Interview with Ena Bromley
What is a statistical unicorn?
What should we be doing to become one?
Gary Sullivan introduced me to Ena and she’s a really interesting statistician. She was actually quite ahead of her time when she founded her CRO.
Listen while Ena and I discuss how to improve one’s career and how to make good choices that help you become more effective and have more impact at work.
- How Ena started
- What are the statistical unicorns
- How to become a better communicator
- Sharing of experience about an important presentation
- Key lessons learned from her career
Ena (Christina) Bromley, Ph.D., M.Sc.
Dr. Ena Bromley cofounded BioStat Solutions, Inc. (BSSI) in 2001. The company was acquired by PharmaLex in September of 2001 facilitating expansion of analytical services internationally for PharmaLex. She recently co-founded Analytika, a consulting company focusing on communications and analytics in the biotech and pharmaceutical space.
She is trained as both a statistical geneticist, genetic epidemiologist. She has been contributing to regulatory agencies along with biotech and large pharmaceutical companies in establishing criteria towards precision medicine and targeted therapeutics for complex disease. She has provided her expert opinion in the design, analysis and interpretation of complex data. This include representing companies towards regulatory approval for vaccines, drugs and medical devices. She also serves as representative on various data safety monitoring committees. Due to the urgent need for expert analytical knowledge of vaccine studies, she has provided her expert opinion for various clinical studies involving SARS-CoV-2 (COVID-19). Previously Dr. Bromley was involved in advising vaccine and therapeutic studies for Ebola, Plague, Rabies, Influenza among an array of infectious diseases.
She serves on the organizing committee for the Bayesian Biostatistics Meeting, BAYES2020. She is also co-chair for MBSW (Midwest Biopharmaceutical Statistics Workshop). Dr. Bromley cofounded BSSI on the principle of effective communication between analytical experts and clinical investigators and Ena continues this passion as a consultant for C-level executives to better understand opportunities as well as effectively avoid risk when exploring opportunities within the pharmaceutical, biotechnology and device industries. She utilizes her knowledge across various therapeutic areas as well as her network of key opinion leaders (KOL’s) to bridge the communication gap between scientific expertise and investment opportunities.
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Alexander: You’re listening to the effective statistician podcast, a weekly podcast with Alexander Schacht, Benjamin Piske, and Sam Gardner, designed to help you reach your potential to lead great science and serve patients without becoming overwhelmed by work. Today. We are talking about Statistically Unicorns and the interview with Ena Bromley and really take care of that. That’s a really interesting one. And not just if you’re a little girl, unicorns are interesting. This one will be really great. So stay tuned. And now some music. Gary Sullivan introduced me to Ena and he recommended her and all when I heard about that. I thought, hmm, that must be a really interesting statistician. And I learned so much from her and I think she was actually quite ahead of her time when she founded her CRO, and you’ll learn about that. And this podcast episode as well as lots of other things that can help you to improve your career as well and to make choices that help you become more effective and have more impact. So stay tuned forces awesome discussion with Ena,
I’m producing this podcast in association with PSI, a Community dedicated to Leading and promoting the use of Statistics within the healthcare industry for the benefit of patients. Join PSI today to further develop your statistical capabilities with access to the ever-growing video on demand Content Library, free registration to all PSI webinars and much much more by now. There should also be lots of content from this year’s conference. So from the 2021 conference, which was an amazing event, head over to The PSI website at PSI web.org to learn more about PSI activities and become a PSI member today.
Welcome to another episode. Today. I’m talking with Ena Hi, how are you doing?
Ena: Hi Alexander! I’m doing well, how are you doing?
Alexander: Very, very good. We had a little bit of a pre-chat recently with Gary, but maybe for the listeners who don’t know you, can you start with a short introduction of yourself.
