Getting the most from your biostatistician with Dr. Maggie Kuchibhatla

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Welcome to the Clinician Researcher podcast, where academic clinicians learn the skills

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to build their own research program, whether or not they have a mentor.

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As clinicians, we spend a decade or more as trainees learning to take care of patients.

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When we finally start our careers, we want to build research programs, but then we find

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that our years of clinical training did not adequately prepare us to lead our research

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program.

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Through no fault of our own, we struggle to find mentors, and when we can't, we quit.

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However, clinicians hold the keys to the greatest research breakthroughs.

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For this reason, the Clinician Researcher podcast exists to give academic clinicians

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the tools to build their own research program, whether or not they have a mentor.

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Now introducing your host, Toyosi Onwuemene.

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Welcome to the Clinician Researcher podcast.

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I'm your host, Toyosi Onwuemene.

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It is such a pleasure to be with you today.

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Thank you for listening and thank you for being here because today's an especially special

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episode.

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We have a superstar biostatistician here with us, Dr. Maggie Kuchibhatla.

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I'm going to allow her to introduce herself in a minute, but I'm so pumped because as

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clinician researchers, many times we say we have to work with biostatisticians, but we

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don't even know the first thing to do.

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And Dr. Cotubatla is going to help to kind of demystify some of that process.

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So without further ado, I'm going to invite her to introduce herself.

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Maggie, welcome to the show.

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Thank you, Tracey.

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I don't know about superstar, but I love counting numbers.

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I lived that life this year, 30 years at Duke, so, I can't complain.

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I love it.

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As a statistician, it's a pleasure to help anybody, any researcher with all kinds of

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experiences come to the door.

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But the one thing that is always helpful and will not be very upsetting for the investigators

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would be to have a basic training in research methodology.

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So there are a number of places where a young investigator can go get that kind of training.

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I mean, for example, one of the easiest places to go is the local universities offer just

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a short-term course, like a week-long course.

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And NIH offers a summer course on quantitative methods for all the investigators.

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And Johns Hopkins has a course.

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Even I, as an investigator, for some of the epidemiological stuff, I've been to Johns

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Hopkins, where they have a summer program.

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I think NIH has provided them with some funding, and they have offered those courses for anybody

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from across the country.

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And it's a small thing.

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And the institutions from where you work from can provide you a little bit of money to go

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and get that training.

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So I'll now leave it to you now to ask any questions, Toyosi.

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Sure.

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Maggie, I just want to say thank you for just starting with that.

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And something that's so important, and I know we've talked about this before, is that as

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clinicians, we get little to no research training.

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Yes, we do a little bit of research here and there, but we're not leading the projects.

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We're not deciding what's the primary outcome, what's the secondary outcome.

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And so you're right.

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When we get to our faculty positions and we're like, I now want to do research, and we're

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like, well, you've got to talk to the biostatistician.

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Sometimes we don't have a clue.

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And so I appreciate what you talk about, the importance of getting a little bit of an education,

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no matter how small it is, and research methods, so that you can have a conversation.

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In a sense, it's just, how can we even start talking when we're not speaking the same language?

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And so thank you for sharing that and how important it is for clinicians to get educated

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so that they can really contribute to the research conversation.

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So the first question I want to ask you, if you don't mind, is can you talk to me about

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what is one thing about your role that every clinician researcher should know?

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We hear about biostatisticians.

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People are always talking about biostatisticians, but what should we know about you that we

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may not know?

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Well, the one thing that the investigator should know before coming to me is know a

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little bit about what their aims are, what their goals are.

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But even before they know their aims and goals, they've got to be working on those aspects

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before coming to me.

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So in that sense, they have to know, for example, what is their study design?

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What is it that they, how are they going to answer the questions?

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Are they just going to go collect the data that is there in the literature, or in which

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case it's just a systematic study?

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Or are they going to answer a question by doing a clinical trial?

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So they need to know the study design.

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So the question is, what kind of study design are they talking about?

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Is that a clinical trial, or is it a retrospective study, or a prospective study, or an observational

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study, or is that basic science study where you're doing experiments in animals?

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Or are they doing, collecting data from running a lot of analyses from the blood?

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In other words, it's the omics, proteomics, metabolomics, genetics.

