Welcome to the Clinician Researcher podcast, where academic clinicians learn the skills
to build their own research program, whether or not they have a mentor.
As clinicians, we spend a decade or more as trainees learning to take care of patients.
When we finally start our careers, we want to build research programs, but then we find
that our years of clinical training did not adequately prepare us to lead our research
program.
Through no fault of our own, we struggle to find mentors, and when we can't, we quit.
However, clinicians hold the keys to the greatest research breakthroughs.
For this reason, the Clinician Researcher podcast exists to give academic clinicians
the tools to build their own research program, whether or not they have a mentor.
Now introducing your host, Toyosi Onwuemene.
Welcome to the Clinician Researcher podcast.
I'm your host, Toyosi Onwuemene.
It is such a pleasure to be with you today.
Thank you for listening and thank you for being here because today's an especially special
episode.
We have a superstar biostatistician here with us, Dr. Maggie Kuchibhatla.
I'm going to allow her to introduce herself in a minute, but I'm so pumped because as
clinician researchers, many times we say we have to work with biostatisticians, but we
don't even know the first thing to do.
And Dr. Cotubatla is going to help to kind of demystify some of that process.
So without further ado, I'm going to invite her to introduce herself.
Maggie, welcome to the show.
Thank you, Tracey.
I don't know about superstar, but I love counting numbers.
I lived that life this year, 30 years at Duke, so, I can't complain.
I love it.
As a statistician, it's a pleasure to help anybody, any researcher with all kinds of
experiences come to the door.
But the one thing that is always helpful and will not be very upsetting for the investigators
would be to have a basic training in research methodology.
So there are a number of places where a young investigator can go get that kind of training.
I mean, for example, one of the easiest places to go is the local universities offer just
a short-term course, like a week-long course.
And NIH offers a summer course on quantitative methods for all the investigators.
And Johns Hopkins has a course.
Even I, as an investigator, for some of the epidemiological stuff, I've been to Johns
Hopkins, where they have a summer program.
I think NIH has provided them with some funding, and they have offered those courses for anybody
from across the country.
And it's a small thing.
And the institutions from where you work from can provide you a little bit of money to go
and get that training.
So I'll now leave it to you now to ask any questions, Toyosi.
Sure.
Maggie, I just want to say thank you for just starting with that.
And something that's so important, and I know we've talked about this before, is that as
clinicians, we get little to no research training.
Yes, we do a little bit of research here and there, but we're not leading the projects.
We're not deciding what's the primary outcome, what's the secondary outcome.
And so you're right.
When we get to our faculty positions and we're like, I now want to do research, and we're
like, well, you've got to talk to the biostatistician.
Sometimes we don't have a clue.
And so I appreciate what you talk about, the importance of getting a little bit of an education,
no matter how small it is, and research methods, so that you can have a conversation.
In a sense, it's just, how can we even start talking when we're not speaking the same language?
And so thank you for sharing that and how important it is for clinicians to get educated
so that they can really contribute to the research conversation.
So the first question I want to ask you, if you don't mind, is can you talk to me about
what is one thing about your role that every clinician researcher should know?
We hear about biostatisticians.
People are always talking about biostatisticians, but what should we know about you that we
may not know?
Well, the one thing that the investigator should know before coming to me is know a
little bit about what their aims are, what their goals are.
But even before they know their aims and goals, they've got to be working on those aspects
before coming to me.
So in that sense, they have to know, for example, what is their study design?
What is it that they, how are they going to answer the questions?
Are they just going to go collect the data that is there in the literature, or in which
case it's just a systematic study?
Or are they going to answer a question by doing a clinical trial?
So they need to know the study design.
So the question is, what kind of study design are they talking about?
Is that a clinical trial, or is it a retrospective study, or a prospective study, or an observational
study, or is that basic science study where you're doing experiments in animals?
Or are they doing, collecting data from running a lot of analyses from the blood?
In other words, it's the omics, proteomics, metabolomics, genetics.
So it's all the blood, all the data that you're getting from the blood.
Now you can have a study where it's a combination of long-term outcomes or long-term outcomes,
short-term outcomes, as well as all these blood work data that you're having.
So it's a combination of all that.
So to know, even to know what you want, you have to know what a study design is.
So the place to go get that is, you can come to us.
We can give you a five-minute list bill, or we can give you a lot of articles to read.
But a good place to start would be to get a short course, because before you're coming
to the statistician, you already know what you want, what questions to be answered.
But how to design the study comes from going to a short course and getting to know what
are all the study designs that are available out there.
How do you ask a question?
What is your outcome?
