Disparity of NSF funding

You are familiar with the #GintherGap, the disparity of grant award at NIH that leaves the applications with Black PIs at substantial disadvantage. Many have said from the start that it is unlikely that this is unique to the NIH and we only await similar analyses to verify that supposition.

Curiously the NSF has not, to my awareness, done any such study and released it for public consumption.

Well, a group of scientists have recently posted a preprint:

^{Chen, C. Y., Kahanamoku, S. S., Tripati, A., Alegado, R. A., Morris, V. R., Andrade, K., & Hosbey, J. (2022, July 1). Decades of systemic racial disparities in funding rates at the National Science Foundation. OSF Preprints. July 1. doi:10.31219/osf.io/xb57u.}

It reviews National Science Foundation awards (from 1996-2019) and uses demographics provided voluntarily by PIs. They found that the applicant PIs were 66% white, 3% Black, 29% Asian and below 1% for each of American Indian/Alaska Native and Native Hawaiian/Pacific Islander groups. They also found that across the reviewed years, the overall funding rate varied from 22%-34%, so the data were represented as the rate for each group relative to the average for each year. In Figure 1, reproduced below, you can see that applications with white PIs enjoy a nice consistent advantage relative to other groups and the applications with Asian PIs suffer a consistant disadvantage. The applications with Black PIs are more variable year over year but are mostly below average except for 5 years when they are right at the average. The authors note this means that in 2019, there were 798 awards with white PIs above expected value, and 460 fewer than expected awarded with Asian PIs. The size of the disparity differs slightly across the directorates of the NSF (there are seven, broken down by discipline such as Biological Sciences, Engineering, Math and Physical Sciences, Education and Human Resources, etc) but the same dis/advantage based on PI race remains.

Fig 1B from Chen et al. 2022 preprint

It gets worse. It turns out that these numbers include both Research and Non-Research (conference, training, equipment, instrumentation, exploratory) awards. Which represent 82% and 18% of awards, with the latter generally being awarded at 1.4-1.9 times the rate for Research awards in a given year. For white

Fig 3 from Chen et al 2022 preprint FY 13 – 19;
open = Non-Research, closed = Research

PI applications the two types both are funded at higher than the average rate, however significant differences emerge for Black and Asian PIs with Research awards having the lower probability of success.

So why is this the case. Well, the white PI applications get better scores from extramural reviewers. Here, I am not expert in how NSF works. A mewling newbie really. But they solicit peer reviewers which assign merit scores from 1 (Poor) to 5 (Excellent). The preprint shows the distributions of scores for FY15 and FY16 Research applications, by PI race, in Figure 5. Unsurprisingly there is a lot of overlap but the average score for white PI apps is superior to that for either Black or Asian PI apps. Interestingly, average scores are worse for Black PI apps than for Asian PI apps. Interesting because the funding disparity is larger for Asian PIs than for Black PIs. And as you can imagine, there is a relationship between score and chances of being funded but it is variable. Kind of like a Programmatic decision on exception pay or the grey zone function in NIH land. Not sure exactly how this matches up over at NSF but the first author of the preprint put me onto a 2015 FY report on the Merit Review Process that addresses this. Page 74 of the PDF (NSB-AO-206-11) has a Figure 3.2 showing the success rates by average review score and PI race. As anticipated, proposals in the 4.75 (score midpoint) bin are funded at rates of 80% or better. About 60% for the 4.25 bin, 30% for the 3.75 bin and under 10% for the 3.25 bin. Interestingly, the success rates for Black PI applications are higher than for white PI applications at the same score. The Asian PI success rates are closer to the white PI success rates but still a little bit higher, at comparable scores. So clearly something is going on with funding decision making at NSF to partially counter the poorer scores, on average, from the reviewers. The Asian PI proposals do not have as much of this advantage. This explains why the overall success rates for Black PI applications are closer to the average compared with the Asian PI apps, despite worse average scores.