Ena: I would be happy to. So I started my career actually in animal statistics, animal breeding genetics and came to the states and studied more restricted maximum likelihood models. And that’s when I did my predoc. But then I went to NIH to do a postdoc and statistical, genetics and genetic epidemiology was what I focused on. And while I was at NIH, doing my fellowship, I realized that there’s this communication gap between the statistician and clinical investigator and that we speak one language and statisticians. And the usual is unique needs on whether it’s Roland with a specific endpoint or whatever it might be that the clinical investigators are struggling with. And sometimes we talk past each other. So that led to the idea that it could be a good thing to start a company with that model. Do to Foster communication between a statistician and a clinical investigator. So we started a company 20 years ago. In 2001, my husband and I went by Stat Solutions and focused specifically on trying to listen to what the clinical investigator has to say. And then utilizing the statistical models around that best answer to use it almost like a surgeon tool in the tool box, to see what is best we can employ specific clinical study or how can we advise towards an optimum study. So that’s the story in short, really bridging that gap, that communication gap.
Alexander: Yeah, and then you grew that company over many years and recently sold it. So, congratulations on this nice exit strategy, after such a long time. I guess it wasn’t easy to kind of let go of it.
Ena: No, I think the good thing is, it was late going to a wonderful team formerly exactly German company, but borders and it was so nice to see them continue that vision. But really, you know, to get a team together that has that aptitude to, to communicate to utilize different scenarios where it was a blind maybe in devices or biologics or drug development. And then we also had this biomarker add-on that we really specialized on. But it’s a wonderful team and then really to see how, when you focus on communication when you focus on what is best for a specifical study or two to work on a specific study design. It was good to see and it’s good to see folks excel within that field.
Alexander: When you talk about the communication aspect. Where did you see the biggest gaps at that time? In terms of, where did you see the biggest problems? We have any kind of, for example, specific stories that come to your mind.
Ena: Yeah, I think the biggest gap for us is as statisticians, we get so excited about specific models, about specific algorithms, specific tools that we use. That we sometimes forget that there’s an end-user that may not be as familiar with the statistical methodology and to visualize the findings to communicate that findings has been really something where you need to know your audience. If you deal with a small biotech company or a large Pharma company, maybe a slightly different message, or if you phase one study or preclinical versus a phase three studies would be a different message. And it sounded really, if you can visualize something, a picture says a thousand four thousand words and people catch on to it, and sometimes, you may have a few versions of visualization to see what works best to explain a specific hypothesis or objective. And I really found that that’s a fantastic tool. Especially now that we have our shiny, we have a lot of visualization tools to our disposal that once we use those tools and give that to the VP. At a company, that works very well. We also worked with executive summaries on that quickly. With the way we see executive summaries, something you give a hand of an executive that’s running from one meeting to another, that can get that so what message across. And sometimes it’s just a statement, right? But sometimes it’s that hey, this is your response. This is a specific group that responds well, and this is how we visualized it and you see that light bulb go on. And that really helps a lot.
Alexander: Executed summary does not necessarily mean it’s just sentences and some words. It could be, maybe two statements sent a nice visualization, yeah.
Ena: Exactly, we evolved in, you know, we started way back and waited until the executive summary looked like an abstract and boredom factor came in and we lost our audience. So we had to really change that to, whether it’s a sentence or two, whether it’s a figure, whether it is, you know, how Hey, watch out for the specific subgroup of covariant or what you need to look at. But some want to be there and then you can always have a statistical report underneath it, that has that statistical rigor, that has all that details in for the statisticians you are communicating it to, but it’s so essential to know who your audience is and, and to be respectful. Yeah, you know, maybe somebody that has a vast amount of clinical knowledge, but maybe not as much analytical knowledge or sometimes it may be somebody that is very well versed. So knowing your client, knowing and with client, I mean, it could be a colleague could be a client, right? But it could also be a true client. But to know and to read the individual to to know when there’s question marks on their face and to respect those questions and then come around utilizing the tools we have.
Alexander: Yeah, I think it’s also respectful to respect their time, yeah, and say to invest, and make it short, and sharp. So that maybe the three key points of this 300-page Statistics report really fits on the Alpha page.