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So it's all the blood, all the data that you're getting from the blood.

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Now you can have a study where it's a combination of long-term outcomes or long-term outcomes,

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short-term outcomes, as well as all these blood work data that you're having.

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So it's a combination of all that.

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So to know, even to know what you want, you have to know what a study design is.

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So the place to go get that is, you can come to us.

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We can give you a five-minute list bill, or we can give you a lot of articles to read.

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But a good place to start would be to get a short course, because before you're coming

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to the statistician, you already know what you want, what questions to be answered.

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But how to design the study comes from going to a short course and getting to know what

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are all the study designs that are available out there.

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How do you ask a question?

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What is your outcome?

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And what else can you ask?

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What is your, what are the groups that you're comparing?

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Are you comparing just one group?

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Are you comparing several groups?

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Then what is your sample size?

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Do you have a lot of money from which you can collect the data to answer your questions?

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Or is it going to be a very small study?

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So you need to know what your sample study size is going to be and things like that.

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And then the kind of data that you're collecting, is that quantitative?

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Is that qualitative?

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Or is that ordinal?

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These are all basic stuff.

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So it doesn't need, for some statistics can be scary.

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It's not.

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It's, it can be dumbed down to one, two, three.

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Is that a quantitative, continuous one?

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Is that ordinal?

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Or is that a discrete one?

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So they're all, all these can be described to anybody.

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These days, even the 10th graders are doing statistics.

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They're doing AP statistics.

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When my daughter was going to do AP statistics, knowing her background and what she's interested

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in, I said, don't do AP statistics yet.

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Do statistics before you go to AP statistics.

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So stats are being offered at all levels.

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And I get calls from all the local schools asking, Karen, can a student just follow you

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or just to know what kind of work that you do?

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Because the person wants you to, the kid wants you to research, but they don't know where

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to start.

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So there are many ways to know, offer statistics.

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And it starts with high school, for example.

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And then if somebody is already in college and is a doctor or trained to be a doctor,

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the colleges or the universities and the medical centers offer a lot of training to do research.

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So that's the place to start.

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Awesome.

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Thank you, Maggie.

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You know, one of the things I hear you saying is that you've got to know what you want.

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Your statistician is not the one to tell you what you want.

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You got to figure it out.

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What kind of study do you, what kind of study design, how much data do you already have?

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Or what data are you collecting?

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Do you have money to collect the data prospectively?

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Is this retrospective?

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You've got to have a plan.

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And it sounds like that's where you're able to help the most.

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When somebody comes to you with a plan, they know what their primary outcome is.

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They know what they're looking for, what they're really, what questions they're seeking to

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answer from what data.

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And so it sounds like I'm hearing you say, you got to be prepared for these conversations.

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Is that fair to say?

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Yeah, it is.

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It is.

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And they turn around, they turn around to do research in a short while when they come

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with questions and they've already, they already have their groundwork done.

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We can start, though, we can start holding hands now, yes, from ground zero, but then

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it will take a longer time to study, to have, to recent goal.

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Sure.

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It's good to have some kind of training.

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And these days, you know, all the medical schools have for one year of research or six

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months of research.

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And if that's the path one wants to take, take, use that opportunity to be, to do the

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research or go to the Institute or go to a medical school where you want to just try

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that out.

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You could try that out and then get a student to say, no, this is not what you want to do.

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You want to be doing clinical, clinical work all the time.

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That's fine.

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But at least you've bettered your feed by knowing what you need to know to do research.

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Awesome.

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Thank you, Maggie.

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Now, let me ask you a question.

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And this is not a question that I thought I would ask you.

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It just comes to my mind.

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What about the people who say, well, I've had statistics one and two.

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I don't need a biostatistician.

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What do you say to those people?

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So number crunching is different from designing the study.

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And one, just let me, let me take a step further behind actually, before you come to a number

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crunching.

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So the science has to be solid.

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One that the science has to be solid, but to get data from that scientific question

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that you have, you need to know how to collect the data.

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So if you know, if you're not a statistic, statistics is number crunching.

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It cannot be just number crunching.

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It can be how to collect the data.

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You can go wrong in many places by not designing the right study.

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So involve a statistician early on.

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Engage a statistician early on.

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People like me come free.

