It is hard to overstate the problem that plummeting success rates at the NIH have caused for biomedical science careers. We have expectations for junior faculty that were developed in the 1980s and maybe into the 90s. Attitudes that are firmly entrenched in our senior faculty who got their first awards in the 1980s or even the 1970s…and then were poised to really rake it in during the doubling interval (since undoubled). Time for a trip down memory lane.
The red trace depicts success rates from 1962 to 2008 for R01 equivalents (R01, R23, R29, R37). These are not broken down by experienced/new investigators status, nor are new applications distinguished from competing continuation applications. The blue line shows total number of applications reviewed and the data in the 60s are listed as “estimated” success rates. (source)
The extension of these data into more recent FY can be found over at the RePORTER. I like to keep my old graph because NIH has this nasty tendency to disappear the good old days so we’ll forget about how bad things really are now. From 2011 to 2017 success rates hovered from 17 to 19% and in the past two years we’ve seen 21-22% success.
In the historical trends from about 1980 to the end of the doubling in 2002 we see that 30% success rates ruled the day as expected average. Deviations were viewed as disaster. In fact the doubling of the NIH budget over a decade was triggered by the success rates falling down into the 25% range and everyone screaming at Congress for help. For what it is worth, the greybeards when I was early career were still complaining about funding rates in the early 1980s. Was it because they were used to the 40% success years right before that dropping down to 30%? Likely. When they were telling us “it’s all cyclical, we’ve seen this before on a decade cycle” during the post-doubling declines….well it was good to see these sorts of data to head off the gaslighting, I can tell you.
Anyway, the point of the day is that folks who had a nice long run of 30% success rates (overall; it was higher once you were established, aka had landed one grant) are the ones who set, and are setting, current expectations. Today’s little exercise in cumulative probability of grant award had me thinking. What does this analysis look like in historical perspective?
I’m using the same 17.7% success rate for applications with white PIs reported in Hoppe et al and 30% as a sort of historical perspective number. Relevant to tenure expectations, we can see that the kids these days have to work harder. Back in the day, applicants had a 83.2% cumulative probability of award with just 5 applications submitted. Seems quaint doesn’t it? Nowadays a white PI would have to submit 9 applications to get to that same chance of funding.
How does that square with usual career advice? Well, of course newbs should not submit R01 in the first year. Get the lab up and running on startup, maybe get a paper, certainly get some solid preliminary data. Put the grant in October in year 2 (triaged), wait past a round to do a serious revision, put it in for July. Triaged again in October of Year 3. Two grants in, starting Year 3. Well now maybe things are clicking a bit so the PI manages to get two new proposals together for Oct and/or Feb and if the early submission gets in, another revision for July. So in Fall of Year 4 we’re looking at four or five submissions with a fairly good amount of effort and urgency. This could easily stretch into late Year 4.
Where do the kids these days fit in four more applications?