Ena:And that’s an art. It’s so true. But you cannot say everything in a short summary, and I think it helps you when you have a summary or you have a key message that you need to get across. It helps you to not get that tunnel vision down, one specific alley. But to say, Hey, you know, why am I doing it? Is this the way of doing it? What should my next steps be? What is important to the person I’m communicating these findings to? And to know your data, the data tells the story. I remember my advisor for my dissertation would frequently sent me to a coffee shop and say let the data speak to you, go and know your data understand some of these new ones that’s going on in your data because that’s sometimes is to keep going that you need to communicate across, but I think we’re so rushed many a times that we just dig in and do the analysis instead of saying, well, I have unique distribution properties or I have unique phenotypes or endpoints or something is major at. My missingness is an issue, that spending that half an hour to kind of get to know the good, the bad and the ugly of your data is so key in what you want to communicate.
Alexander: Yeah, and here data both in terms of the raw data, but also the results I think it’s kind of, if you have, you can do a, lots of lots and lots of different different subgroups endpoints. And, know, things over time, and you need to look into correlations and all kinds of different things. Not just kind of deliver the analysis but say what they mean with it. Don’t just kind of throw the tables over the fence. Here are the results.
Alexander: I need, to be honest, very early in my career. I also sometimes say that because I didn’t know better. And then my step supervisor told me. No, he expects us statisticians to have a look into the results and interpret them and say, you know, work together with others, like medical writers and things like that.
Ena: Absolutely. When a unique position in statisticians, right? When you have lived and breathed that data set for a while and you’ve seen some of the nuances in your analysis. It’s almost an obligation that we have. Is to report something unique that we see to see that Serendipity sometimes of hey, this is something this unique pattern or these this unique finding and it’s also what is not there. Sometimes. It’s what you have in the data. Once the true results jump out at you but it’s sometimes, also when you work on conclusions or when you work on what’s next steps. It’s maybe, you know, I didn’t see this in this data set. Or we didn’t look within this effect modifier covariate. Or and it may be useful to do so. So, it’s a little bit difficult to look at what is not there.
Alexander: I think that’s when it becomes really interesting, if you have certain expectations, and these are not met in your data set.
Alexander: We need to understand why that is the case? Is it a mistake? Yeah, so I once saw a data set of the about, I think it was about ADHD and 80% patients were female where usually it’s the other way around, and that’s weird. Of course it was a coding mistake, but if you then see, ah, You and to study we don’t see this kind of correlations that we see as well. Why is that? Yeah, do we have a specific population and things like that? And kind of anticipate the questions that your audience will have.
Ena: Yeah, and I think statisticians are eternally curious. And if we lose that curiosity about our data, then you don’t go into that unique pattern, right? And I hate when you sometimes go into something like you say, was the females, is that a mistake? Or is that, you know, maybe enrollment bias or you know, what happened or is this a unique study population? And what is the story? We are really story writers, and behind that is the truth. It’s the day that we have and I think that’s the beauty of being a statistician and a fun being a statistician really.
Alexander: Yeah. I love that you talked about visualization in the current context of communication. Yeah. When I asked you about communication, one of your P points, you directly jumped to was visualization. And sometimes, you know, speaking with statistatians has had visualizations. Yeah, that but this data takes so long. And is it really my job? What do you tell these statisticians?
Ena: You know, this is the flip side of the story. If you have this amazing finding and you cannot communicate that finding to the end user, you’ve lost it, you’ve really the value of all your efforts are lost. SoI think it’s almost the priority that we need to focus on our visualization and so much of our coding for visualization. You can reuse that if you have something somewhere that you’re really doing, but it should almost be that you could, time aside. Set time aside to do the visualization in order to get your message across because if it’s just a data dump what’s the worth versus spending, maybe 15%-20% of your time and you do good visualizations. We’re good figures that tell you exactly what you’ve done and then somebody can use that, or somebody can come back to you and say hey really interesting. So let’s go with that path and look at something. I always think it’s sad that sometimes we don’t do that to each organization and some ways, you know, these pressures this time. And we need to push back a little bit when we’re in a situation where that is not required and we know that that visualization can really get the message across. Instead of, you know, five pages where you’re going to lose your audience on paragraph 2.
