Life of a scientist: grant-writing edition.

I’ve been tweeting a ton lately about grant-writing but I know many of my followers aren’t academics & might not understand that process, so I thought I’d do a short explainer.

After all, government grants are your tax dollars.
As an example of how scientific grants work, I’m going to walk through the steps of a grant I submitted last fall to the National Institutes of Health (NIH).

The NIH is one of the major grant funding parts of the US govt, & the most common funding source for epidemiologists.
There are 3 times per year when you can apply for funding from the NIH — we call these “funding cycles” & they happen roughly in February, May, and October (dates vary a bit).

I submitted one in October.
It typically takes a couple of months to put together a grant application.

There are different types of grants, but most (health) scientists eventually want to get what’s called an R01 (pronounced “r-oh-one”).

This is the type I applied for.
An R01 would give me 4-5 years of funding, and cover some of my salary (I asked for ~30%), some of the salary of other scientists on my team, and all of the salary of some student researchers.

My team for this grant project includes ~10 scientists at two universities.
The grant application requires a ton of info from each of those scientists & all the universities involved.

This paperwork is the most time consuming part, but luckily I had help from a really excellent grants manager at BUSPH.
Then there’s the part of the grant where we tell the NIH what we want to do.

For an R01, this is 12 pages of justification & project plan, plus a one-page summary (called the “Specific Aims”), a ~half page summary (the “abstract”), and a 3-sentence summary (the “narrative”).
Yep, that’s right. Three different types of summaries. Why? For the various people who will review some or all of your application.

Which gets us to what happens after the grant was submitted in early October.
Everything gets checked by my university’s finance people, and then uploaded to a govt website called ERA Commons (pronounced “e-r-a”).

Once uploaded, there’s a 2-day period when you can make changes, and then it’s processed into the system.
A few days later, you get an email telling you what part of the NIH (there are many different institutes that together make up the National Institutes of Health) will get a chance to fund you, and which “study section” (aka review panel) will give your grant a score.
Now comes the waiting game. The study sections meet 3 times a year too, so a grant submitted in October has to wait until February for review.

I’ve never been a grant reviewer, so I’d love if people chime in with their experiences, but briefly, here’s what happens.
Each grant is given to 3 reviewers to read, score, and comment. Each reviewer has a stack of grants to review.

Reviewers are scientists who are volunteering their time *on top of* all their other job responsibilities to help the NIH decide what projects to fund.
They are exhausted, they are probably working on the weekend or in the evening, they are probably distracted by their own projects, and they might not even specialize in the topic area of your proposed project.

It’s not an easy thing to do!
But, eventually every grant gets 3 sets of scores. Scores range from 1-10 where 1 is the “best” and 10 is the “worst”. IDK why.

Then in roughly mid-February, the study section meets.
This is an all-day affair & usually a couple days. All of the grants that have been assigned to reviewers who are part of this study section get ranked.

The top 50% get discussed by everyone in the room, and those grants get a consensus score.
A grant I submitted *last* Feb didn’t get discussed at it’s study section last May. This is the kiss of death for a grant (although you do get to try again). A grant that isn’t discussed won’t be funded.
If your grant is discussed (and mine was this time around!🥳), it gets an “impact score” from 10-100 with 10 being best & 100 being worst, and a “percentile rank” with 1% being the best and 100% being the worst.
The rank and score are the keys to being funded, although it’s not always a guarantee. But if you got a rank of <5-10% then you are almost sure to be funded.

My grant got an in-between sort of score/rank, so I have to wait some more to find out if I’ll be funded.
What comes next: there is a council meeting that will happen in ~May that actually decides which projects get funded.

Before then, I can expect to get a “summary sheet”. This gives me the reviewers’ feedback & a detailed breakdown of my score.
If it seems like something was really misunderstood, I have the chance to send a rebuttal for the council to consider, but there’s no guarantee they will.
I might get a “just-in-time” request, which means I have to send evidence that my study has ethics approval & a list of all my other funding. This could happen before or after the council meeting.
After the council meeting, I’ll probably have to wait another few weeks or even months to find out for sure if my project is funded or not. If it is funded, the funding will start ~September. A full year after I applied for it!
The success rate of funding is ~10% and it takes a whole year to find out if you are successful. Most academic scientists have to fund between 50-100% of their salary from grants. Plus 100% of the salary of any students or postdocs they employ. You can see why we’re stressed.
So, that’s the basics of NIH grant funding.
•I submitted my grant application in October.
•Last week, my grant’s study section met. It was discussed so it might get funded, but the score was kind of mediocre so it might not.
•I’ll have to wait a few more months to find out.
(I’ve been informed that maybe my score isn’t quite so mediocre after all and I’m being too pessimistic, so now I’m crossing all my fingers & toes! I think this project is so important & I want to be able to work on it so badly!!!)

