Thank you very much. Can everyone hear me okay? Yeah, fine.
Um, so I hadn't really thought about the western part of it that occurred to me on Thursday as a well-respected Western.
I don't know if you're into Westerns, but I will seek to cover the good,
the bad and the ugly as we progressed thinking about life as a trial statistician.
Um, this is why I wanted to talk about. I'm going to give you a little bit of background about me,
so maybe help explain some things I'm going to talk about or moan about and complain about and also where I'm coming from.
Other trials too, would that a different, slightly different set.
But I think there's enough in common they'll be interesting to everyone
hopefully here and I don't want to finish on ugly the problem with the bar that
some of the fellow that volunteered to write personal law to see if I can put
it that way I start they'll might vaguely remember us as an undergraduate.
Obviously I did a student project related to clinical trials, randomised controlled trials, which is prior to 1999.
Um, and from that there was an opportunity to do a Ph.D. which was really a topic,
but a colleague of Jill's at the time and then later a colleague of mine that I stayed on.
Um, so my interest has gone through all my professional career.
It's gone from very early on in my studies into clinical trials.
It's mainly randomised controlled trials working up in Aberdeen in the Health Services Research Unit and then for a year I spent in Canada.
So I got to see of a different environment and at about five years ago I moved down here to Oxford for the Centre for Statistics and Medicine,
and happily enough people I was already working with from Aberdeen put in a department and there was an opportunity to move into that
department which CSM so quite complex get story basically to two sets of people I worked for kind of moved to make a nice set up for me,
a way to transition in quite a gentle way after my big break from obviously in after 15 years or so.
I have been involved in all trials for over 15 years.
But I know what you're thinking. I'm not sure.
But the young fellow and I'm very vocal about 20 trials of various types, I think from very small one month bond sort of trials,
if I can put it out in the old traditional sense as, as, as it was usually one man who did everything by himself.
Clinician But then from about a bunch of that was pretty much it was slightly better than that but had my start,
but it was only slightly better overall. But also they were moving into the much more diverse and better on high quality, large, expensive trials,
sometimes too expensive maybe trials that we can get nowadays and from a variety of different funders and different types of treatment.
So I mainly have work looking at non-drug treatments and specifically those that involve surgery, perhaps double the burden.
And what people might be familiar with know the regulation of language, of systems, um,
physiotherapy and other types of things which you didn't even know was something you could put on a trial and told from the beginning to time.
I had an interest the there was lucky to work in a group that had an interest not just in doing trials but having to do trials on.
And I have my PhD in topical these things kind of are there with you think your fear of them come back to haunt you some time for crossword puzzle
so she got hopefully some sort of reputation for I also work in some of our generic sort of trial issues but very much with applied science.
So you will know I can assure you see any form in this presentation might be a relief for this time on a monday.
So there you go. I know I've been involved in lots of teams.
Have you been introduced to that terminology or something you'd be familiar to?
70 years or so they are monitoring phase and PSC or trial steering committees or the equivalent of management committees,
etc. I've been involved in both of them bundling that only with the trials for research.
So it's a fairly broad experience of trials,
but very much on the academic side and then involved in the various different roles from both sides of the fence.
So then more recently I've been managing some trial statisticians, so I.
Do the hard work, really undermine other people I work with, do the hard work, and I think we try and help them occasionally.
Okay.
I'm happy to take any comments or queries, by the way, if things go wrong, but hopefully most things will change for some food for a better place.
So. So what's so cute about clinical trials where you probably had a bit of a story,
but as a statistician, there's probably a few ways you can think about.
If I was to give you one phrase of analysis, it gives you a safe and great environment to do statistics.
And one thing that you should never take for granted is that this is a design study.
By definition, a clinical trial is some sort of experiment or study looking at the impact of the treatment.
Therefore, that implies someone thought about it before that happened.
Let me reassure you, unless you're in any doubt, people do come to you and they haven't thought about what they're doing.