Fig 5 from Chen et al 2022 preprint

One more curious factor popped out of this study. The authors, obviously, had to use only the applications for which a PI had specified their race. This was about 96% in 1999-2000 when they were able to include these data. However it was down to 90% in 2009, 86% in 2016 and then took a sharp plunge in successive years to land at 76% in 2019. The first author indicated on Twitter that this was down to 70% in 2020, the largest one year decrement. This is very curious to me. It seems obvious that PIs are doing whatever they think is going to help them get funded. For the percentage to be this large it simply has to involve large numbers of white PIs and likely Asian PIs as well. It cannot simply be Black PIs worried that racial identification will disadvantage them (a reasonable fear, given the NIH data reported in Ginther et al.) I suspect a certain type of white academic who has convinced himself (it’s usually a he) that white men are discriminated against, that the URM PIs have an easy ride to funding and the best thing for them to do is not to declare themselves white. Also another variation on the theme, the “we shouldn’t see color so I won’t give em color” type. It is hard not to note that the US has been having a more intensive discussion about systemic racial discrimination, starting somewhere around 2014 with the shooting of Michael Brown in Ferguson MO. This amped up in 2020 with the strangulation murder of George Floyd in Minneapolis. Somewhere in here, scientists finally started paying attention to the Ginther Gap. News started getting around. I think all of this is probably causally related to sharp decreases in the self-identification of race on NSF applications. Perhaps not for all the same reasons for every person or demographic. But if it is not an artifact of the grant submission system, this is the most obvious conclusion.

There is a ton of additional analysis in the preprint. Go read it. Study. Think about it.

Additional: Ginther et al. (2011) Race, ethnicity, and NIH research awards. Science, 2011 Aug 19; 333(6045):1015-9. [PubMed]

NIH data on Discussion Rates and Grant Submitting Vigor

The latest blog post over at Open Mike, from the NIH honcho of extramural grant award Mike Lauer, addresses “Discussion Rate”. This is, in his formulation, the percent of applicants (in a given Fiscal Year, FY21 in this case) who are PI on at least one application that reaches discussion. I.e., not triaged. The post presents three Tables, with this Discussion rate (and Funding rate) presented by the Sex of the PI, by race (Asian, Black, White only) or ethnicity (Hispanic or Latino vs non-Hispanic only). The tables further presented these breakdowns by Early Stage Investigator, New Investigator, At Risk and Established. At risk is a category of “researchers that received a prior substantial NIH award but, as best we can tell, will have no funding the following fiscal year if they are not successful in securing a competing award this year.” At this point you may wish to revisit an old blog post by DataHound called “Mind the Gap” which addresses the chances of regaining funding once a PI has lost all NIH grants.

I took the liberty of graphing the By-Race/Ethnicity Discussion rates, because I am a visual thinker.

There seem to be two main things that pop out. First, in the ESI category, the Discussion rate for Black PI apps is a lot lower. Which is interesting. The 60% rate for ESI might be a little odd until you remember that the burden of triage may not fall on ESI applications. At least 50% have to be discussed in each study section, small numbers in study section probably mean that on average it is more than half, and this is NIH wide data for FY 21 (5,410 ESI PIs total). Second, the NI category (New, Not Early on the chart) seems to suffer relative to the other categories.

Then I thought a bit about this per-PI Discussion rate being north of 50% for most categories. And that seemed odd to me. Then I looked at another critical column on the tables in the blog post.

The Median number of applications per applicant was…. 1. That means the mode is 1.

Wow. Just….wow.

I can maybe understand this for ESI applicants, since for many of them this will be their first grant ever submitted.

but for “At Risk”? An investigator who has experience as a PI with NIH funding, is about to have no NIH funding if a grant does not hit, and they are submitting ONE grant application per fiscal year?

I am intensely curious how this stat breaks down by deciles. How many at risk PIs are submitting only one grant proposal? Is it only about half? Two-thirds? More?