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More from @EpiEllie

10 Feb
There’s been some really tantalizing data coming out in the past few days that the COVID vaccines might in fact do a great job reducing infection & transmission too, and if this holds up it is such a game changer!
I expect that we will finally have an answer to the extremely important question of how much the vaccines reduce transmission soon.

(Incidentally, I’ve been saying we’d learn this in Feb or March since December)
That said, I disagree with a claim I’ve been seeing circulating from many people that scientists & public health experts should have been always messaging that vaccines reduce transmission, because that message would encourage vaccine uptake.
Read 11 tweets
31 Jan
Turns out I’ve never actually explained the g-formula #onhere, even though I tweet about quite a bit, and it’s a vital part of my #causalinference toolkit & helps shape my whole approach doing science.

So, b/c it’s Sunday & I’m bored, let’s nerd out a bit. Ready?
Before we get into the details of the g-formula, we need to step back & talk about what #causalinference is.

Simply put, causal inference is the science of getting a reliable & believable answer to the question “what will happen in the world if I do A instead of B?”
Say we wanted to know how many fewer people would have heart attacks if everyone was vegetarian compared to if everyone ate meat.

If we had a time machine, we could feed people one diet, see what happens, and then go back & do it over with the other diet.
Read 60 tweets
23 Jan
We need to do now what we should have done last Spring: shut it all down, provide funds to support people & businesses thru closure and—here’s the key—USE THE TIME TO IMPLEMENT GOOD RESPONSE. That means: better testing & vaccine logistics, ventilation in schools & workplaces...
...improved production & distribution of N95 masks for everyone, improve internet access to allow wfh & school from home for those who *can* continue with that, safe opening plans for every type of workplace, universal paid quarantine & sick leave, open up parks & beaches...
...public health educational campaigns that teach everyone the basics of how viruses work (and while we’re at it, lets add some bacterial info to combat overuse of antibiotics), extended eviction moratoriums, actual oversight & accountability for workplace safety...
Read 11 tweets
14 Jan
IDK who needs to hear this but in the context of COVID “driven” is a term that implies a level of causal primacy which is almost always untrue and for the establishment of which we generally do not have sufficient data nor methods.
Our data and methods can tell us whether an activity or a location is one cause among many. For COVID, places that facilitate transmission include bars and restaurants and schools and nursing homes and prisons and workplaces and households and parties and so so many others
We do not collect the right data nor, in absence of data, have good methods to identify which is the PRIMARY cause of the pandemic. We do not establish for each case where & from whom that person got infected nor where & to whom they transmitted. We do not identify every case.
Read 7 tweets
3 Jan
I will miss George immensely. I learned so much from him, not just about epidemiology & how to do research, but about how to be a great mentor & a great human being. George always made time to talk with students about anything & everything and encouraged me in so many ways.
George served on my doctoral committee, and as a co-Investigator on my first ever NIH grant, which would never have gotten written, much less accepted, without his support and guidance and friendship.
I’m sure many many other epidemiologists can say the same about George & I hope everyone will share their stories. He has impacted so many people so deeply.
Read 4 tweets
31 Dec 20
I did a TON of things in 2020, but one thing I didn't manage to keep up with is writing #tweetorials for all of the academic papers I published.

So here's a thread of very short summaries of the papers I've been a part of. Let me know which ones you want to hear more about!
My first paper of 2020 was the final paper from my dissertation. I really love this paper because it packs so many ideas into such a short amount of text, but one thing I regret is not giving it a more general title.

journals.sagepub.com/doi/abs/10.117…
Despite the title's focus on individual-level simulation models, the paper has important implications for all mediation analyses and for defining causal questions for non-manipulable exposures.

journals.sagepub.com/doi/abs/10.117…
Read 38 tweets

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