Ena: That one bigger might actually tell the story.
Alexander: Yeah, and I think in terms of programming, if you have set up your systems quite nicely, you can become really, really fast. At the PSI conference the keynote is from a graphic designer and data visualization experts that are working at the financial times. And one of them reporters had only a couple of minutes to do a visualization for the, you know, for the front page for the cover page.
Ena: Oh, wow!
Alexander: And he produced it in seven minutes. Okay. That’s maybe a kind of very, very fast thing. Yeah, but In our world, usually we have a day or something like that to get it right.
Ena: Absolutely, and I’ve seen some of our statisticians that were absolute whizards in visualizing and they would actually sometimes come and push back. Like, hey, this plot may be better than one in conjunction with the data and a message. Because on one side of the data, and nn the other side you have your message, right? And that bridge is your visualization.
Ena: And knowing those right tools, more inexperienced statisticians I frequently refer to the National Geographic. I think they were earlier on if you take up a National Geographic magazine and see their visualization. So amazing, and even now with covid, there were some really beautiful visualizations that I have seen people publish on, or whether it was in the media and we keep learning. It’s an artist painting, a picture. And that, that’s what our visualization is. It’s a masterpiece.
Alexander: Yeah, but It’s an art that we can learn for sure. You know, what free discussion, you mentioned. You will always be on the lookout for unicorns, statistical unicorns. What’s that?
Ena: I think It’s something that’s gonna keep following me. When you go through the interview process to try and find a new member of a team. I frequently would go and have those questions.
Ena: How would you take it? How would you approach the data? How would you analyze it? How would you look at the data? Based on theoretical knowledge. But there were very few times where candidates would show up that would not just focus on using the results, right? And with that, I’m interested in the clinical aspects. I’m interested in biology, and physiology. I’m interested in visualization. So, we talked a lot about the visualization, that to me, is that Unicorn? It’s somebody that kind of gets that so what, question and usually that excitement somebody that’s excited about what they do. That has that enthusiasm and truly trying, you know, ultimately we’re serving for us. If the patient population is making the world a better place by serving the patient population. And if there’s an individual that gets excited, About that too, to really think about the data as a privilege to look at. That was to me the Unicorn and absolutely somebody to contact the theory and communicate that theory, so that somebody else can use it. That’s hard, right? Sometimes we all get stuck into it that there’s a bit of a, their methodology, there’s a better theory. Something more complex, especially now do we have complex data, sets with long, additional data, mobile devices, and so forth. But then how do you boil that down into a message? Again, that’s a Unicorn, that, that you’re looking for and I’m seeing more and more of it. And I’m optimistic, that that’s something that we, as statisticians, are starting to pay more attention to.
Alexander: If you look into these Unicorns in your company and your career, what are they specifically doing to get to that level?
Ena:: Some of it, I think it’s the individual. It’s the individual being curious by nature and being committed to what they are doing, but I think there’s training involved. I think there is good mentorship involved. To where through our experience we communicate, maybe with somebody that’s fresh out of grad school on what tools are available, right? We can’t be trained in everything. And there’s really so many different subspecialties within statistics that you go and constantly expose people, for instance, to the client to understand that communication or sit in on meetings. They may not necessarily be participating in meetings, way in the beginning, but they understand the bigger picture that it’s not that years, a data set, analyze the data set, you know, throw these tables at me, all these results at me. But think about it a little bit and create that time for the individuals, to think of the data to have those interim meetings, that where we discuss or brainstorm around along the meetings. It’s almost like a ground round, right? That you go and you say use this patient, use this data set, use this result. Let’s talk about what we found when we haven’t found it. What’s interesting is where do we take it? Where do we run with specific directions? And how do we communicate? And communication always has to be, has to be in the game when it comes over time and different individuals, we don’t respect the individuality of different people. But I also think if you bring that team of statisticians together or data analysts to give, and they bring this different talent based. You get great ideas and we learn from each other and we grow for each other, on how to ultimately get a good product out.