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In some institutes, you've got to pay, but places like Duke, you have a lot of resources,

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a lot of places to go to now.

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I mean, I agree that 10 years ago, things were different, but 10 years now, there are

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a lot of places where you can go.

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There are a lot of training grants that offer you places where you can learn how to do research.

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But even there, it's good to know where to start.

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You already know some things, some basic things about how to do research.

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Awesome.

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Thank you, Maggie.

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So Maggie, what I'm hearing from you is that you don't just finish all the data collection,

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finish your study, and then go find a statistician and say, here, crunch my numbers for me.

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What you're saying is that a biostatistician is a partner in the research process.

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And from the very beginning, where you're even thinking about, how do I design the study

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to answer this question, whether it be retrospective data, prospective, how do I design the study

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to answer the question?

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I'm hearing you say a biostatistician should be a partner with you in figuring out how

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to design the study, how to collect the data, and then how to analyze the data.

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Yes.

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Analyze the data.

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And the data that you're collecting also is going to feed into your next set of research.

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So you need to have a long-term goal.

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So in that sense, have a long-term relationship with your statistician that you're working.

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They're like tools.

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They're somebody who understands your research.

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And it's a training both ways.

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I don't know everything about science.

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So I value science.

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So I'm getting all the knowledge I can from the primary investigators like you.

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And I use that knowledge to see, okay, now this is what TOEIC wants.

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This is how I need to design a study.

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And what are the best ways to collect the data?

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And what are the efficient ways to collect the data?

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And if the part of money is large, then we go a certain route.

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If the part of money is small and we have a limited time, then we just go collect immediate

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data or immediate gratification.

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So sometimes the grant is down the corner and we don't have enough time to go collect

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the data, long-term data.

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But you can just go hone in to what you've already done and answer some questions.

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A grant based on that, turn that into a proposal and turn that out.

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I love it.

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So Maggie, one of the things I'm also hearing, and you said this earlier when you talked

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about you want to do good science.

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You're not just gathering data together to just say, oh, I said something.

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You really do want to do good science.

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You want to answer a question correctly.

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And so it's important to get it right from the beginning.

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Get someone who is partnering with you so that at the end of the day, your science is

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high quality that will actually be a contribution to the field.

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So I'm hearing you talk about just in improving the quality, you're involving a statistician

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early.

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And it also helps because if you don't have enough resources to answer all the questions

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you want to, a statistician can help you focus and say, okay, well, this is the amount of

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data this can help you get.

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And this is what it will get you to the next step and the next step and the next step after

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that.

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So I'm hearing you talk about longevity too.

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This is not just about one project and you're done.

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This is really about answering a series of questions and your statistician partnering

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with you to help you do that.

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Yes.

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So when you write a grant, you also at the end of the grant, you also have to make a

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statement on what are the future uses of this grant and where are you going to go as a

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researcher at the end of this grant?

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Okay, you're giving the institution like NIH is giving us the money.

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They like the proposal point.

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But if you don't have a plan on what you want to do using the data and using the reserves

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from the money that they're funding you with, they're not going to like it.

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So you're going to come back and say, okay, once we have all this data and all these things

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answered, what are all the paths that you're going to take after you've collected and analyzed

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and published your data?

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And next set of goals, you want to have that.

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So if you don't plan the study, this plan, the study, right?

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So you, there's a chain reaction of things that you don't do right.

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Thank you for sharing that, Maggie.

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So you mentioned grants.

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And so can I ask you about that?

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In what way can a biostatistician be helpful to an investigator who's writing a grant?

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At what point do we get you involved and how do you help us in writing grants?

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So once again, I cannot emphasize the importance of science.

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So you come with the solid science, you found evidence of this.

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Now you want to design a study to answer that, to answer that in some form or in a larger

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form, or it's a, it's a conglomeration of lots of variables that are going there.

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So you want to answer all those questions.

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So now that means again, back to, back to the drawing board, you're going to be needing

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to design the study.

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So my question would be, when, when do you want to start the study?

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So if they say that they want to start the study tomorrow, my question is, what is it

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that you have now?

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So if they already have the data now and they want to write a grant, my question would be,

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well, once you have some data and you already know that some of your questions are answered,

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but you want some more to be answered, involve the statistician early on in when you have

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your aims, your goals and your future questions to be answered.