Just happen to think you might be able to explain to them why they're bothering, quite frankly, and why they do move over to you in the first place.
Whereas in trials you're nice, safe in the sense as designed,
and therefore also the data you're going to get is prospective and not just say so much horrible message from you.
I'm generally speaking of funded studies as well to various degrees,
but that also ensures a certain degree of thoughtful rigour and sort of due process oversight studies or regulation.
I'll come back to that kind of regulation,
but that is a very positive thing because it ensures data quality in a way that isn't true in many other areas.
We do get very high quality studies or not clinical trials, but you get very many studies that are not high quality for clinical trials.
And so this process of regulation and understanding what we're doing as a serious business leads to death.
I would say, generally speaking of a better quality in a review process.
And then lastly, I'd say it's for a while.
We are doing something that's useful and adds to our understanding, understanding in a very altruistic generic sense.
We're trying to gain insight so that we can help improve patient's health and we can make
judgements about what treatment people should or shouldn't get and who should be treated.
So we're addressing something important, but our the analysis are not very important and then it's very well,
there are phases that people use in clinical trials, phase one, phase four, which works well for terms of drug treatment.
But really there are very, very few actually even on the same page.
This is a slide by some countries. Consider the phase of clinical trials which probably no one will agree with exactly with all things.
But what we do have is is a real you can see just very easily the aim of what you're trying to do is on trial,
trials vary quite a lot from very exploratory to efficacy.
So called in part of the treatment post-marketing surveillance.
There's language that might be used in an industry setting, but the size is very, very small studies.
Two very large studies of population can vary. The design is building on that.
And whether we even have basic things like the control group and the outcome and how we're going to assess the results of the study.
And I have mostly focussed on what might be called phase three ones here and even rules are very,
very mixed, but by far the same thing about being involved.
And so I just want to emphasise a little bit about our systems because we need to remind
ourselves that they are a really good thing to do because this is what most of us,
the strong statisticians, spend most of our time to do it and you can view this in many different ways.
You've probably had some of those today, so I'll just walk through very quickly.
But you can describe a very simple language. You address the scenario of a depressed treatment or not treatment or treatment for people.
The scripts are expected to be similar.
It allows you to isolate the factor of interest. That is the treatment.
Usually it prevents intentional, intentional bias.
It provides clarity on inclusion and section. Point of participation is not something to take for granted.
You do get into discussions about who is in my study and who should be in my study
and all those things you do if you want to think of it more as a statistician.
But I'm actually a different way that we can think about that or describe it.
In fact, that's one of the, I think the exciting and good things about being trial sessions.
Trials are fairly complex. They're also very simple.
So the concept very simply is tossing coin or Ian Chalmers used to use and talk about children when they separate into two teams.
Tick tock. Talk about this very simple concept.
You're trying to roughly randomly create two teams. Two groups are going to be several.
In that sense, you're trying to play their game here. You're trying to say the only difference is a treatment.
Therefore, I told the team in March, that is true. But it's also true that we can think about in a more mathematical, complex sense.
I'm the one where the word you'll often see is exchangeable.
It just means basically that everything is the same except for the treatment.
And you can write out formulas and justify it in that basis.
There is a whole framework of statistics, classical statistics, which you will have been introduced,
I would think, over in this question billion ones to some degree.
But a really cool thing about randomisation is it justifies an analysis with no program assumptions.
It's not necessarily what we do there today as a statistician,
but actually there is a theoretical basis for doing an analysis for creating things like a feasibility that doesn't rely on anything else.
And then there's also a very different way that is growing actually more recently in the literature, which is a causal way to think about it.
And you can express things in terms of the observed or the counterfactual.
But if you think about clouds, they are a little bit counterfactual in the sense that you create two groups.
We can never really assess what we want to know, which is if you have a patient in front of you and you're a doctor,
you you want to really know what would be the result they would have with the two treatments.
So you get treatment aid, but you kind of go back in time and then say, okay, let's, let's give them treatment.
And that's the kind of factual.