Alexander: I think it’s having this willingness to constantly improve, to constantly challenge yourself. And learn from each other, and it’s also kind of trying to always serve your audience better, kind of not just be kind of satisfied with the status growth that, you know, provided the table job done, that mentality. But really kind of making sure that the message is understood.
Ena: So very true Alexander. I don’t think our discipline has room for egos because there’s so much to be learned. This, in a way that’s fun too, right? We statisticians are sometimes the conductor of the orchestra. We have regulatories, PK, we have clinical groups. All these groups around us, but it goes all about the data. And what does the data tell us? And when we keep growing, keep learning but also keep growing. Taking those new ideas and producing products from those new ideas. That’s pretty fun stuff. It’s where you serve so many better.
Alexander: Yeah, I got some interesting perspective from looking at it. That you are basically the conductor and you’re not kind of somewhere in the back, maybe it is a triangle one.
Ena: Exactly, I think traditionally that was kind of maybe a statistician was put in a corner, right? We got officers without the windows but more and more so. Think about it when there’s a phase three study that there’s data log, who’s the first one to know whether that study is successful? It’s the statistician. It’s the one looking at the data before anybody else really knows. So, we play an important role, a very crucial role. In studies.
Alexander: I think I completely see it the same way, if you look into all the details of other areas. Yeah, let it be regulatory medical writing, clinical development, the Physicians, Medical affairs, Payers, Market, market access, marketing, sales, medical education. So many different functions, all kind of depend on data. And yet, they don’t necessarily speak to each other. Whereas, everybody really needs to understand the data and that, where we come into place.
Ena: Absolutely, that’s the conductor that we are playing, we’re connecting those dots and it’s a big responsibility. If you really think about it, they know your data. You cannot just crunch data and shoot it at a table or figure or listing, or whatever it might be. I think we do have this responsibility and I think that’s where it’s so in a way fun. When you have those teams come together and you may sometimes mention something to a clinical investigator and you see that light bulb go on and somebody jumping up and down of excitement. Because of something that you just mentioned about thinking like, oh, okay, I’m going to dig into that analysis a little bit deeper. And you know, we make it something to be successful. But if there’s no market update, then where are we really? And I think that statisticians frequently miss that unfortunately. There is a business aspect to it and we need to understand the big picture, as well as being into the minute detail. So sometimes you have to kind of jump out a little bit and get back into your day.
Alexander: Yeah, and I think that’s an interesting point. Also kind of as a conductor, you need to make sure that everybody plays the same song. And you know, if one part of the orchestra plays a song with great efficacy, no safety risks and the other one says safety first, don’t care about efficacy, then that will not lead to a nice kind of song overall.
Ena: Exactly. And sometimes we focus on efficacy, and sometimes it is focused on toxicity. How do we bring those pieces together to play? And so she needs to stop for that moment and maybe rewrite the music by saying, hey, we focused on this one area, but we are seeing something that’s concerned maybe with safety and do we have the numbers? What can we say? What makes sense and what doesn’t make sense? Right? What is observational?But what is really there and something that’s worth conducting. So I think maybe we’re conducting while we write the music.
Alexander: I guess that is probably the case, and music is changing all the time. And it also depends on the audience we play it too. Yes, do you play it to a sudden, regular talk, or do you play it to maybe a player or do you play it to, you know, GP? Yeah, every audience has a different favorite song and so it’s the same here in this case.
Ena: Yeah, it’s so true and you mentioned regulatory and I think that’s where sometimes we will also get stuck because regulatory is such a rigid environment that we need to address specific aspects and topics and we sometimes feel like you do not have a lot of play in that. But I beg to differ, I think we do even in the regulatory environment that we still have some of those things that we need to communicate or something like now. This is it, but they are different. If you’re an early discovery, you do have a little bit more player, right?
Ena: you’re gonna be looking under different stones to look at different things. Then what you will have when you finish a regulatory study, but knowing that audience and knowing who’s looking for what is so important.