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When you have all those things written up and you've formed it up, formed it up to some

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extent, come see a statistician because then the, the principal investigators and the statisticians

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can sit together, put their minds together and design, come up with a design that's best

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for what your aims are and what data you have and what data can be collected for the current

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study that you have in mind.

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Awesome.

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And I keep hearing you talk about the importance of that partnership.

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So have a plan, come up with your specific aims, and then let's sit together and design

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what that study will look like to fit the goals of your study.

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Yeah.

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Okay.

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So now, Maggie, let me ask you this.

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You know, you've kind of answered the question about how can clinicians come prepared to

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get value from you?

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You've talked about get a little bit of an education so we can have a conversation and

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then have a clear goal for where you want to go.

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I just wonder, is there anything else you want to add to what, how can clinicians best

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get value from the experience of working with a biostatistician?

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So, so if they're, if they're, if they're from day one, let's say day one, they have

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nothing but they have an idea and they want to do something, come to us because we can

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design the study at that time and tell you what, what to do several things.

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One, to go, we will tell the investigators to go and look into the literature and come

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up with it for the questions that they want.

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Is there anything in the literature that they have already done?

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That are there researchers who've already done that kind of research?

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If they have, what are all the results they have?

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So that, you know, get an Excel spreadsheet.

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I will tell the investigator to get an Excel spreadsheet.

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This is the question.

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These are all the, these are all the papers that are out there.

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In each of these papers, what is it that the, that that particular investigator has looked

304
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at and what is it that I'm going to contribute that they have not contributed to?

305
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Because nobody's going to give us money if something is already looked at several times

306
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and the results and the same results are coming up over and over again.

307
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So you have to, you have to come up with something that is kind of novel, over and above that

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is already studied in the literature.

309
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So if, if a part of that particular question that you have in mind, most of it is answered,

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but some of it is not answered, then we can help you design a study in addition to what

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is already there, how you can either add to what is already there by adding a new design

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and going and collecting data for that particular aspect of that aim that you have.

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And then move from there to the next phase.

314
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So what I also hear you talking about, Maggie, is that you bring the innovation too.

315
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So you can help people think about how to take what's already present and make it new.

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And so in, you know, for clinicians who are trying to write an innovation section of their

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grant, you actually can help with that and helping them innovate.

318
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Yes, because innovation is science.

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Innovation is also a new statistical methods.

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So some of the questions could not be answered, you know, in a very sophisticated way earlier

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on from 20 years ago.

322
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But now with the advent of computing power, immense computing powers, especially since

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the last 10 years, the immense computing power, the computing, it's not too expensive to run

324
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big numbers, to run big, big models.

325
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Ten years ago, it would take two nights to run a study.

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Or we used to, we have to go to a supercomputing center to run some models.

327
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But now with the advent of computers and the cheapness and how cheap they are to run some

328
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of these models, in terms of time, we can advocate newer methodologies that will incorporate

329
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lots and lots of variables from different models to come up with a very complicated

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model.

331
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I like it.

332
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Thank you.

333
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Innovation in the statistical methods, which is great.

334
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It's not something I think about, but that's absolutely necessary.

335
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All right.

336
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That's awesome.

337
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Okay.

338
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Let me ask you this.

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How, what, what is one life hack that you can share that maybe clinicians don't know

340
00:21:31,540 --> 00:21:35,660
about as what is one life hack that you have to share?

341
00:21:35,660 --> 00:21:43,700
Well, our hearts are very important to us and heart, you know, heart stops, feet all

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gone.

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So a few years ago, one of the investigators here came to me and said, Hey, we did this

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research in our, in our lab and they found this one more car to be very high in these

345
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patients who are going to, whose heart is going to fail.

346
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How do we design a study?

347
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How do we, how, what do we do next?

348
00:22:09,580 --> 00:22:13,540
So we, what we, we want to, we want to say that we have this four hearts out of these

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four cars, three hearts had this one marker very high, but this is not enough to get money.

350
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And we will start a design study.

351
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We just put the heart in the solution and we try to see what came out of the, from these

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hearts and we tested the solution that was there.

353
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The heart was put in a solution when we looked at the solution and we found this marker in

354
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all three of these cards.