And what else can you ask?
What is your, what are the groups that you're comparing?
Are you comparing just one group?
Are you comparing several groups?
Then what is your sample size?
Do you have a lot of money from which you can collect the data to answer your questions?
Or is it going to be a very small study?
So you need to know what your sample study size is going to be and things like that.
And then the kind of data that you're collecting, is that quantitative?
Is that qualitative?
Or is that ordinal?
These are all basic stuff.
So it doesn't need, for some statistics can be scary.
It's not.
It's, it can be dumbed down to one, two, three.
Is that a quantitative, continuous one?
Is that ordinal?
Or is that a discrete one?
So they're all, all these can be described to anybody.
These days, even the 10th graders are doing statistics.
They're doing AP statistics.
When my daughter was going to do AP statistics, knowing her background and what she's interested
in, I said, don't do AP statistics yet.
Do statistics before you go to AP statistics.
So stats are being offered at all levels.
And I get calls from all the local schools asking, Karen, can a student just follow you
or just to know what kind of work that you do?
Because the person wants you to, the kid wants you to research, but they don't know where
to start.
So there are many ways to know, offer statistics.
And it starts with high school, for example.
And then if somebody is already in college and is a doctor or trained to be a doctor,
the colleges or the universities and the medical centers offer a lot of training to do research.
So that's the place to start.
Awesome.
Thank you, Maggie.
You know, one of the things I hear you saying is that you've got to know what you want.
Your statistician is not the one to tell you what you want.
You got to figure it out.
What kind of study do you, what kind of study design, how much data do you already have?
Or what data are you collecting?
Do you have money to collect the data prospectively?
Is this retrospective?
You've got to have a plan.
And it sounds like that's where you're able to help the most.
When somebody comes to you with a plan, they know what their primary outcome is.
They know what they're looking for, what they're really, what questions they're seeking to
answer from what data.
And so it sounds like I'm hearing you say, you got to be prepared for these conversations.
Is that fair to say?
Yeah, it is.
It is.
And they turn around, they turn around to do research in a short while when they come
with questions and they've already, they already have their groundwork done.
We can start, though, we can start holding hands now, yes, from ground zero, but then
it will take a longer time to study, to have, to recent goal.
Sure.
It's good to have some kind of training.
And these days, you know, all the medical schools have for one year of research or six
months of research.
And if that's the path one wants to take, take, use that opportunity to be, to do the
research or go to the Institute or go to a medical school where you want to just try
that out.
You could try that out and then get a student to say, no, this is not what you want to do.
You want to be doing clinical, clinical work all the time.
That's fine.
But at least you've bettered your feed by knowing what you need to know to do research.
Awesome.
Thank you, Maggie.
Now, let me ask you a question.
And this is not a question that I thought I would ask you.
It just comes to my mind.
What about the people who say, well, I've had statistics one and two.
I don't need a biostatistician.
What do you say to those people?
So number crunching is different from designing the study.
And one, just let me, let me take a step further behind actually, before you come to a number
crunching.
So the science has to be solid.
One that the science has to be solid, but to get data from that scientific question
that you have, you need to know how to collect the data.
So if you know, if you're not a statistic, statistics is number crunching.
It cannot be just number crunching.
It can be how to collect the data.
You can go wrong in many places by not designing the right study.
So involve a statistician early on.
Engage a statistician early on.
People like me come free.
In some institutes, you've got to pay, but places like Duke, you have a lot of resources,
a lot of places to go to now.
I mean, I agree that 10 years ago, things were different, but 10 years now, there are
a lot of places where you can go.
There are a lot of training grants that offer you places where you can learn how to do research.
But even there, it's good to know where to start.
You already know some things, some basic things about how to do research.
Awesome.
Thank you, Maggie.
So Maggie, what I'm hearing from you is that you don't just finish all the data collection,
finish your study, and then go find a statistician and say, here, crunch my numbers for me.
What you're saying is that a biostatistician is a partner in the research process.
And from the very beginning, where you're even thinking about, how do I design the study
to answer this question, whether it be retrospective data, prospective, how do I design the study
to answer the question?
I'm hearing you say a biostatistician should be a partner with you in figuring out how
to design the study, how to collect the data, and then how to analyze the data.
Yes.
Analyze the data.
And the data that you're collecting also is going to feed into your next set of research.
So you need to have a long-term goal.
So in that sense, have a long-term relationship with your statistician that you're working.
They're like tools.
They're somebody who understands your research.
And it's a training both ways.
I don't know everything about science.
So I value science.
So I'm getting all the knowledge I can from the primary investigators like you.