But you can express trials, not when you can actually take that into another direction in terms of mathematics and thinking.
So that's another way you can do it.
And then lastly, have Bayesian statistics, which probably not touched on too much,
but that's another way that you can think of randomised controlled trials and about you might think
of trials in terms of refining your understanding or updating your prior by getting good data.
Okay. So if we take that for granted, what are the other values or what the good things about being a trial starts to show?
Probably the best place to start is high impact commodities.
That's my heart. And you will see the language gold standard.
You've heard of that that properties are the gold standard way to prepare people for something like this is number four.
Clinical trials are the most definitive. To provide radiation therapy for three years is very 5 to 8,
which is very nice ultimately in many regards when it comes to treatment about and comes to research about
treatments or cities or clinical trials more generally are the standard which other research is held to account.
And they are the most researched area. And roughly speaking,
the methods in the process are most of the working of that is that they're
generally the most well respected and they have the highest potential impact.
And that impact can be in two ways. It can be a direct impact.
If you work as a trial statistician, your work on a trial, your trial might theoretically change things straight away as soon as it's published.
That's very exciting, but certainly is not true of most trials.
So I'd be lying if I stood up here and told you that every trials will do that.
Everything's going to change straight away.
Nevertheless, it's also true that some of them do have a dramatic impact from this, a potential there for your study.
So it is also true that the impact of your study might be more subtle or indirect, but outcome come over time.
You are contributing a trial result which might be then taken with a body of evidence that might eventually end up in a systematic review.
You would hope for maybe a better analysis, and through that you're contributing again to the practice and the updating of practice
and the understanding that irrespective of what the result is in many regards,
this is a sample of a paper that was by a colleague of mine in the department, and he was involved in a study called Draft.
I don't expect you to be able to read the book, but the title you probably can read,
which says Do large pragmatic randomised trials change clinical practice?
What is interest? This is a graphical issue. What they show here is the use of different treatments for the condition or contrast.
This is orthopaedics, this is trauma. And they've got wires, which is a blue line plate, which is a red light, and then they've got other.
So the offer is not what they were evaluating and the format stayed pretty constant over this period of ten years, which is shown on the x axis.
But what happens is there's a quite dramatic change, or particularly an uptick in the Wire,
which was one of the two interventions is trial play, it being the other.
And because this is a very large trial and then influenced involving the large
teaching hospital by purely doing the trials seem to actually influence the practice.
People become more aware of the alternatives, we're told, which is not always true in surgery about the options are available.
I'm actually started using and then when the results of the trial actually came out about 10% even more so the trial very quickly,
even before it was actually completed, seems to have influenced practice.
And then when the results came out to the issue of the use of wire, this has continued in this quite dramatic increase.
This is another one. Again, forgive me for using Peter examples because it's where I work most familiar with of recent.
There's another study which again in this trial is called for for all I want to highlight here is a conclusion which may or may not be able to read,
but it says proper has had an impact on the surgeons clinical practice both for changing that and underpinning existing non profit practice.
So trials, aside from the correct results, they also can be an embodiment of good practice in many clinical areas.
Surgery is certainly one and here they're highlighting that although not everyone accepted immediately the result of the trial,
everyone knew about it pretty much showed, a lot did, and many recognised that the practice was being applied.
So it's just another example of how the trials can have a big impact.
This for me personally is my most recent example, and if you get a paper in The Lancet, you like to tell people it doesn't fit in with The Lancet.
Hence why I have to say that this just came out just last month, but it's got some uptake in some newspapers and whatever.
So it will be interesting to see, but the word is that it's actually starting to influence people's focus.
The problem here. This is about shoulder impingement, which was a growing and reasonably common shoulder operation.
We did what you might call a placebo form of the shoulder operation.
You can ask me afterwards about it, but you're interested that we didn't show that there was much benefit for the real surgery.
So it's quite controversial.
And if you're into ultra metrics, it's got something like 1300 results for medical school, which is pretty good, given that most people have a ten.