355
00:22:38,540 --> 00:22:42,380
So I said, how, you know, my studies are cheap.

356
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You know, we cannot kill people.

357
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We only can get the hearts from people who die.

358
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So why don't we take the hearts of mice or if you have more money or pigs or rabbits,

359
00:22:58,300 --> 00:22:59,820
any of these.

360
00:22:59,820 --> 00:23:07,720
And we design a study where we put a stressor in the hearts.

361
00:23:07,720 --> 00:23:14,180
So they're going to die or they're going to be near that near death and see what comes

362
00:23:14,180 --> 00:23:21,860
out of their hearts because our animal models eventually do translate into human models.

363
00:23:21,860 --> 00:23:22,860
Right.

364
00:23:22,860 --> 00:23:26,340
It was some for some they are not, but for most part they're, they're pretty, pretty

365
00:23:26,340 --> 00:23:27,340
close.

366
00:23:27,340 --> 00:23:28,340
We are all pretty close.

367
00:23:28,340 --> 00:23:30,380
So why don't you, why don't we do that in the next?

368
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I said, how long will it take?

369
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The grant is due.

370
00:23:34,860 --> 00:23:35,860
This is Brookhaven.

371
00:23:35,860 --> 00:23:41,020
Brookhaven Institute, they were the, the grant is due in like six months.

372
00:23:41,020 --> 00:23:42,020
Can you do a trial?

373
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Can you do studies in the next two to three months?

374
00:23:43,820 --> 00:23:47,100
He said in three months we can get the result that you want.

375
00:23:47,100 --> 00:23:55,620
So they came up, we came up with some, some estimates using mice as an example.

376
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And it translated what we saw in the human, human anecdotal data.

377
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These are anecdotal data.

378
00:24:01,500 --> 00:24:02,500
We use that.

379
00:24:02,500 --> 00:24:05,780
And then we said, okay, mice models are cheaper to run.

380
00:24:05,780 --> 00:24:10,500
So let's see if we can, if we do the same thing in mice, what happens?

381
00:24:10,500 --> 00:24:14,580
So we initially we did three mice and we found some good results.

382
00:24:14,580 --> 00:24:20,260
We then moved on to a slightly larger samples, six mice, and we, the results were consistent

383
00:24:20,260 --> 00:24:23,940
with what we saw in the human hearts.

384
00:24:23,940 --> 00:24:31,820
And we had two cycles of grants that got funded based on those small studies that we designed

385
00:24:31,820 --> 00:24:37,140
in animals and then we went on to get some more grants.

386
00:24:37,140 --> 00:24:38,140
That's really awesome.

387
00:24:38,140 --> 00:24:41,900
So Maggie, I hear you, you keep reiterating the importance of getting your partners by

388
00:24:41,900 --> 00:24:46,140
statisticians involved early because they can help you think about how do you set up

389
00:24:46,140 --> 00:24:50,940
to be ready to submit a grant, to be ready to be successful in grant funding.

390
00:24:50,940 --> 00:24:54,260
But I'm also hearing you, hearing you talk about how much time is needed.

391
00:24:54,260 --> 00:24:59,380
So you were talking about investigators coming to you six months before a grant is due.

392
00:24:59,380 --> 00:25:04,260
Talk about how much time is needed to really prepare a good submission with the help of

393
00:25:04,260 --> 00:25:06,740
the statistician.

394
00:25:06,740 --> 00:25:09,420
So basic science is a different beast.

395
00:25:09,420 --> 00:25:15,540
So sure, this study we were able to do very quickly and there were a lot of resources.

396
00:25:15,540 --> 00:25:20,180
There was a big name, so the investigators have a lot of money and a lot of resources,

397
00:25:20,180 --> 00:25:25,940
so we were able to turn around and do the work and get the data to have to submit to

398
00:25:25,940 --> 00:25:26,940
the grant.

399
00:25:26,940 --> 00:25:29,060
But it's not always the case.

400
00:25:29,060 --> 00:25:33,740
The mice studies, all the mice can die for various reasons.

401
00:25:33,740 --> 00:25:36,140
And so we may not have solid data.

402
00:25:36,140 --> 00:25:38,900
So I would say, come even a year ahead.