As you know, my perspective on the NIH grant getting system is that if you have only put in one grant you are not really trying. The associated implication is that any solutions to the various problems that the NIH grant award system might have that are based on someone not getting their grant after only one try are not likely to be that useful.

I just cannot make this make sense to me. Particularly if the NIH

It is slightly concerning that the NIH is now reporting on this category of investigator. Don’t get me wrong. I believe this NIH system should support a greater expectation of approximately continual funding for investigators who are funded PIs. But it absolutely cannot be 100%. What should it be? I don’t know. It’s debatable. Perhaps more importantly who should be saved? Because after all, what is the purpose of NIH reporting on this category if they do not plan to DO SOMETHING about it? By, presumably, using some sort of exception pay or policy to prevent these at risk PIs from going unfunded.

There was just such a plan bruited about for PIs funded with the ESI designation that were unable to renew or get another grant. They called them Early Established Investigators and described their plans to prioritize these apps in NOT-OD-17-101. This was shelved (NOT-OD-18-214) because “NIH’s strategy for achieving these goals has evolved based on on-going work by an Advisory Committee to the Director (ACD) Next Generation Researchers Initiative Working Group and other stakeholder feedback” and yet asserted “NIH..will use an interim strategy to consider “at risk investigators”..in its funding strategies“. In other words, people screamed bloody murder about how it was not fair to only consider “at risk” those who happened demographically to benefit from the ESI policy.

It is unclear how these “consider” decisions have been made in the subsequent interval. In a way, Program has always “considered” at risk investigators, so it is particularly unclear how this language changes anything. In the early days I had been told directly by POs that my pleas for an exception pay were not as important because “we have to take care of our long funded investigators who will otherwise be out of funding”. This sort of thing came up in study section more than once in my hearing, voiced variously as “this is the last chance for this PIs one grant” or even “the PI will be out of funding if…”. As you can imagine, at the time I was new and full of beans and found that objectionable. Now….well, I’d be happy to have those sentiments applied to me.

There is a new version of this “at risk” consideration that is tied to the new PAR-22-181 on promoting diversity. In case you are wondering why this differs from the famously rescinded NINDS NOSI, well, NIH has managed to find themselves a lawyered excuse.

Section 404M of the Public Health Service Act (added by Section 2021 in Title II, Subtitle C, of the 21st Century Cures Act, P.L. 114-255, enacted December 13, 2016), entitled, “Investing in the Next Generation of Researchers,” established the Next Generation Researchers Initiative within the Office of the NIH Director.  This initiative is intended to promote and provide opportunities for new researchers and earlier research independence, and to maintain the careers of at-risk investigators.  In particular, subsection (b) requires the Director to “Develop, modify, or prioritize policies, as needed, within the National Institutes of Health to promote opportunities for new researchers and earlier research independence, such as policies to increase opportunities for new researchers to receive funding, enhance training and mentorship programs for researchers, and enhance workforce diversity;

“enacted December 13, 2016“. So yeah, the NOSI was issued after this and they could very well have used this for cover. The NIH chose not to. Now, the NIH chooses to use this aspect of the appropriations language. And keep in mind that when Congress includes something like this NGRI in the appropriations language, NIH has requested it or accepted it or contributed to exactly how it is construed and written. So this is yet more evidence that their prior stance that the “law” or “Congress” was preventing them from acting to close the Ginther Gap was utter horseshit.

Let’s get back to “at risk” as a more explicitly expressed concern of the NIH. What will these policies mean? Well, we do know that none of this comes with any concrete detail like set aside funds (the PAR is not a PAS) or ESI-style relaxation of paylines. We do know that they do this all the damn time, under the radar. So what gives? Who is being empowered by making this “consideration” of at-risk PI applications more explicit? Who will receive exception pay grants purely because they are at risk? How many? Will it be in accordance with distance from payline? How will these “to enhance diversity” considerations be applied? How will these be balanced against regular old “our long term funded majoritarian investigator is at risk omg” sentiments in the Branches and Divisions?