Okay. Later on,
I think something that's hidden because a lot of statisticians who are not really areas on trials is the variety of work and issues that are raised.
And I think that, you know, almost any type of analysis, at least to some degree,
we can argue any kind of very simple things like Fisher's exact facts that,
you know, the names or T test and quite robustly and confident of the use of some aspirin and probably some clinical trials in randomised trials.
Having said that though,
there's such a wide variety of issues and concepts that are addressed in statistical literature that's common to some of the trials.
So issues such as clustering,
if people are familiar that the idea that some observations are more similar as a group to each other than they are to every other observation.
And so you get a variety of statistical methods like multilevel modelling and GS.
These are things that people familiar for other areas and are options.
You have the longitudinal aspect.
Of course, most cases you have different types of outcomes, quite different types of programs which bring up different types of analysis.
Just for any regression you can think of, you can find a way to analyse in a trial somewhere.
And then you also have more recently a lot of the causal work which I alluded to earlier,
this whole dream of growing literature online, which interests enough.
A lot of it has come from economics and the interest of doing looking at the impact of policy in health economics
and trying to isolate factors that actually inspire the use and analysing clinical trials and increasingly so.
So don't be misled by thinking.
It's all just one very simple thing when you do the same type of analysis of the research and past array of different things.
And the core analysis, as I say, can be straightforward, but there's many interesting lawyers.
Our next topic is, I think it's generally accepted that you're a valued part of a team, the level of study,
your value for your expertise, even if sometimes about corruption like people need to your office,
for example, let's say speak to a statistician looking for the final flight and then 6 hours later,
at least to get sample size, they might think vaguely.
You should do something more than that. But more often than not, increasingly is my experience.
Maybe I've been fortunate is that people do recognise your expertise,
they do recognise the value and to recognise more generally as a trial statistician,
you understand things about the study, about how to do what's the right way to do it.
How could you improve a study? What's the right way to go about it?
Sometimes from the more getting rid of them than the number actually coming, then actually the problem will continue.
And I think also that's been my experience that you get to work with some excellent scientists.
The whole process can be quite some things.
And so for the clinical trial, it's a long drawn process.
But one positive is that people who tend to get there at the end often are fairly normal.
This will help to converse, but often very good for experienced researchers and people who have good scientific understand.
So they're quite voluble people to be around and just spend and be part of the team when you will work.
For lots of professional people, particularly when you're in the law study,
there will be professional managers of the project, will get the professional data managers as well to have there,
although will be programmers who specialise often in doing files and not expertise,
is a very exciting thing to be part of and they're often along with nice people, I think.
And then what is fun is that you're there to take over.
I would say a unique trial, there is a sense of trials, is a robust methodology, and that's true.
But the application of it every time I would say is unique in some ways that the trials of little life to those trials and events happen,
whether it's personnel related or otherwise. And the makes it quite an interesting thing to be part of an unpredictable what's going to happen.
So when you're working employing together to get to the finish line.
And so that's really enjoyable.
One of the special things having a baby in a house station is that as well as being part of the team, you have a very unique perspective.
You're one of the few, maybe the only people sometimes who other than maybe the C.I. who's involved from the very beginning to the very end.
But we start possibilities there for you from the very kernel of an idea or working group, the PICO type idea,
or all the way to thinking about funding and the grant application and getting
that and then stirring up a study and then running out and all the rest.
And then to the analysis and the reporting.
And also when it's running as a statistician, you get through the data, you get to understand how things all connect up.
And I find that not everyone who's involved in travel really has that perspective.
So it's really quite a privilege, I think, in some ways to do that.
And it allows you to improve and to all think you have something useful to say about data collection and how it forms and complete.
You have something useful to say,
but databases and how they're set up and how the data comes out and how it's processed and about data quality and all these other things.
But um, here is a picture of a team and I still enjoy being part of it, but it's not because there've been some dramatic, glamorous things.