403
00:25:38,900 --> 00:25:45,780
If you have some data, then just anecdotally, let's say you found some data anecdotally,

404
00:25:45,780 --> 00:25:50,940
then come to us right away and see how we can set up a study that we can systematically

405
00:25:50,940 --> 00:25:54,620
collect the data and write a grant on that.

406
00:25:54,620 --> 00:25:59,300
Because it's not just one question that you're going to answer from designing the study.

407
00:25:59,300 --> 00:26:01,900
You're going to be answering three or four questions.

408
00:26:01,900 --> 00:26:04,140
The aims typically are...

409
00:26:04,140 --> 00:26:11,820
So an R21 mechanism kind of helps you get data, collect data to write a big old grant.

410
00:26:11,820 --> 00:26:12,820
So it's basically R21.

411
00:26:12,820 --> 00:26:21,220
R03 is basically an R-age mechanism to really collect pilot data and then use that data

412
00:26:21,220 --> 00:26:25,780
then to do kind of an R21 and then move on to a bigger one.

413
00:26:25,780 --> 00:26:33,900
But in the medical schools, there are all these training grants that give you pilot

414
00:26:33,900 --> 00:26:35,520
money.

415
00:26:35,520 --> 00:26:41,060
So some of these small grants, $10,000 grants, can help you set up small studies.

416
00:26:41,060 --> 00:26:48,420
They may not fund your salary, but they can fund small studies, lab studies.

417
00:26:48,420 --> 00:26:49,420
Those are one.

418
00:26:49,420 --> 00:26:50,420
That is lab studies.

419
00:26:50,420 --> 00:26:57,580
But if they're not lab studies, if they involve secondary data, secondary data is already

420
00:26:57,580 --> 00:26:59,220
there.

421
00:26:59,220 --> 00:27:05,500
So this money can be the small study funding from pilot studies internally can be used

422
00:27:05,500 --> 00:27:21,940
on writing up grants, small grants, and also come up with some other decent studies that

423
00:27:21,940 --> 00:27:23,380
can go with that.

424
00:27:23,380 --> 00:27:26,580
So it doesn't have to ask just one question.

425
00:27:26,580 --> 00:27:30,300
You'll have three or four questions that can go with as part of the project.

426
00:27:30,300 --> 00:27:31,460
I love it.

427
00:27:31,460 --> 00:27:36,020
So I hear you talking about how you can even help people really maximize the benefit of

428
00:27:36,020 --> 00:27:37,380
any sample of data.

429
00:27:37,380 --> 00:27:39,780
So it's like here you're collecting this data.

430
00:27:39,780 --> 00:27:40,900
This is what you can get from it.

431
00:27:40,900 --> 00:27:41,900
You can also get this.

432
00:27:41,900 --> 00:27:43,500
You can also get that.

433
00:27:43,500 --> 00:27:48,420
And I also hearing you talk about for everyone to realize that no matter how small the pot

434
00:27:48,420 --> 00:27:52,500
of money you get, you can always do something with it to turn it into the next grant and

435
00:27:52,500 --> 00:27:54,980
the next grant and the next grant after that.

436
00:27:54,980 --> 00:27:55,980
That's awesome.

437
00:27:55,980 --> 00:27:56,980
Awesome.

438
00:27:56,980 --> 00:27:57,980
All right.

439
00:27:57,980 --> 00:28:03,020
Even big data sets, even big data sets, we could do some small studies using the big

440
00:28:03,020 --> 00:28:09,460
national databases, using the internal funding and use that to write bigger grants.

441
00:28:09,460 --> 00:28:10,460
I love it.

442
00:28:10,460 --> 00:28:11,460
I love it.

443
00:28:11,460 --> 00:28:12,460
Thank you.

444
00:28:12,460 --> 00:28:13,460
Thank you.

445
00:28:13,460 --> 00:28:14,460
Okay.

446
00:28:14,460 --> 00:28:17,980
So Maggie, if there is a clinician sitting out there thinking, I want to become a researcher,

447
00:28:17,980 --> 00:28:23,140
I want to work with a biostatistician, but I'm not sure I can, what encouragement do

448
00:28:23,140 --> 00:28:26,540
you have for them in terms of how best to move forward?

449
00:28:26,540 --> 00:28:28,260
Yeah.