This is one of the reasons I like the aforementioned Datahound analysis, because at least it gave a baseline of actual data for discussion purposes. A framework a given I or C could follow in starting to make intelligent decisions.

What is the best policy for where, who, what to pick up?

Research colonialism, analyzing an RFA

I recently fielded a question from a more junior scientist about what, I think, has been termed research colonialism with specificity to the NIH funding disparity known as the Ginther Gap. One of the outcomes of the Hoppe et al 2019 paper, and the following Lauer et al 2021, was a call for a hard look at research on the health issues of communities of color. How successful are grant proposals on those topics, which ICs are funding them, what are the success rates and what are the budget levels appropriated to, e.g. the NIMHD. I am very much at sea trying to answer the question I was asked, which boiled down to “Why is it always majoritarian PIs being funded to do research with communities of color?”. I really don’t know how to answer that or how to begin to address it with NIH funding data that has been generated so far. However, something came across my transom recently that is a place to start.

The NIH issued RFA-MD-21-004 Understanding and Addressing the Impact of Structural Racism and Discrimination on Minority Health and Health Disparities last year and the resulting projects should be on the RePORTER books by now. I was cued into this by a tweet from the Constellation Project which is something doing co-author networks. That may be useful for a related issue, that of collaboration and co-work. For now, I’m curious about what types of PIs have been able to secure funding from this mechanism. According to my RePORTER search for the RFA, there are currently 17 grants funded.

Of the funded grants, there are 4 from NIMHD, 4 from NIDA, 2 from NIA, 1 each from NIMH, NIHNDS, NINR, NICHD, NIGMS, NIDCD, and NCCIH. In the RFA, NIMHD promised 6-7 awards, NIDA 2, NIA 6, NIGMS 4-6 so obviously NIDA overshot their mark, but the rest are slacking. One each was promised for NIMH, NINDS, NICHD, NIDCD and NCCIH, so all of these are on track. Perhaps we will see a few more grants get funded by the time the FY elapses on Sept 30.

So who is getting funded under this RFA? Doing a quick google on the PIs, and admittedly making some huge assumptions based on the available pictures, I come up with

PI/Multi-PI Contact: White woman (2 NIA; 1 NCCIH; 3 NIDA; 1 NIDCD; 1 NIGMS; 1 NINDS); Black woman (1 NIDA; 1 NICHD; 1 NIMHD); Asian woman (1 NIMHD; 1 NIMHD; 1 NINR); White man (1 NIMHD; 1 NIMH)

Multi-PI, non-contact: Asian woman (1 NIA, 1 NIDA, 1 NIMHD); Black woman (2 NIDA, 1 NIMHD); White woman (1 NIDCD; 1 NIGMS; 1 NINR) Black man (1 NIGMS; 1 NIMH); White man (2 NIMH)

I would say the place I am most likely to be off in terms of someone who appears to me to be white but identifies as a person of color would be white women. Maybe 2-3 I am unsure of. I didn’t bother to keep track of how many of the non-contact PIs are on the proposals with white Contact PIs versus the other way around but….I can’t recall seeing even one where a non-contact white PI was on a proposal with a contact PI who is Black or Asian. (There was one award with three white men and one Black man as PIs and, well, does anyone get away with a four PI list that includes no woman anymore?) Anyway… make of that what you will.

I suspect that this RFA outcome is probably slightly better than the usual? And that if you looked at NIH’s studies that deal with communities or color and/or their health concerns more generally it would be even more skewed towards white PIs?

Ginther et al 2011 reported 69.9% of apps in their sample had white PIs, 16.2% had Asian PIs and 1.4% had Black PIs. Hoppe et al 2019 reported (Table S1) 1.5% of applications had Black PIs and 65.7% had white PIs in their original sample. So the 11 out of 17 grants having white PIs/Contact MultiPIs matches expected distribution, as does 3 Asian PIs. Black PIs are over represented since 1-2% of 17 is..zero grants funded. So this was not an opportunity that NIH took to redress the Ginther Gap.