And I use that knowledge to see, okay, now this is what TOEIC wants.
This is how I need to design a study.
And what are the best ways to collect the data?
And what are the efficient ways to collect the data?
And if the part of money is large, then we go a certain route.
If the part of money is small and we have a limited time, then we just go collect immediate
data or immediate gratification.
So sometimes the grant is down the corner and we don't have enough time to go collect
the data, long-term data.
But you can just go hone in to what you've already done and answer some questions.
A grant based on that, turn that into a proposal and turn that out.
I love it.
So Maggie, one of the things I'm also hearing, and you said this earlier when you talked
about you want to do good science.
You're not just gathering data together to just say, oh, I said something.
You really do want to do good science.
You want to answer a question correctly.
And so it's important to get it right from the beginning.
Get someone who is partnering with you so that at the end of the day, your science is
high quality that will actually be a contribution to the field.
So I'm hearing you talk about just in improving the quality, you're involving a statistician
early.
And it also helps because if you don't have enough resources to answer all the questions
you want to, a statistician can help you focus and say, okay, well, this is the amount of
data this can help you get.
And this is what it will get you to the next step and the next step and the next step after
that.
So I'm hearing you talk about longevity too.
This is not just about one project and you're done.
This is really about answering a series of questions and your statistician partnering
with you to help you do that.
Yes.
So when you write a grant, you also at the end of the grant, you also have to make a
statement on what are the future uses of this grant and where are you going to go as a
researcher at the end of this grant?
Okay, you're giving the institution like NIH is giving us the money.
They like the proposal point.
But if you don't have a plan on what you want to do using the data and using the reserves
from the money that they're funding you with, they're not going to like it.
So you're going to come back and say, okay, once we have all this data and all these things
answered, what are all the paths that you're going to take after you've collected and analyzed
and published your data?
And next set of goals, you want to have that.
So if you don't plan the study, this plan, the study, right?
So you, there's a chain reaction of things that you don't do right.
Thank you for sharing that, Maggie.
So you mentioned grants.
And so can I ask you about that?
In what way can a biostatistician be helpful to an investigator who's writing a grant?
At what point do we get you involved and how do you help us in writing grants?
So once again, I cannot emphasize the importance of science.
So you come with the solid science, you found evidence of this.
Now you want to design a study to answer that, to answer that in some form or in a larger
form, or it's a, it's a conglomeration of lots of variables that are going there.
So you want to answer all those questions.
So now that means again, back to, back to the drawing board, you're going to be needing
to design the study.
So my question would be, when, when do you want to start the study?
So if they say that they want to start the study tomorrow, my question is, what is it
that you have now?
So if they already have the data now and they want to write a grant, my question would be,
well, once you have some data and you already know that some of your questions are answered,
but you want some more to be answered, involve the statistician early on in when you have
your aims, your goals and your future questions to be answered.
When you have all those things written up and you've formed it up, formed it up to some
extent, come see a statistician because then the, the principal investigators and the statisticians
can sit together, put their minds together and design, come up with a design that's best
for what your aims are and what data you have and what data can be collected for the current
study that you have in mind.
Awesome.
And I keep hearing you talk about the importance of that partnership.
So have a plan, come up with your specific aims, and then let's sit together and design
what that study will look like to fit the goals of your study.
Yeah.
Okay.
So now, Maggie, let me ask you this.
You know, you've kind of answered the question about how can clinicians come prepared to
get value from you?
You've talked about get a little bit of an education so we can have a conversation and
then have a clear goal for where you want to go.
I just wonder, is there anything else you want to add to what, how can clinicians best
get value from the experience of working with a biostatistician?
So, so if they're, if they're, if they're from day one, let's say day one, they have
nothing but they have an idea and they want to do something, come to us because we can
design the study at that time and tell you what, what to do several things.
One, to go, we will tell the investigators to go and look into the literature and come
up with it for the questions that they want.
Is there anything in the literature that they have already done?
That are there researchers who've already done that kind of research?
If they have, what are all the results they have?
So that, you know, get an Excel spreadsheet.
I will tell the investigator to get an Excel spreadsheet.
This is the question.
These are all the, these are all the papers that are out there.
In each of these papers, what is it that the, that that particular investigator has looked
at and what is it that I'm going to contribute that they have not contributed to?
Because nobody's going to give us money if something is already looked at several times
and the results and the same results are coming up over and over again.
So you have to, you have to come up with something that is kind of novel, over and above that
is already studied in the literature.
So if, if a part of that particular question that you have in mind, most of it is answered,
but some of it is not answered, then we can help you design a study in addition to what
is already there, how you can either add to what is already there by adding a new design
and going and collecting data for that particular aspect of that aim that you have.