It's not ended up in The Lancet by any means.
But what was very fun was it was people who wanted to do a trial that were new to it, but there was a keen interest.
And so I put off also being part of the picture.
I think this was tweeted by the Journal. You wouldn't believe it,
but I personally have enjoyed working with that group and it was a new area and they were just very interested in keen to do what's nice to say also.
So we did a pilot study and now we've got funding from the property company, so we're continuing to work together.
Another nice thing as a special thing about being across cloud services is you get to know the answer for
a number of people first to know they are monitored and I'll come back to them on the negative side.
On the positive side, you get to know the answer. Everybody wants to know it's that's great.
And then the final answer as well,
that's the most special thing because the results in between can change and you don't get always to see the full picture,
but you will be the first person who sits down with the results and you will know for anyone else really which way this thing is going to go.
So we'll see. So we knew we and the other of this, we knew that this was going to be quite controversial.
That's quite exciting. I quite like this quote.
So I learned that some things I think it is a slightly funny position as a statistician that you have this information,
but you've got to not reveal it. But it is quite an exercise like well, okay, but scale of work I three see.
So if I'm going to say the negative only because it's very familiar with me on the scale.
So soft spots are not bad, but it's quite a long work to produce a good fair one.
This is what we had for so others will produce much longer document.
There's a lot of work involved, lots of analysis.
I tend to think of primary outcomes or maybe a few of our outcomes.
We have nine analysis that's of only the primary in the main paper.
We also have supplementary paper at 18 and also some primary outcome 20 year of secondary outcomes.
So that's 46 analysis.
Now one of the things we're publishing, which is very interesting,
is you keep producing the same thing in different formats and every time it gets revised you have to double check.
And every time someone's touched it, it's changed. And so I consider recently those 618 numbers in the press that we needed to check.
Now, you would think that if you checked the ones in the proof, you might think that you can just rely on those numbers being right.
You'd be wrong. So it doesn't matter how good a journal is, you will find.
In fact, we found two numbers which we hadn't asked for any changes and there are other ones
where we had asked for changes and they haven't changed that the way we asked them.
So we found two numbers that were correct and the original proof that were then wrong.
And so you have to do that checking every time.
That's in addition to all the checks that we did on the multiple versions prior and multiple collaborators.
So it just gives you a feel for the scale of work at the end, which then you if you think through back the way to vote, how much?
So with the analysis, it's a lot of time and a lot of effort, mortality, whether some people seem to go away with what?
Come as they're kind of all study but certainly not to clinical trials is a long process.
My first involvement in the study was November 2010 to get involved in the drama published November seven,
about seven years from initial search, which was 2016 to publication 16 months.
So I actually when I knew the results, we knew the results of the team that 16 more months to get it published.
I thought it was crazy business and everything, but that was it was only two journals,
one journal to face and plan about a year finally to side with the back and forth with about 40 page briefs,
submission and response to reviewer comments before they decided they were going to reject it.
So even when you think you're tired, we're not actually done. Which brings me to my next point, which is increasing demands sops.
I put those as necessarily legal. I guess they are there.
So who knows how many sops and I don't even know the results that they have.
Nobody ever knows how many. So. So it's always changing and the SOPs are always changing.
I am not most fond of sports, but they do and are necessary and increasingly necessary.
And part of what clinical trials like.
But particularly if you work on drug trials and it is something as a trial statistics you have to accept, I'm afraid.
But as part of your life on the frontline, you are part of your life very little.
And they very much symbolise.
It's not really fair to say this, but it's also very much symbolise the increasing scrutiny and expectations that have come about for good reasons,
mostly about trials and about safe conduct, about people's data, and about handling things appropriately and robustness.
And if you have consults and photo ops, they will be very useful.
When you need to defend yourself and defend your study more,
you've got to put personal expectations on data alongside that and there's been a much greater emphasis on it.
But when the scale, when you put it together scale,
it's very demanding about crafts in the process and as a statistician to sign off and check and make sure and it's true what I said before,
that you are you are the person who can look at all these things, but it falls on you and you're the person has to make sure it's done right.
