Do You Waste Your and Your Teams Time Without Knowing It? 3 Case Studies and One Reason

Transcript

Do You Waste Your and Your Teams Time Without Knowing It? 3 Case Studies and One Reason

[00:00:00] Alexander: Okay, so today’s episode will sound maybe at first glance as a medical affairs episode. But if you look into it a little bit deeper, you will see that this is really for everybody. Recently I had a discussion with a more junior member of the team, and we were discussing about the workload, all the different things that are going, on how much priority all these different things have.

And I realized there was really a lack of understanding. What is really important. Yeah. So if you work in medical affairs, it can easily look like there’s an endless task list, like 20 different abstracts to work on. There’s new posters coming through. You also want to publish some manuscripts. There are slide preparations for Q opinion leader meetings.

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There are additional requests for feasibility analysis of new publications. There is additional post talk analysis because someone asked for it. And of course, then you also have these endless meetings and all these kind of different things. Maybe you also have additional studies that you’re working on, phase 3b, phase 4 studies, there’s additional kind of real world evidence coming in, all kind of different things. What do you do with this?

First, first example. We had a discussion about these type of studies quite a lot recently in the longitudinal life cycle special interest group. I know that this is a topic that affect many of us across the industry. And these are single arm studies. You can have single arm clinical trials and you can have single arm observational studies.

Now, the difference between these is that in the clinical studies, the treatment is paid by the sponsor. At least as long as patients are in the study. If you, for example, run a study in a chronic disease, and you put, let’s say, 200, 300 patients on them, and you only pay for, let’s say, the first three months, because then the study runs out, then, of course, these patients stay there after, usually on the treatment, and then it’s paid by government, reimbursement, insurance, or maybe even the patients themselves. But it’s not paid by the sponsor anymore. Second part is observational study. In an observational study, you only observe. Which means you don’t pay for the study medication or the medication that the patient is on. And if you collect only data on your medication that you provide through all kind of different channels, then of course this medication means for the company sales.

Yeah, so in both cases. These one armed studies can lead to quite significant commercial upsides. Let’s make just some, rough calculations. Let’s say you run a study with 500 patients, and the study may cost, let’s say, 10 million, just to have a number and it’s and these drugs, let’s say it cost 50,000 euros per year. Yeah. If that runs, over four years, let’s say on average patients on four years on the treatment, then that means on average for each patient in the study, you have a commercial return on investment of 200,000 whatsoever dollars, euros per patient. And maybe, for your clinical study, you have covered the first three months.

It’s still, nearly four years of treatment that is not covered. The mathematics… More or less are the same for these short term studies. Maybe it’s a little bit different for the clinical trial because the overhead is a little bit more expensive. Although I know many big companies that run observation studies more or less like clinical studies, which creates therefore more or less the same cost, which is… I think a completely different topic because it’s a waste of resources, observational studies should be far cheaper, but that’s a different topic for a different podcast episode. Anyway. So getting back to the mathematics. So you have 200,000 return on investment times 500 patients means you have 100 million of sales from this one study. If the study costs 10 million, 20 million, you have an upside of 80 to 90 million euros dollars whatsoever. And that is very often. The real reason behind why these studies are run by companies. The return on investment is pretty clear. And that’s also clear that, if you do this calculation, that people really want to have 500 patients on the sponsor’s drug and not do something that is comparative, like putting 250 patients on the competitor drug and only 250 patients on the own drug. Because basically that means you don’t get 100 million, you only get 50 million. And I heard people say, why should we pay for another company’s product?

And that is really the thinking behind this, yeah? People very often see that primarily as commercial tools. You could even say that goes into the direction of bribing the people, yeah, because the physicians get reimbursement for participating in the study and for the documentation, all these kind of different things.

And of course, that all these needs to follow rules and guidelines. And we all know that these rules and guidelines are partly subjective. Yeah. So for some physicians, it might be really attractive to participate in the study. But of course, they can also only participate in the study and get money from the sponsor when they put patients on the sponsor’s drug.