And then move from there to the next phase.
So what I also hear you talking about, Maggie, is that you bring the innovation too.
So you can help people think about how to take what's already present and make it new.
And so in, you know, for clinicians who are trying to write an innovation section of their
grant, you actually can help with that and helping them innovate.
Yes, because innovation is science.
Innovation is also a new statistical methods.
So some of the questions could not be answered, you know, in a very sophisticated way earlier
on from 20 years ago.
But now with the advent of computing power, immense computing powers, especially since
the last 10 years, the immense computing power, the computing, it's not too expensive to run
big numbers, to run big, big models.
Ten years ago, it would take two nights to run a study.
Or we used to, we have to go to a supercomputing center to run some models.
But now with the advent of computers and the cheapness and how cheap they are to run some
of these models, in terms of time, we can advocate newer methodologies that will incorporate
lots and lots of variables from different models to come up with a very complicated
model.
I like it.
Thank you.
Innovation in the statistical methods, which is great.
It's not something I think about, but that's absolutely necessary.
All right.
That's awesome.
Okay.
Let me ask you this.
How, what, what is one life hack that you can share that maybe clinicians don't know
about as what is one life hack that you have to share?
Well, our hearts are very important to us and heart, you know, heart stops, feet all
gone.
So a few years ago, one of the investigators here came to me and said, Hey, we did this
research in our, in our lab and they found this one more car to be very high in these
patients who are going to, whose heart is going to fail.
How do we design a study?
How do we, how, what do we do next?
So we, what we, we want to, we want to say that we have this four hearts out of these
four cars, three hearts had this one marker very high, but this is not enough to get money.
And we will start a design study.
We just put the heart in the solution and we try to see what came out of the, from these
hearts and we tested the solution that was there.
The heart was put in a solution when we looked at the solution and we found this marker in
all three of these cards.
So I said, how, you know, my studies are cheap.
You know, we cannot kill people.
We only can get the hearts from people who die.
So why don't we take the hearts of mice or if you have more money or pigs or rabbits,
any of these.
And we design a study where we put a stressor in the hearts.
So they're going to die or they're going to be near that near death and see what comes
out of their hearts because our animal models eventually do translate into human models.
Right.
It was some for some they are not, but for most part they're, they're pretty, pretty
close.
We are all pretty close.
So why don't you, why don't we do that in the next?
I said, how long will it take?
The grant is due.
This is Brookhaven.
Brookhaven Institute, they were the, the grant is due in like six months.
Can you do a trial?
Can you do studies in the next two to three months?
He said in three months we can get the result that you want.
So they came up, we came up with some, some estimates using mice as an example.
And it translated what we saw in the human, human anecdotal data.
These are anecdotal data.
We use that.
And then we said, okay, mice models are cheaper to run.
So let's see if we can, if we do the same thing in mice, what happens?
So we initially we did three mice and we found some good results.
We then moved on to a slightly larger samples, six mice, and we, the results were consistent
with what we saw in the human hearts.
And we had two cycles of grants that got funded based on those small studies that we designed
in animals and then we went on to get some more grants.
That's really awesome.
So Maggie, I hear you, you keep reiterating the importance of getting your partners by
statisticians involved early because they can help you think about how do you set up
to be ready to submit a grant, to be ready to be successful in grant funding.
But I'm also hearing you, hearing you talk about how much time is needed.
So you were talking about investigators coming to you six months before a grant is due.
Talk about how much time is needed to really prepare a good submission with the help of
the statistician.
So basic science is a different beast.
So sure, this study we were able to do very quickly and there were a lot of resources.
There was a big name, so the investigators have a lot of money and a lot of resources,
so we were able to turn around and do the work and get the data to have to submit to
the grant.
But it's not always the case.
The mice studies, all the mice can die for various reasons.
And so we may not have solid data.
So I would say, come even a year ahead.
If you have some data, then just anecdotally, let's say you found some data anecdotally,
then come to us right away and see how we can set up a study that we can systematically
collect the data and write a grant on that.
Because it's not just one question that you're going to answer from designing the study.
You're going to be answering three or four questions.
The aims typically are...
So an R21 mechanism kind of helps you get data, collect data to write a big old grant.
So it's basically R21.
R03 is basically an R-age mechanism to really collect pilot data and then use that data
then to do kind of an R21 and then move on to a bigger one.
But in the medical schools, there are all these training grants that give you pilot
money.
So some of these small grants, $10,000 grants, can help you set up small studies.