But should it be? What should be the identity of PIs funded to work on issues related to “racism and discrimination” as it applies to “minority health and health disparities”? The “best” as determined by a study section of peer scientists, regardless of applicant characteristics? Regardless of the by now very well established bias against applications with Black PIs?

Someone on twitter asked about the panel that reviewed these grants. You can see from the funded grants on RePORTER that the study section reviewing these proposals was ZMD1 KNL (J1). Do a little web searching and you find that the roster for the 11/15/2021-11/17/2021 meeting is available. A three day meeting. That must have been painful. There are four chairs and a huge roster listed. I’m not going to search out all of them to figure out how many were white on the review panel. I will note that three of the four chairs were white and one was Asian (three of four were MDs, one was a PHD). This is a good place for a reminder that Hoppe et al reported 2.4% of reviewers were Black and 77.8% white in the study sections reviewing proposals for funding in FY2011-2015. I would be surprised if this study section was anything other than majority white.

Research Opportunities for New and “At-Risk” Investigators to Promote Workforce Diversity

NIDA, NIMH, and NINDS have issued a Program Announcement (PAR-22-181) to provide Research Opportunities for New and “At-Risk” Investigators with the intent to Promote Workforce Diversity.

This is issued as a PAR, which is presumably to allow Special Emphasis Panels to be convened. It is not a PAS, however, the announcement includes set-aside funding language familiar to PAS and RFA Funding Opportunity Announcements (FOA).

Funds Available and Anticipated Number of Awards The following NIH components intend to commit the following amounts for the duration of this PAR: NINDS intends to commit up to $10 million per fiscal year, approximately 25 awards, dependent on award amounts; NIDA intends to commit up to $5 million per fiscal year, 12-15 awards, dependent on award amounts; NIMH intends to commit up to $5 million per fiscal year, 12-15 awards, dependent on award amounts; Future year amounts will depend on annual appropriations.

This is a PA typical 3 year FOA which expires June 7, 2025. Reciept dates are one month ahead of standard, i.e., Sept (new R01) / Oct (Resub, Rev, Renew); Jan/Feb; May/Jun for the respective Cycles.

Eligibility is in the standard categories of concern including A) Underrepresented Racial/Ethnic groups, B) Disability, C) economic disadvantage and D) women. Topics of proposal have to be within the usual scope of the participating ICs. Eligibility of PIs is for the familiar New Investigators (“has not competed successfully for substantial, NIH (sic) independent funding from NIH“) and a relatively new “at risk” category.

At risk is defined as “has had prior support as a Principal Investigator on a substantial independent research award and, unless successful in securing a substantial research grant award in the current fiscal year, will have no substantial research grant funding in the following fiscal year.“

So. We have an offset deadline (at least for new proposals), set aside funds, SEPs for review and inclusion of NI (instead of merely ESI) and the potential for the more experienced investigator who is out of funding to get help as well. Pretty good! Thumbs up. Can’t wait to see other ICs jump on board this one.

To answer your first question, no, I have no idea how this differs from the NINDS/NIDA/NIAAA NOSI debacle. As a reminder:

Notice NOT-NS-21-049 Notice of Special Interest (NOSI): NIH Research Project Grant (R01) Applications from Individuals from Diverse Backgrounds, Including Under-Represented Minorities was released on May 3, 2021.

The “debacle” part is that right after NIDA and NIAAA joined NINDS in this NOSI, the Office of the Director put it about that no more ICs could join in and forced a rescinding of the NOSI on October 25, 2021 while claiming that their standard issue statement on diversity accomplished the same goals.

I see nothing in this new PAR that addresses either of the two real reasons that may have prompted the Office of the Director to rescind the original NOSI. The first and most likely reason is NIH’s fear of right wing, anti-affirmative action, pro-white supremacy forces in Congress attacking them. The second reason would be people in high places* in the NIH that are themselves right wing, anti-affirmative action and pro-white supremacy. If anything, the NOSI was much less triggering since it came with no specific plans of action or guarantees of funding. The PAR, with the notification of intended awards, is much more specific and would seemingly be even more offensive to right wingers.