And you have a thousand data items in the database to double check, and you have to check that they've all got the right courtroom as well.
It's totally new. So and that's, I would say, increased in the time I've been involved.
And then also more so about how it's getting stored. And this is an area that's still developing and will be increasing for the next five,
ten years about where data ends up and about the process and want to make it available to others.
So even when you finished, you haven't finished.
We've still got other responsibilities that will go on as a trial statistician beyond what you think of as your involvement at full time.
And then on top of that, there's some more documents. Again, for good reasons, all have the right place,
but it's just more things that you're involved and more things you have to make are consistent and standard.
And so it does create work, good as they are. Another bad side.
This is what someone told me that recently I was an independent statistician.
And you see, this is all the old first place you stop in the middle of a war.
That is a statisticians. But in general.
But it's very much true as a trial statistician don't usually get the the lead the first off the ground with the
one you usually get to do the presentations that give you all the glory you will be stuck somewhere in the middle.
The good thing is you get involved in multiple studies, but the bad thing is people will sometimes think, well, what did he do?
Did he actually do anything? I assure you I did. But there are no convinced because you are far, far, far away from this fairly often.
So to some quite depend on the perspective of people who are high up in universities.
They're not convinced you're doing them or rational independent become the promotion more difficult.
I'm talking in an academic sense, but perhaps the same might be true of an investigative degree.
You may not always see you as a leader.
And so it does say a problem of think, which is fair to say.
I think you can get there in the end, but it tends to be a little bit more difficult or slower.
And I think reinventing the wheel, this is my other bugbear of travel appeal.
Every time I'm doing something with someone from before and done it better, but somehow I'm still doing it.
And what can work of the and it's been solved.
But we can always be assured, of course, the good things that we always go round with.
So you will get there in the end for those seem like the groups waste of time but there's a path.
DMC is very good to do. DMC don't get me wrong, but they are a lot of work and you get quick turnaround.
So you're expected to do things maybe in a couple of weeks and turn out a
report or 30 pages and not make any out of it and then people pore over them.
It's never as automated as you feel that she is the last thing totally true of DMC.
You feel like you've done one 2003, but that for a member of a poster you can get another better.
This is the Sword of Damocles. There is a study of up an operating corp of Damocles group, which is a great piece of work,
which was giving guidance on Dmcs and how to set them up and also just barely hum the terms of reference.
You can use authority. And so that idea is the sword of Damocles of sort of deciding where the study continues or not.
I'm suggesting the Sword of Damocles in a different sense.
And that's all your spare time to do interesting things, just about to get chopped off your life.
And perhaps most greatly you get no direct academic credit for it.
If you're there at the end, then you will get credit somewhat.
But no one will give you any credit. Really. No one will really thank you or hardly anyone sees the results of your labour.
But three or four people that you may have heard the definition of insanity,
which is doing the same thing over and over and expecting different results.
But it is what you did in the DMCA. It is quite ironic, I think, but I move on to further analysis and some studies I've taken it.
No one's particularly interested in the screws because there are different types of screws and it's very important to have different types of screws.
But there is a reason why some studies or some studies on further analysis or further analysis of the symbol fireflies.
Most of us are not that interested in the types of fireflies.
And so I wanted to give you a quick list of five things I would say I've learned to watch out for.
One is the observational comparison. A The randomised controlled trial,
the main of investigators have come to me later after they've seen the main results and
basically talk their way around saying could you not just do the observational comparison?
That is incredible. There is also the there is a positive study and they're waiting for discovery to be as applicable.
False, positive or false negative discovery.
That's this case of well, we'll just keep analysing it in various ways and eventually we'll get the results we want.
There's also the I have data, please make me a paper or an X people to turn off.
The study was done that they associated with it.
They hadn't really thought that what they were just going who for imaging data or something like that and then you're supposed to progress
the study in your spare time because no one ever gives you time to do the completely unrelated substudy I think is a very dangerous one.