They may not fund your salary, but they can fund small studies, lab studies.
Those are one.
That is lab studies.
But if they're not lab studies, if they involve secondary data, secondary data is already
there.
So this money can be the small study funding from pilot studies internally can be used
on writing up grants, small grants, and also come up with some other decent studies that
can go with that.
So it doesn't have to ask just one question.
You'll have three or four questions that can go with as part of the project.
I love it.
So I hear you talking about how you can even help people really maximize the benefit of
any sample of data.
So it's like here you're collecting this data.
This is what you can get from it.
You can also get this.
You can also get that.
And I also hearing you talk about for everyone to realize that no matter how small the pot
of money you get, you can always do something with it to turn it into the next grant and
the next grant and the next grant after that.
That's awesome.
Awesome.
All right.
Even big data sets, even big data sets, we could do some small studies using the big
national databases, using the internal funding and use that to write bigger grants.
I love it.
Thank you.
Okay.
So Maggie, if there is a clinician sitting out there thinking, I want to become a researcher,
I want to work with a biostatistician, but I'm not sure I can, what encouragement do
you have for them in terms of how best to move forward?
Yeah.
So nationally, NIH has started providing mechanisms for quantitative, to provide quantitative
help to the institutes.
So the CTSI is one of, one such grant that many organizations or many academic institutions
write to get money, so that CTSI, it's like a, it's a core that helps investigators within
the institute with all kinds of help they need.
So Duke got two rounds of CTSI grants and as part of that, some of that money is given
to the statisticians.
So a 20 or 25% of a statistician's salary is covered by the CTSI.
So those statisticians, what they do is now help investigators who are starting from ground
zero.
And they can tap into those aspects of any institute that they have.
So practically every institute has some kind of money.
It's the question of how much money that you want or how much effort that you want from
a statistician.
So if an investigator, if a new investigator is getting through the door, we have that
all the time.
We have lots of, lots and lots of investigators coming into our institute.
And the first thing they want to know is what are the research resources available?
So call up, so the thing to do is to call the Biostat department and find out what are
all the resources available.
Or within an institute, for example, Division of Hematology, for example, within the Division
of Hematology, find out who are the, what are all the resources available to do research.
Both basic science, long-term outcomes, outcomes research, what are all the resources available.
So that's the starting point.
If within the department or within the department that you're in, what are the resources available?
And there are multiple resources available.
That's not the only resource available.
If the department tells you that this resource is available only for people who have funding,
then at the School of Medicine level, there are resources available that are available
for any researcher who can start from ground down.
That's awesome.
Thank you, Maggie.
What I'm hearing from you is just that you got to keep pushing for what you need.
Make sure you're looking, especially as you're applying for your first faculty job, making
sure that these resources are already available, how you're going to get access to them.
But even if you come and you don't have access to the resource to get a biostatistician to
work with, look to your department, look to the schools, look to the institutes, especially
if you have a CTSI, and look to see what resources are available.
And I love that because I think one of the things, Maggie, we tell our audience is that
you can't, don't get stuck.
Don't make sure that you're taking ownership and leading your own research, not letting
obstacles stop you.
And so it sounds like really it's that there are resources available, but you do need to
go out and look for them.
Yeah, yeah.
That's right.
I do want to mention one thing though.
So every department, if it's a research institution, every department has a vice chair of research.
Go talk to the vice chair of research and tell them you are interested.
You did some research in college or in high school, college, and in mid school that you
want to pursue some of that research.
How do I go about doing that?
Talk to the vice chair of research and the vice chair of research will be able to help
you put you in touch with somebody who's already doing something along those lines.
So you have a mentoring right there.
And so you get to start working with that particular research with somebody who's already
doing that kind of work.
Or you can start your own search because the department is interested in something that
you just put up with.
I love it.
Thank you, Maggie.
I hear you saying, you know what, there's someone at your institution who cares that
research gets done.
So find them and have them help you because that is their job.
That is so awesome.
Maggie, you have shared such amazing insights.
Thank you so much.
It is rare that we really have access to biostatisticians to help us get the inside story on how to
work well with the biostatistician.
And so I really want to thank you for the insights you've shared today.
And to our audience members, if you have benefited from the things that Maggie has shared, please
share this episode with somebody else who needs to hear it.
Or if you're a mentor and your mentees need to understand this, please share this episode
with them.
So having said that, I want to say Maggie, thank you so much for coming on the show and
sharing your wisdom with our audience.
Thank you, Tiosi.
Thank you for giving me this opportunity.
And so thank you to our audience and we'll see you again next time.
Take care.
Bye.
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