450
00:28:28,260 --> 00:28:42,780
So nationally, NIH has started providing mechanisms for quantitative, to provide quantitative

451
00:28:42,780 --> 00:28:44,860
help to the institutes.

452
00:28:44,860 --> 00:28:53,820
So the CTSI is one of, one such grant that many organizations or many academic institutions

453
00:28:53,820 --> 00:29:08,540
write to get money, so that CTSI, it's like a, it's a core that helps investigators within

454
00:29:08,540 --> 00:29:14,100
the institute with all kinds of help they need.

455
00:29:14,100 --> 00:29:23,980
So Duke got two rounds of CTSI grants and as part of that, some of that money is given

456
00:29:23,980 --> 00:29:26,260
to the statisticians.

457
00:29:26,260 --> 00:29:32,180
So a 20 or 25% of a statistician's salary is covered by the CTSI.

458
00:29:32,180 --> 00:29:40,700
So those statisticians, what they do is now help investigators who are starting from ground

459
00:29:40,700 --> 00:29:42,860
zero.

460
00:29:42,860 --> 00:29:48,180
And they can tap into those aspects of any institute that they have.

461
00:29:48,180 --> 00:29:51,540
So practically every institute has some kind of money.

462
00:29:51,540 --> 00:29:55,020
It's the question of how much money that you want or how much effort that you want from

463
00:29:55,020 --> 00:29:56,660
a statistician.

464
00:29:56,660 --> 00:30:01,260
So if an investigator, if a new investigator is getting through the door, we have that

465
00:30:01,260 --> 00:30:02,260
all the time.

466
00:30:02,260 --> 00:30:08,740
We have lots of, lots and lots of investigators coming into our institute.

467
00:30:08,740 --> 00:30:14,380
And the first thing they want to know is what are the research resources available?

468
00:30:14,380 --> 00:30:20,700
So call up, so the thing to do is to call the Biostat department and find out what are

469
00:30:20,700 --> 00:30:22,700
all the resources available.

470
00:30:22,700 --> 00:30:28,620
Or within an institute, for example, Division of Hematology, for example, within the Division

471
00:30:28,620 --> 00:30:34,300
of Hematology, find out who are the, what are all the resources available to do research.

472
00:30:34,300 --> 00:30:43,220
Both basic science, long-term outcomes, outcomes research, what are all the resources available.

473
00:30:43,220 --> 00:30:44,420
So that's the starting point.

474
00:30:44,420 --> 00:30:50,740
If within the department or within the department that you're in, what are the resources available?

475
00:30:50,740 --> 00:30:52,460
And there are multiple resources available.

476
00:30:52,460 --> 00:30:54,700
That's not the only resource available.

477
00:30:54,700 --> 00:31:01,100
If the department tells you that this resource is available only for people who have funding,

478
00:31:01,100 --> 00:31:07,780
then at the School of Medicine level, there are resources available that are available

479
00:31:07,780 --> 00:31:11,300
for any researcher who can start from ground down.

480
00:31:11,300 --> 00:31:12,300
That's awesome.

481
00:31:12,300 --> 00:31:13,300
Thank you, Maggie.

482
00:31:13,300 --> 00:31:17,920
What I'm hearing from you is just that you got to keep pushing for what you need.

483
00:31:17,920 --> 00:31:21,780
Make sure you're looking, especially as you're applying for your first faculty job, making

484
00:31:21,780 --> 00:31:25,760
sure that these resources are already available, how you're going to get access to them.

485
00:31:25,760 --> 00:31:30,140
But even if you come and you don't have access to the resource to get a biostatistician to

486
00:31:30,140 --> 00:31:35,420
work with, look to your department, look to the schools, look to the institutes, especially

487
00:31:35,420 --> 00:31:39,780
if you have a CTSI, and look to see what resources are available.

488
00:31:39,780 --> 00:31:43,700
And I love that because I think one of the things, Maggie, we tell our audience is that

489
00:31:43,700 --> 00:31:45,340
you can't, don't get stuck.

490
00:31:45,340 --> 00:31:49,820
Don't make sure that you're taking ownership and leading your own research, not letting

491
00:31:49,820 --> 00:31:51,100
obstacles stop you.

492
00:31:51,100 --> 00:31:55,100
And so it sounds like really it's that there are resources available, but you do need to

493
00:31:55,100 --> 00:31:56,700
go out and look for them.