I do have two concerns with this approach, as much as I like the idea.

First, URM-only opportunities have a tendency to put minority applicants in competition with each other. Conceptually, suppose there is an excellent URM qualified proposal that gets really high priority scores from study section and presume it would have also done so in an open, representation-blind study section. This one now displaces another URM proposal in the special call and *fails to displace* a lesser proposal from (statistically probable) a majoritarian PI. That’s less good than fixing the bias in the first place so that all open competitions are actually open and fair. I mentioned this before:

These special FOA have the tendency to put all the URM in competition with each other. This is true whether they would be competitive against the biased review of the regular FOA or, more subtly, whether they would be competitive for funding in a regular FOA review that had been made bias-free(r). […] The extreme example here is the highly competitive K99 application from a URM postdoc. If it goes in to the regular competition, it is so good that it wins an award and displaces, statistically, a less-meritorious one that happens to have a white PI. If it goes in to the MOSAIC competition, it also gets selected, but in this case by displacing a less-meritorious one that happens to have a URM PI. Guaranteed.

The second concern is one I’ve also described before.

In a news piece by Jocelyn Kaiser, the prior NIH Director Elias Zerhouni was quoted saying that study sections responded to his 2006/2007 ESI push by “punishing the young investigators with bad scores”. As I have tried to explain numerous times, phrasing this as a matter of malign intent on the part of study section members is a mistake. While it may be true that many reviewers opposed the idea that ESI applicants should get special breaks, adjusting scores to keep the ESI application at the same chances as before Zerhouni’s policies took effect is just a special case of a more general phenomenon.

So, while this PAR is a great tactical act, we must be very vigilant for the strategic, long term concerns. It seems to me very unlikely that there will be enthusiasm for enshrining this approach for decades (forever?) like the ESI breaks on merit scores/percentiles/paylines. And this approach means it will not be applied by default to all qualifying applications, as is the case for ESI.

Then we get to the Oppression Olympics, an unfortunate pitting of the crabs in the barrel against each other. The A-D categories of under-representation and diversity span quite a range of PIs. People in each category, or those who are concerned about specific categories, are going to have different views on who should be prioritized. As you are well aware, Dear Reader, my primary concern is with the Ginther gap. As you are aware, the “antis” and some pro-diversity types are very concerned to establish that a specific person who identifies as African-American has been discriminated against and is vewwwwy angweee to see any help being extended to anyone of apparent socio-economic privileges who just so happens to be Black. Such as the Obama daughters. None of us are clean on this. Take Category C. I have relatively recently realized that I qualify under Category C since I tick three of the elements, only two are required. I do not think that there is any possible way that my qualification on these three items affects my grant success in the least. To do so would require a lot of supposing and handwaving. I don’t personally think that anyone like me who qualifies technically under Category C really should be prioritized against, say, the demonstrated issue with the Ginther gap. These are but examples of the sort of “who is most disadvantaged and therefore most deserving” disagreement that I think may be a problem for this approach.

Why? Because reviewers will know that this is the FOA they are reviewing under. Opinions on the relative representation of categories A-D, Oppression Olympics and the pernicious stanning of “two-fers” will be front and present. Probably explicit in some reviews. And I think this is a problem in the broader goals of improving equity of opportunity and in playing for robust retention of individuals in the NIH funded research game.

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*This is going to have really ugly implications for the prior head of the NIH, Francis Collins, if the PAR is not rescinded from the top and the only obvious difference here is his departure from NIH.

Grant awards and the new, new NIH Biosketch

Way back in 2015 the NIH made some major changes to the Biosketch. As detailed in this post, one of the major changes was replacing the long list of publications with a “contribution to science” section which was supposed to detail up to five areas of focus with up to four papers, or other research products, cited for each contribution. Some of the preamble from NIH on this suggests it was supposed to be an anti-Glamour measure. Sure. There was also an inclusion of a “personal statement” which was supposed to be used to further brag on your expertise as well as to explain anything…funny… about your record.