And that’s where really the problem comes in. Because then the study drives prescribing patterns. And then it becomes really nasty, ugly area. Now, from a statistics point of view, you may be assigned as a statistician to the study. Is that a top priority for you? Probably not. Because we all know from these single arm studies, you will get nothing substantial. Yeah, you will not be able to do provide any claims placed on it. Maybe you get some low quality publication somewhere, but marketing sales, medical scientific liaisons will not be interested in it because there’s hardly anything you can do with these studies, yeah? You can’t speak about efficacy, you can’t really talk about safety very often because you don’t have any comparative data.

In the end, from a scientific point of view, there’s very little output from it. And that’s why I think, as a statistician, if you get assigned to such studies, and you can’t, change them into comparative studies, into high quality studies, then invest the minimal effort you can do. It really doesn’t move the needle. If you’re up for promotions, and people speak about, tell us about the impact that you have. Publications from this study will not have any impact. Just from your promotion criteria, from your internal criteria for how you are evaluated, you will see that working on the study doesn’t really help you.

If it doesn’t help you, then the incentives are set in such a way that you shouldn’t work on it. Full stop. Yeah, I know some people might disagree that incentives for personal things and, what’s good for the overall company are two different things. I don’t think so. Yeah. A company sets the incentives in such a way that it helps them achieve their goals.

That is example number one. These one armed observational studies And you can waste a lot of effort and time, but because you lack the big picture on it. And so if you see the big picture, you know that you shouldn’t spend a lot of time on this. So you can deprioritize it and work on things that have more impact.

Second example, outsourcing of clinical studies. Maybe you as a statistician are included in discussions that we outsource to this big company X or big company Y or big company Z. And you can spend a lot of time, talking about, is A or B or C or X or Y or Z better. From a stats perspective, it often doesn’t really matter.

First is, from a financial perspective. If you think about the overall budget, roughly 90% of the budget goes to clinical operations. The time the CRAs spend, the money that goes to the physicians, all these kind of different things. Yeah. 90% of the rest 10%, 90% go to data management and all these kind of other areas.

And this last little part, this last percent, that’s approximately where stats comes in. Now, if you think from a overall perspective, from a company perspective, what will change the needle in terms of vendor selection? Clearly not stats. Yeah. Most likely, not even data management. It’s clinical operations. Because a little bit more on the clinical operations side and a little bit less on the clinical operations side is bigger than the overall stats budget. It doesn’t really matter in the bigger scheme of things. Yeah. Therefore, that doesn’t, help so much. The other point is, if you work with these big organizations, with these big full service providers, you probably know all of them, yeah, the IQVS, ICONS, PyXels, you name them.

Yeah? They’re probably on your preferred vendor list, and in terms of quality very often you don’t know what you will get because these are big companies and really depends on in the end what team you get. Yeah. And yes, within these companies, probably a much higher variability in terms of quality than between the companies. Why should there be a super big difference in terms of stats quality? Yeah. I’m pretty sure the people that work in these areas and these companies disagree, but they all recruit from the same pool of people say, switch between these different companies all the time. And in the end, it really is.

Your personal luck, whether you get a team that is good or whether you get a team that is less optimal. And in the end, big picture thinking, it doesn’t really matter how much time and effort you give and provide in selecting which kind of pick vendor. I think it probably makes much more sense to have a discussion about whether you want to take out the stats part and move it differently, yeah? So assign it to an organization where you know the persons that you will get. Maybe that’s a smaller vendor. Maybe that is a specific stats vendor, or where you know that for this study, you will really get Fred the statistician and you know that Fred and his team is good. That’s a very good other point. Maybe it makes more time to invest in this than deciding whether it’s Icona or QVR or PyXels or whoever. Again, big picture thinking. If you understand the big picture, you can prioritize your work, you can focus your work, and probably you can even get rid of certain tasks because they don’t move the needle.

Let’s come to the third example. Publications. In many areas, There are endless streams of medical publications. I have seen people raving about that they have during the launch period, 40 abstracts at an international conference, 30 abstracts at an international conference over 100 abstracts during the launch year, all these kinds of different things.