494
00:31:56,700 --> 00:31:57,700
Yeah, yeah.

495
00:31:57,700 --> 00:31:58,700
That's right.

496
00:31:58,700 --> 00:32:00,700
I do want to mention one thing though.

497
00:32:00,700 --> 00:32:07,620
So every department, if it's a research institution, every department has a vice chair of research.

498
00:32:07,620 --> 00:32:10,980
Go talk to the vice chair of research and tell them you are interested.

499
00:32:10,980 --> 00:32:16,940
You did some research in college or in high school, college, and in mid school that you

500
00:32:16,940 --> 00:32:18,500
want to pursue some of that research.

501
00:32:18,500 --> 00:32:20,940
How do I go about doing that?

502
00:32:20,940 --> 00:32:25,780
Talk to the vice chair of research and the vice chair of research will be able to help

503
00:32:25,780 --> 00:32:30,500
you put you in touch with somebody who's already doing something along those lines.

504
00:32:30,500 --> 00:32:33,540
So you have a mentoring right there.

505
00:32:33,540 --> 00:32:38,220
And so you get to start working with that particular research with somebody who's already

506
00:32:38,220 --> 00:32:39,540
doing that kind of work.

507
00:32:39,540 --> 00:32:45,620
Or you can start your own search because the department is interested in something that

508
00:32:45,620 --> 00:32:48,420
you just put up with.

509
00:32:48,420 --> 00:32:49,420
I love it.

510
00:32:49,420 --> 00:32:50,420
Thank you, Maggie.

511
00:32:50,420 --> 00:32:53,540
I hear you saying, you know what, there's someone at your institution who cares that

512
00:32:53,540 --> 00:32:54,980
research gets done.

513
00:32:54,980 --> 00:32:58,940
So find them and have them help you because that is their job.

514
00:32:58,940 --> 00:32:59,940
That is so awesome.

515
00:32:59,940 --> 00:33:03,260
Maggie, you have shared such amazing insights.

516
00:33:03,260 --> 00:33:04,260
Thank you so much.

517
00:33:04,260 --> 00:33:09,980
It is rare that we really have access to biostatisticians to help us get the inside story on how to

518
00:33:09,980 --> 00:33:11,980
work well with the biostatistician.

519
00:33:11,980 --> 00:33:15,020
And so I really want to thank you for the insights you've shared today.

520
00:33:15,020 --> 00:33:19,540
And to our audience members, if you have benefited from the things that Maggie has shared, please

521
00:33:19,540 --> 00:33:22,060
share this episode with somebody else who needs to hear it.

522
00:33:22,060 --> 00:33:26,500
Or if you're a mentor and your mentees need to understand this, please share this episode

523
00:33:26,500 --> 00:33:27,500
with them.

524
00:33:27,500 --> 00:33:31,700
So having said that, I want to say Maggie, thank you so much for coming on the show and

525
00:33:31,700 --> 00:33:34,660
sharing your wisdom with our audience.

526
00:33:34,660 --> 00:33:35,660
Thank you, Tiosi.

527
00:33:35,660 --> 00:33:37,780
Thank you for giving me this opportunity.

528
00:33:37,780 --> 00:33:41,260
And so thank you to our audience and we'll see you again next time.

529
00:33:41,260 --> 00:33:44,260
Take care.

530
00:33:44,260 --> 00:33:50,700
Bye.

531
00:33:50,700 --> 00:33:56,060
Thanks for listening to this episode of the Clinician Researcher Podcast, where academic

532
00:33:56,060 --> 00:34:01,500
clinicians learn the skills to build their own research program, whether or not they

533
00:34:01,500 --> 00:34:02,860
have a mentor.

534
00:34:02,860 --> 00:34:08,980
If you found the information in this episode to be helpful, don't keep it all to yourself.

535
00:34:08,980 --> 00:34:10,700
Someone else needs to hear it.

536
00:34:10,700 --> 00:34:14,760
So take a minute right now and share it.

537
00:34:14,760 --> 00:34:20,220
As you share this episode, you become part of our mission to help launch a new generation

538
00:34:20,220 --> 00:34:26,180
of clinician researchers who make transformative discoveries that change the way we do healthcare.