In dealing with the “contributions to science” change, I more or less refused to do what was requested. As someone who had been a PI for some time, had mostly senior author pubs and relatively few collaborative papers, I could do this. I just made a few statements about an area I have worked in and listed four papers for each. I didn’t describe my specific role as instructed. I didn’t really describe the influence or application to health or technology. So far this has gone fine, as I can’t remember any comments on Investigator on grants I’ve submitted with this new (old) Biosketch that appear confused about what I have done.

The NIH made some other changes to the Biosketch in 2021, the most notable of which was the removal of the list of Research Support that was previously in Section D. I pointed out in a prior post that I suspect this was supposed to be an attempt to break a specific culture of peer review. One that had hardened reviewers and applicants against the longstanding intent of the NIH. It is very clear in the prior instructions that Section D was not supposed to list all active and completed funding over the past three years. The NIH instructed us to only include that which we wanted to call attention to and added the note that it was for reviewers to assess qualifications of the research team for the new project being proposed. They further underlined this by instructing applicants not to confuse this with the Other Support page which was explicitly for reporting all funding. This failed entirely.

As we have said many times, many ways on this blog…. woe betide any poor newbie applicant who takes the instructions about other grant support at face value and omits any funding that can be easily found on funder websites or the investigator’s lab or University splash page. Reviewers will get in a high dudgeon if they think the PI is trying to conceal anything about their research support. This is, I will assert, because they either overtly or covertly are interested in two things. Neither of which the NIH wants them to be interested in.

One, does the PI have “too much money” in their estimation. The NIH is absolutely opposed to reviewers letting their evaluation of proposal merit be contaminated with such concerns but….people are people and jealously reigns supreme. As does self-righteous feelings about how NIH funds should be distributed. So…review, in practice, is biased in a way that the NIH does not like.

The second concern is related, but is about productivity and is therefore slightly more palatable to some. If the recitation of funding is selective, the PI might be motivated to only present projects that have been the most productive or led to the most Glammy papers. They might be also motivated to omit listing any project which have, by some views, under-produced. This is a tricky one. The instructions say reviewers will look at what the PI chooses to list on the Biosketch as evidence of their overall qualifications. But. How can a reviewer assess qualifications only from the projects that went amazingly well without also assessing how many tanked, relatively speaking? Or so would think a reviewer. The NIH is a little more wobbly on this one. “Productivity” is a sort-of tolerated thing and some analysis of papers-per-grant-dollar (e.g. from NIGMS) show their interest, at least from a Program policy perspective. But I think overall that Program does not want this sort of reviewer bean counting to contaminate merit review too much- the Biosketch instructions insist that the direct costs should not be included for any grants that are mentioned. Program wants to make the calls about “too much money”.

Ok so why am I blogging this again today? Well, we’re into the second year of the new, new attempt of NIH to get the list of grants on the Biosketch more selective. And I’m thinking about how this has been evolving in grants that I’ve been asked to review. Wait..”more selective”? Oh yes, the list of grants can now be added to Section A, the Personal Statement. With all of the same language about how this is only for ongoing or completed projects “that you want to draw attention to“. NOT-OD-21-073 even ties this new format description to the re-organization of the Other Support page, again making it clear that these are not the same thing.

So the question of the day is, how are applicants responding? How are reviewers reacting to various options taken by applicants?

I put in my first few applications with the grant list simply removed. I added a statement to Section A summarizing my total number of intervals of competitive support as PI and left it at that. But I’ve seen many applicants who put all their grants in Section A, just as they would have put them in Section D before.

I guess I had better do the same?

Reconsidering “Run to Daylight” in the Context of Hoppe et al.

In a prior post, A pants leg can only accommodate so many Jack Russells, I had elucidated my affection for applying Vince Lombardi’s advice to science careers.