And then the ill judged a fairly new one to me recently.
Came across a study for the BMC member where to see how felt it was a good idea of the two Ph.D. PhDs were closely tied to the progress of the trial.
Because we all know trials never, ever needed extensions.
The recruitment periods are not always, always on time.
And therefore we were in this awkward position of stations failing to get access to the data very well to start is still ongoing because they said,
well I needed to cement for my my PhD in very idea.
Which leads me on to data requests. And this person up here, that's how it feels like as the or system.
You just pick things for the various spokes and eventually that thankless task.
And also you spend a lot of time clearing up after the people and the trial.
It can never end. Sometimes it can get in the way. It's not it's not the ones you have to live with, thankfully.
But other people have a bright idea or layer that they want the data for something else to get long term.
Both the study time comes back to life and then there's more work involved.
Meta Analysis. If I got a request last year for 35 years after the study was published for a really evaluation of one of the outcomes.
So that takes quite a bit of time to remember. Well, how come that we're talking about even more study in our time?
I find though get access to the data.
I've moved cities by that point and then to read Coda and then produce the difference in which case you send it to them.
They don't apply, which made it real for you feel really worthwhile.
But I think that it did include an analysis of more systematic review and then you might get the last one, the next trial.
If you do a good job or a half decent job, people will come to you. Their night is always electronic.
They could see eyes as an extra and you might get rushed into doing things to help along
with some device that can be a good opportunity or it can be quite burdensome burden,
some thing that's not on your desk and so you get pulled in lots of different directions.
I put all these in red because everyone wants it.
It's all over here. And this all happened to me in the last couple of months and it's all different.
So different things going on and you can feel a bit overwhelmed because no matter how well you plan,
different culture will be working on different trials and they will have their own timescales on agendas you can't control.
So there are moments where you feel a bit like this guy up here, the ugly.
But I move towards the end. Probably the most uncomfortable thing is the bearer of bad news.
The classic one is the sample size and it is much bigger than you expect.
Akin to 60 isn't really that many or variations on that thing.
The trial result is negative, so despondency you can create in people as they see the result, which is quite absurd.
And then just this could be people's plan over the next five years work to the premise of this intervention that
if you spend the last five years working on and then you're showing that it doesn't seem to actually work,
and you also spend a lot of time saying, no, no,
we can just change the analysis because you don't like the result of about one third of and recently said we didn't want to be part of study.
You didn't realise the result could show that something didn't work everywhere.
So no one what seems to make sense to you is statistically a bad idea.
We do spend quite a while unfortunately explaining what seems quite intuitive actually isn't a good idea.
You can analyse the data halfway through because you're bored keen to go to conference one Christmas to come only variations on a theme.
But you do get people who feel the company could be not just send an outsider.
Can we not just give us. I know it's taking a long time, so it could be not.
And then probably the worst thing is that, Houston, we have a problem because of your role.
They can they monitoring emails or other things. You see data where you observe the randomisation.
You're the person whose fault the programmers made an error in the randomisation code.
Therefore the randomisation has not been working properly or you're the person response when
someone asks you to analyse something in the hopes that the data doesn't exist because
nobody actually remember to put it in the CRF or you're the person who worked out the
one point we were asking about Saturdays and every time we're asking for three days,
why would we choose to talk about sororities and Fridays?
But it's the same study. Surely we just want to pick one day.
So you unfortunately are the person who finds these things often and then has to go to people and explain to them.
And it's almost like you've created this problem where really you're just the person who finds it,
but then you're also left trying to resolve it as best can be done and can.
Then I think my last one is ignorance.
These are bandits, by the way. This is like you are confused.
This comes in various forms. This is my great moment to some reviewers.
You spend all this time doing all this work and then you get little statements
where people just basically scrap your whole study and make it all the rubbish
and like a couple of sentences which don't even make that much sense in my
opinion or I was recently as part of a group were accused of confirmation bias.