Alexander: Yeah, speaking about that. A couple of months ago I had a discussion with the VP of regulatory and she told me we absolutely need to have many more data visualizations in our reports. Yeah, we need to better tell the stories because regulators also kind of want to understand fast say, you know, they don’t always have time to look into all the different details. And if they can, you know have a couple of different reports, a need to look into and one looks like oh that looks interesting with a couple of nice figures. The other one is hundreds of tables. Well, guess who’s it? What do they choose? Yeah.
Ena: Yeah, and if you think of the folks that work within the environment, they work with multiple studies, that comes across their desk. And if you cannot communicate with the regulatory, that’s a challenge. And I unfortunately see where we fall short, a lot, this with interim analysis and data, safety monitoring committees and you just get this stack of things that you have to work through. And you know, the other side of the statistician that could have visualized data a little bit bigger or summarized the data a little bit better. Instead of just don’t be a lot of T’appelles. So much is lost there. I think that when we don’t do it and you know what it is, I decided to monitor. It’s looking and the safety for the patient population to hear me and just diving through endless listings. You could even sometimes miss people.
Alexander: Yeah, I completely agree. I think if you as a statistician set yourself a goal that you need to understand the data out of the database log really, really quickly. Kind of, let’s say half an hour. You need to get a very, very good overview of all data. You probably can’t go without good data visualizations, yeah.
Ena: Yep. I totally agree and sometimes, maybe I’m just old school and I hope not, But sometimes I see what’s frequently even business. What’s the distribution if your primary point is that that’s sometimes even best, right? And if you have a little distribution plotted to put through, see what your data looks like. That’s something very basic even if I see a mess and to do a few descriptive statistics in the beginning, when you get your data, you know, it doesn’t take long to do those descriptive statistics and yet, that can help you so much understand, what is your next steps in analysis or communicate, maybe some of the challenges that you have for the day.
Alexander: Yeah, or include the individual patient data in your data visualizations.
Alexander: Oh yes, so many really nice ways that you can embed that in your data visualization. So yeah, to not just get, you know, the means and standard deviation and the p-value, but you get your really kind of the individual data plots and you know, if they’re ordered disordered colored in a good way. Then it can directly tell you, whether maybe something weird was going on.
Ena: Yeah, or something really exciting.
Ena: And yeah, right? And the beautys is right now you can do that and realize you can look into something that you’re going back to shine that you’ve programmed in that. And you can really show. If you control for this, if we take this out group out of the, we put this group in or if we look at a specific time point, you can really start seeing some of these unique signals that you would really miss otherwise. And again folks who are not statisticians, without exceptions, we folks, get excited about that when I see that visualization and it’s just a wonderful tool we have that we can use.
Alexander: Yep, completely agree. In terms of presentations there were interesting stories that you told about, you gave a presentation, a very, very important one on short notice. I think that’s also something that a listener, for sure, wants to learn about. What were the circumstances there?
Ena: Yeah, it’s one of those stories that you’re wanting to write if you didn’t anticipate. So all in it. I think behind the stories is really sometimes how important we are as Statisticians. So for folks who go and represent clients with regular agencies, you know that sometimes a Statistician is sitting there in a corner and you’re ready to add to answer something. We were working with a client and it was on a device and it was an international client that flew in from Europe. And as so if you guys know, if you practice and practice and anticipate so many things as you get ready for that meeting. So it was an orthopedic device so we have an Orthopedic Surgeon. And we were going through really being very well prepared, but then we had to travel from the hotel where we did all the preparation to the FDA, the CEO of the company would have done most of the presentation. Then I got lost, so I got this text and frantic call following it that they don’t get to make. They are not going to make it to give this presentation and then please, if I could give the presentation so all of a sudden I changed from being this Statistician in the back of the room to. Oh my goodness! So much is on my shoulders and I looked at the Orthopedic Surgeons like anything clinical helped me out of it and because we prepared so much we could we could literally look at each other and it was one of those meetings where we didn’t know where it was going to go and he was a few issues and I presented every now then I looked at him and he helped out of the clinical. And as I finished the presentation, the CEO walked into the room. And it was such an adrenaline thing that, you know, I would never have expected to happen, but it happened and it was real life. And we had to think very fast on our feet and move it forward and at the end of the day, we had some laughs about how it went well for the client. That certainly while it was happening, there were stress proteins being released. I can tell you that.
