The R37/MERIT award is an interesting beast in NIH-land. It is typically (exclusively?) awarded upon a successful competing continuation (now called renewal) R01 application. Program then decides in some cases to extend the interval of non-competition for another 5 years*. This, my friends, is person-not-project based funding.

The R37 is a really good gig….if you can get it.

So, given that I’m blogging about award disparity this week….I took a look at the R37s currently on the books for one of my favorite ICs.

There are 25 of them.

The PIs include

1 transgender PI.
4 female PIs
0 East Asian / East Asian-American PIs (that I could detect)
3 South Asian / South Asian-American PIs (that I could detect)
0 SubSaharan African / African-American PIs (that I could detect)
0 Latino PIs (that I could detect)

hmmm, not that strong of a job. How about another of my favorite ICs?

23 awards (Interesting because this IC is half the size of the above-mentioned one)

12 female PIs.
0 East Asian / East Asian-American PIs (that I could detect)
1-2 South Asian / South Asian-American PIs (that I could detect)
0 SubSaharan African / African-American PIs (that I could detect)
3-4 Latino PIs (that I could detect)

way better on the sex distribution. Whether this number of R37s reflects more than average good-old-folks clubbery or the above represents less than average I don’t know. 25 at another large IC close to my interests. 95ish (I didn’t parse for supplements) at another. Only 45ish at NCI. Clearly a big range relative to IC size.

Both of these are doing really poorly on East Asian/ Asian-American and African-American PIs. The first is pretty pathetic on Latino PIs as well.

On the other hand, good old white guys with grey hair or receding hairlines are doing quite well in the R37 stakes.

How are your favorite ICs doing, Dear Reader?

*The way I hear it. I have heard rumor that these can go beyond a total of 10 years of R37 but I’m not sure on that.

In a conversation on the twitts:

@drugmonkeyblog @RockTalking I’m an out gay grad student and I have never, to my knowledge, met an LGBT PI. is it the numbers or visibility?

Yeah, that sucks.

The takeaway message from the report of Ginther and colleagues (2011) on Race, Ethnicity and NIH Research Awards can be summed up by this passage from the end of the article:

Applications from black and Asian investigators were significantly less likely to receive R01 funding compared with whites for grants submitted once or twice. For grants submitted three or more times, we found no significant difference in award probability between blacks and whites; however, Asians remained almost 4 percentage points less likely to receive an R01 award (P < .05). Together, these data indicate that black and Asian investigators are less likely to be awarded an R01 on the first or second attempt, blacks and Hispanics are less likely to resubmit a revised application, and black investigators that do resubmit have to do so more often to receive an award.

Recall that these data reflect applications received for Fiscal Years 2000 to 2006.

Interestingly, we were just discussing the most recent funding data from the NIH with a particular focus on the triaged applications. A comment on the Rock Talk blog of the OER at NIH was key.

I received a table of data covering A0 R01s received between FY 2010 and FY2012 (ARRA funds and solicited applications were excluded). Overall at NIH, 2.3% of new R01s that were “not scored” as A0s were funded as A1s (range at different ICs was 0.0% to 8.4%), and 8.7% of renewals that were unscored as A0s were funded as A1s (range 0.0% to 25.7%).

I noted the following for a key distinction between new and competing-continuation applications.

The mean and selected ICs I checked tell the same tale, i.e., that Type 2 apps have a much better shot at getting funded after triage on the A0. NIDA is actually pretty extreme from what I can tell- 2.8% versus 15.2%. So if there is a difference in the A1 resubmission rate for Type 1 and Type 2 (and I bet Type 2 apps that get triaged on A0 are much more likely to be amended and resubmitted) apps, the above analysis doesn’t move the relative disadvantage around all that much. However for NIAAA the Type 1 and Type 2 numbers are closer- 4.7% versus 9.8%. So for NIAAA supplicants, a halving of the resubmission rate for Type 1 might bring the odds for Type 1 and Type 2 much closer.

So look. If you were going to try to really screw over some category of investigators you would make sure they were more likely to be triaged and then make it really unlikely that a triaged application could be revised into the fundable range. You could stoke this by giving an extra boost to triaged applications that had already been funded for a prior interval….because your process has already screened your target population to decrease representation in the first place. It’s a feed-forward acceleration.

What else could you do? Oh yes. About those revisions, poorer chances on the first 1-2 attempts and the need for Asian and black PIs to submit more often to get funded. Hey I know, you could prevent everybody from submitting too many revised versions of the grant! That would provide another amplification of the screening procedure.

So yeah. The NIH halved the number of permitted revisions to previously unfunded applications for those submitted after January 25, 2009.

Think we’re ever going to see an extension of the Ginther analysis to applications submitted from FY2007 onward? I mean, we’re seeing evidence in this time of pronounced budgetary grimness that the NIH is slipping on its rather overt efforts to keep early stage investigator success rates similar to experienced investigators’ and to keep women’s success rates similar to mens’.

The odds are good that the plight of African-American and possibly even Asian/Asian-American applicants to the NIH has gotten even worse than it was for Fiscal Years 2000-2006.