Who knows what confirmation bias is the one one or two people.
Well, I was guilty of anybody, which was news to me or we were as a group because the person wrote a nice,
eloquent statement about how they'd been so clever to in about two sentences in the whole paper.
But we can go to confirmation bias. And so again,
they just discarded the whole study on the basis two sentences which could just be rewritten if they really had an objection to two sentences.
So it can be very frustrating. I think it's a tough decision by the skill and effort process.
It's time to produce something that's rigorous and has produced data that people can share and get out if you really don't like what you've done.
And yet people will still just describe it fairly simply.
And then I was also in a group that was accused of academic dissembling.
Dissembling is also not a word I was familiar with, but let me suggest that's not a nice thing to be accused of.
And that, again, related to how we were dealing with adverse events.
So it was only a small component of a report,
but nevertheless we were keen for the academic dissembling someone friend to reporters, to the front door of the university.
You do get co-operators in randomised control because people want to be involved who have a remarkable level of ignorance.
I have heard surgeons again, I'm always using surgeons, the other people around us, but I didn't realise it being a randomised controlled trial then.
I didn't choose the treatment that I've ever done when I was there.
People talk about other people who are way ahead of everyone else, including blah blah people.
I'm just making this not clear who is on the statistics for this person who is way ahead of everyone else.
I have never done a trial in their life, but they were a mathematician, so therefore they must know everything by them.
I'll never side as a reviewer and you can make investigators very unhappy.
And I've had one very eminent photo op and basically try and pressurise me in writing a letter to retract my review because he didn't like it.
So good thing think it was very important to. But the bad thing is because the law is at stake sometimes.
I'm not saying this is a normal you can have a lot of pressure put on that special and
it comes out to the ignorance of the people who are people who should know better,
but they do quite things. So to finish some more period, I like trials.
I'm glad it worked because it was opportunistic, but I got involved in trials.
I would encourage you to work in trials in whatever way works for you.
And I would say there are glimmer of opportunities.
There is opportunities to get publications. In fact, quick wins. I was very lucky.
My first trial we got involved and end up getting published in the BMJ, but also which was very nice for me.
I didn't have to be there all the way through it. I just turned up and somehow they didn't have a statistician at the very end.
I needed one.
I got involved early on, so a friend of mine, six months after being champion, wanted me to get things out of the trials the first happened,
but also you do get to be involved in protocol publications and the results that are sob stories, good studies and all that.
And so it's a good way to build up your CV and just get involved in research.
There are opportunities involved in large grants. Then hundreds of thousands and even millions and projects are not uncommon.
There aren't many of them. So that's true of and also that you could be involved in multiple studies.
There are opportunities to specialise, although it's a very big area and a lot of statisticians,
there's never enough from everything to know about this.
If you know a bit you know a lot more than a lot of these collaborators, as I've just demonstrated to you on this very early on,
an opportunity for you to specialise and become an expert in a soft type of study,
whether that's cost of college or whether it's primary care trials or whether it's cost of primary care time as well have about.
Of adopted designs or whatever is very easy to come up with a scenario where you can
specialise and have some expertise that very few other people will be that comfortable with.
No one will do every type of trial and service career opportunities and you do get opportunities to do metallurgical research alongside.
It does raise operational issues as you go along. So well respected there.
If people are interested, if you can make a small difference in clinical trials because there's so many of them going on,
you can make a small difference that's important. So people will publish and people will be interested.
So if you can find a little way about doing trials, something making slightly better people will care.
And I'd say you have an opportunity to make a difference, browse through and have make an impact on a patient's health.
So my final thoughts. If your question is to be or not to be a trial statistician, I don't know.
The answer is because it's very much a personal thing. I'm very quiet.
I how to come on. I still work in there. This is how I spend my days.
But you have to answer a lot of questions in your cell. But I do quite like this, for there's never a surefire cure, really, except during good work.
My view is that doing trials is good work, but it starts with.