Alexander: I can completely envision that. It’s already stressful if you are kind of mentally prepared to do it, but I think it’s a completely different game if you jump kind of out of nowhere into it, but in the point you mentioned, of course you did a lot of preparation. Yeah, so I think that it also speaks to the point in terms of great communication. It always needs a lot of preparation as it leads to a lot of practicing and that kind of expertise. Grows over time and grows over your career. So you have actually been training for it for many, many years and it’s from your capability point of view.
Ena: Yeah. And also I think one thing that I really kind of afterwards after you digest, you know, something like that happening. Is the fact that as the team that was there, we knew each other. We knew each other’s expertise and strengths. And we kind of benefited from that, so you can jump and say “Hi! I know the stats. I know the design, you know, and I can also I think that plays the role of the We talked earlier on that statisticians really knows everything. So we went and could communicate and jump back and forth as a team and that comes with time. It really does come with time. But it also I think it comes with wanting to and being excited about what you’re doing. It’s really a fantastic time to be a statistician and we just look around us. Let’s go a bit. Everything. We see its data, its data being interpreted, that really drove Society now for the past 18 months and they say statisticians behind that.
Alexander: Yeah, and it’s data. It’s simulation of data is modeling of data. It’s the communication of exponential growth. There’s so many kinds of interesting things and you see it. Where the statisticians are not capable of communicating it and influencing it in the right way, Tragedies happen. Yeah, and that’s especially I think ruins our health care industry. Yeah, if we are not doing your job right? Then the patient’s health, or even their lives are at stake and the overall picture and also you may think like, well, I’m just producing numbers, that’s in the end, it affects real people.
Ena: It really does. And that’s why I sometimes try to communicate like, for instance. If you look at kaplan-meier, every event, the death, it’s somebody that lost a loved one. These are the real people behind this data. They are people that’s desperate for a solution for a treatment option, and we might not meet those patients. I’ve had privilege a few times with clients and meeting the patient’s but many times it becomes just a data point. And I think we need to remind ourselves that it’s not that those are people who in their desperation, sometimes volunteered for studying, its people who committed to make the world a better place volunteered for those studies and enrolled in those studies, and with an amazing privilege to be happy for them, to analyze that data to find something, maybe that can help somebody. And that’s our responsibility. It’s also a wonderful thing to be involved with.
Alexander: I completely agree. When you look back over your career. What would be your key lessons from your careers that you would like to see? And lists to get back to setting up.
Ena: We touched on some of them. I think I will always be willing to learn. Never think, you know it and always stay curious and be excited about what you’re doing. That would be the key message that I would like to get across. But it’s also, you know, looking back at my career, I would say that it was those, there’s moments where I may be arrogant about the data that I would make a mistake. But to be humble, to have a heart of serving others and to make the world a better place. To remember that. This is something real life. This is a wonderful opportunity to make the world a better place. Would be kind of them in summary.
Alexander: Awesome, very good that I can very much relate to that. And I had some earlier interviews with people on Oswald often. And he, I think, talked also about exactly these two points. He had a couple of others, but was staying humble and kind of looking out, being curious. This is really important. Thanks so much for this. Awesome interview. I really enjoyed it as we talked so much about Communications and how important that is in our job. How data visualization plays in but then also how you can become better and better that even if you put in front of the FDA on short notice, you can perform and tell a story. That’s what I think we should aspire to get to. So that if we talked about it really can impact our patients and make sure that it’s the right decision to be taken Based on data.
Ena: Thank you so much Alexander was a privilege.
Alexander: This show was created in association with PSI. Thanks to Reine, who helps us with the show in the background and thank you for listening. If you like this, if you love this discussion, then please let us know as well. It would be great. If as many people can benefit from these insights as possible. So share it with your friends. Share it with your peers, share it with your colleagues and If you have something great to say about it, you know, tell it on social media, reach your potential lead great science and serve patients, just be an effective statistician.