Yeah. And especially for these bigger compounds where you have different indications and you continuously have readouts, you can have amazing numbers of publications. And of course, sometimes these come from really bad publication practices. I’ve once seen at a conference where people analyzed a three item quality of life. So that had three questions and it was assessed, I think, at baseline and maybe two or three times that follow up. It was not a complex study, it was, two arms and so on. Instead of putting that all into one abstract poster. The company has decided to put it into three different abstracts and three different posters. And of course, then you end up with lots of posters and abstracts. Now the question for you is, if you have all these different publications to work on, yeah, if there’s yet another poster, yet another abstract, yet another analysis coming, which of these are really important?

What are the ones that you should focus on? This is a really difficult question because all of these are in your publication plan. In your publication plan, say, there might be, not a big differentiation in terms of what is important, what is not important, and maybe you have some kind of priority publications and these make, I don’t know, 5 or 10% of your overall publication list. But you’re working on these other 90%, so what is really important? Here again, looking into the big picture is really important. A publication is just another step in the overall communication cascade. A publication is there so that people can use it, speak about it. Who will use it? People within your commercial and medical affairs areas.

The Medical Scientific Liaisons. Also sometimes called medical field force. The people that go to key opinion leaders organize advisory boards, give a lot of presentations, are really into the scientific discussions of your data. Do they actually pick up on your publications? Do they include these in their presentations? Do they speak about these with their key opinion leaders? Do they organize advisory boards around these same with your commercial team or your pricing reimbursement and access team, the people said negotiate with insurances and payers, maybe on a global, not on a global, on a local or sublocal level.

So with individual payers or was in Germany, with the governmental system, always nice in the UK. Do these people use your publications? If they don’t use your publications, probably these publications don’t help them, or maybe they don’t know about them, but then you have a different issue. If nobody is using your publications, they have no impact. If not, even your colleagues are using them. Now, how can you avoid that? Pretty simple. Speak about the publications you work on and explain to your commercial people, your medical scientific liaisons. You don’t need to speak to all of them, maybe a few of them.

What will you do with this? How will this help? Your work, is that relevant to you? All the outcomes that likely will come out, will that be relevant to you? If they stare at you and think what is this person talking about? This has nothing to do with my priorities. You probably work on a low value project. If on the other hand, the person says, that would be really brilliant to have these kind of analysis. Then you’re probably on the right path. Yeah, and then you can more understand how will that be used, what are the key kind of topics, what analysis you really need to have in these publications. And here again, understanding the bigger picture is really important.

Now let’s sum it up. Yeah, we looked into three different cases, see one armed observational study, see outsourcing, also publication work. If you don’t understand how all these kind of different things are used, you can’t understand the impact. And if you can’t understand the impact, then you can’t prioritize. And you can waste a lot of time, maybe 90 to 100% of your time, doing things and working on things that don’t move the needle. That will not help you in your promotion dossiers. That will not be seen within the company. Working on these is really crazy. And it’s also dangerous. Imagine, there’s a budget cut. Can you imagine who will first be set free? Those people who can’t explain the impact. Now in medical affairs, I know all of these kind of different things are really difficult. Now, sometimes on the regulatory side, it’s much easier. You have directly discussions about will that end up in the label? Will that not end up in the label? Will that lead to differentiation? Will that help with HTA? All these kind of different things. Hopefully you have these discussions. In medical affairs, it’s much more difficult. And this is why Jenny Devenport and myself have created a course that can help you and really help you to understand what’s going on overall in the medical affairs area.

And that these discussions will help you be more focused, be more effective, get more impact in less time. Does that sound like a really good idea? Not to work like crazy and have, endless task list, but to know what really to focus on and where to put most of your time and get rid of all the other things that doesn’t move the needle.

Check the show notes and it would be awesome to see you in this course that Jenny and I, have developed together and recorded together. And we’ll also have lots of direct interactions with each other. So that’s a fun part that I’m really looking forward to.