty evidence zelligekamab which is not on the market then bimicism ab lebrichismab exochismab sorry in risen chismab so again in flixamab drug that's not on the market then bimicismab exochismab in risen chismab where the results are really gonna be like your new billboard top 40. What is it? That's like in flixamab is cheap I think in some of the other you like per pazzy score in flixamab is by far the cheapest because of all the biosimilers available I think we need to seriously reconsider and flixamab as a as a first line. Not on reason why I think it's my guess is it's not as safe as the other ones that may lose efficacy over time and you gotta go when I guess you can send people to their house to do the infusions now so maybe it's it's not you know for again for your for your regular practicing Durham I think setting up the home infusions is just so outside of what they're used to doing. The other thing I'll say is that you know one of the the assumptions of network metanalysis is it's called transitivity but essentially it's that everyone in one trial could have just as easily been randomized into another trial and you know the inflixamab trials were so long ago that the patient population going into a biologic trial then is probably not really the same as the the patient population going into biologic trials now and so you know there may be some sort of carryover effect just from the fact that the people in those trials were different we're going to run into that in eczema 2 where the people who are in the original dupe trials are not going to be the same kind of people that are you know in the new ox 40 trials now and there are statistical ways to look at that but but that might be part of why inflixamab coming out well there. Yeah it's an it's an interesting thing I always think that the dupe results sort of the subsequent head-to-head studies which didn't necessarily show that the jacks you know at well which basically showed that the jacks at intermediate dose are no better than dupe my guess has always been that the jack companies were surprised at those results because it seems like they likely wouldn't have done those studies if they knew those were going to be the results but it's it's an interesting challenge so I I think we've we've gotten as much clarity into the incredibly murky world of network meta-analyses my main takeaway from this so I guess the other question I would say are there any do you have any rules of thumb for how many studies should be in a meta and network meta-analysis before it becomes useful I mean I've seen network meta-analyses that have four studies in them. Is there and I assume the more studies the better the more patients the better but is any kind of a rule of thumb there? I don't have a real rule of thumb but I think you know generally you you like to see at least some head-to-head not everything connected through placebo of course that's not always possible and the results can still be meaningful even without that and you'd like for at least one of your comparisons to have more than one trial so that you know it's not just one study connecting everything you you have at least for one of the comparisons that you're making more than one trial. What do you what does that mean more than one trial so you have at least two trials of drug A versus placebo is that what you mean by that? Yeah exactly. Okay is there is there a metric of the average distance of connection so like right if you said to me okay for every you know for this one every single one goes to placebo okay your distance of connection your average would be two right drug A to placebo placebo to drug B whereas for every head-to-head now you've got a one right which is drug A to drug B it is it's is that is to, you know, so the more head to head you have, the smaller that number gets. Is that a metric or am I like, did I just come up with something that I should trademark? - Yeah, I think that sounds like it would be a worthwhile statistic I've not seen that, but it might exist, but I haven't seen it. What I do is I just look visually, and we call it hub and spoke, where everything's just on that, you know, placebo spoke and all these got all these hubs coming off of it. So that's how I assess it, but a number would certainly be useful. - Okay, but. - Hey, look at you, man. - Uh-huh. - Oh, it's funny acting out of it over there. - It's coming up with questions that are unanswerable. That's mine, but I would give an answer, right? That's an answerable question that I make up an answer and nobody can really challenge it. - Did I do it? - Speaking of unanswerable questions, you got some trivia for us. - Let's go, Pat, and what do you got? - So, Dr. Dirk Ruggand, the rules are, you gotta let Pat and finish reading the question. As soon as he finishes reading, you can shout out your answer. - All right, so I just went into statistics and interesting historical facts in my little deep dies here. - Before you do that, I gotta say one statistical thing that I always thought was a big thing, but it has never come up in real life. The idea that a drug, if drug A, you got 100% of people 50% better. And drug B got 50% of people 100% better, and 50% of people 0% better. They would both show the same average improvement, but would be very different drugs. And that's, I think always been interesting. Like I always thought that'd be a big, one would have a tiny standard deviation and one would have a huge standard deviation. But just, I've never really heard, like statistics people don't talk about it. So I guess that isn't something that really comes up where we get drugs like that. But, anyway, that's enough of a digression. - I should look for sobbing them. Yeah. - All right, these two men were the only authors of the 1958 paper published in the Journal of American Statistical Association titled Non-Paramedic Estimation from Incomplete Observations. - Bays. - Everyone. - What's that? - Bays, one of them. - No, but you're on the right track. We've heard these two names thousands of times. - Watch it and click. - No, they did statistics. Maybe survival curves. - Capital higher. - Capital higher. - I think Drucker got it out. Ferris, you finished at the same time, but Drucker started. So 1.2.5. - It's the Canadian delay coming over the border. - Yeah, I should just give them a whole one. - That's right. - Yeah, so it was Kaplan and Meyer. Edward Kaplan and Paul Meyer. - That's the study that brought Kaplan Meyer curves into the world. - Yeah. - They both kind of came up with it independently and then somehow got to talking to each other and they're like, let's do it, we'll publish it. - Okay, interesting. - The interesting to see how they decided who would go first because man, it could have been Meyer Kaplan. It was that close. - It was alphabetical. - That sounds right. - That's right. Okay. All right. Number two, using statistical graphics, this woman showed that poor hospital condition caused more deaths than battlefield injuries in the Crimean War. - Nightingale. - Yeah, Florence Nightingale. So she was kind of like her own, she did her own like statistics and she put together, it was called the Coxcomb graph. Really cool representation, you can still find it. She paid more money to print it in color because she thought that was a more effective way of presenting her data. And basically the British army was like, oh my gosh, like we need to increase sanitary conditions in our hospitals, our guys are dying from non battlefield stuff, dysentery, things like that. - Okay. All right, one person. - This one's way too long, but it was a thing where I was like, huh, that's why we do that. All right, you guys ready? - Yep. - Ask. - William Sealey Gossett was a chemist and statistician that worked for the Guinness Brewery in Ireland. And he developed a test to optimize brewing processes using small samples. Because of a company policy, he was not allowed to publish his methods under his own name. What pseudonym did he use on the publication describing his test later called the T-test? - Cox. - No? - Wallace? - No. - You usually see this as the blank T-test. - Students. - Yeah, student. - Oh. - That's why it's called students T-test. He had to publish under pseudonym. So he just picked student as the name he would publish under. And that's why I got called the students T-test. - I thought it was 'cause students learned how to do it. I never knew where it got into. - I always thought it was something like that too. Yeah. - All right. - Shoot. - I thought I was pretty good. - That's it. - It's a three way tie. - It was a three way tie. You guys did a great, that was our best competition ever. - Yes, it was pretty good. Well, all right. I wanna thank Dr. Drucker for joining us. This has been an interesting conversation that I think has made me better at thinking about network meta-analyses and I hope it's helped our listeners as well. I wanna thank everybody for joining us. We hope you learned a few things. Hope you'll have to answer twice. And mostly we're hoping you're planning to join us next week. Until then, I'm Matt Zyrus. - I'm Tim Patton. - And I'm Laura Ferris, and we are Derms on Drugs. (upbeat music)