Run to Daylight.

Seek out ways to decrease the competition, not to increase it, if you want to have an easier career path in academic science. Take your considerable skills to a place where they are not just expected value, but represent near miraculous advance. This can be in topic, in geography, in institution type or in any other dimension. Work in an area where there are fewer of you.

This came up today in a discussion of “scooping” and whether it is more or less your own fault if you are continually scooped, scientifically speaking.

If yr getting "scooped" alla the time, it is perhaps worth interrogating how you choose what scientific lines of inquiry to pursue.

— Dr Jasmonyl Nitters (@mnitabach) January 7, 2022

He’s not wrong. I, obviously, was talking a similar line in that prior post. It is advisable, in a career environment where things like independence, creativity, discovery, novelty and the like are valued, for you NOT to work on topics that lots and lots of other people are working on. In the extreme, if you are the only one working on some topic that others who sit in evaluation of you see as valuable, this is awesome! You are doing highly novel and creative stuff.

The trouble is, that despite the conceits in study section review, the NIH system does NOT tend to reward investigators who are highly novel solo artists. It is seemingly obligatory for Nobel Laureates to complain about how some study section panel or other passed on their grant which described the plans to pursue what became the Nobel-worthy work. Year after year a lot of me-too grants get funded while genuinely new stuff flounders. The NIH has a whole system (RFAs, PAs, now NOSI) set up to beg investigators to submit proposals on topics that are seemingly important but nobody can get fundable scores to work on.

In 2019 the Hoppe et al. study put a finer and more quantitatively backed point on this. One of the main messages was the degree to which grant proposals on some topics had a higher success rate and some on other topics had lower success rates. You can focus on the trees if you want, but the forest is all-critical. This has pointed a spotlight on what I have taken to calling the inherent structural conservatism of NIH grant review. The peers are making entirely subjective decisions, particularly right at the might-fund/might-not-fund threshold of scoring, based on gut feelings. Those peers are selected from the ranks of the already-successful when it comes to getting grants. Their subjective judgments, therefore, tend to reinforce the prior subjective judgments. And of course, tend to reinforce an orthodoxy at any present time.

NIH grant review has many pseudo-objective components to it which do play into the peer review outcome. There is a sense of fair-play, sauce for the goose logic which can come into effect. Seemingly objective evaluative comments are often used selectively to shore up subjective, Gestalt reviewer opinions, but this is in part because doing so has credibility when an assigned reviewer is trying to convince the other panelists of their judgment. One of these areas of seemingly objective evaluation is the PI’s scientific productivity, impact and influence, which often touches on publication metrics. Directly or indirectly. Descriptions of productivity of the investigator. Evidence of the “impact” of the journals they publish in. The resulting impact on the field. Citations of key papers….yeah it happens.

Consideration of the Hoppe results, the Lauer et al. (2021) description of the NIH “funding ecology” in the light of some of the original Ginther et al. (2011, 2018) investigation into the relationship of PI publication metrics is relevant here.

Publication metrics are a function of funding. The number of publications a lab generates depend on having grant support. More papers is generally considered better, fewer papers worse. More funding means an investigator has the freedom to make papers meatier. Bigger in scope or deeper in converging evidence. More papers means, at the very least, a trickle of self-cites to those papers. More funding means more collaborations with other labs…which leads to them citing both of you at once. More funding means more trainees who write papers, write reviews (great for h-index and total cites) and eventually go off to start their own publication records…and cite their trainee papers with the PI.

So when the NIH-generated publications say that publication metrics “explain” a gap in application success rates, they are wrong. They use this language, generally, in a way that says Black PIs (the topic of most of the reports, but this generalizes) have inferior publication metrics so this causes a lower success rate. With the further implication that this is a justified outcome. This totally ignores the inherent circularity of grant funding and publication measures of awesomeness. Donna Gither has written a recent reflection on her work on NIH grant funding disparity, which doubles down on her lack of understanding on this issue.

Publication metrics are also a function of funding to the related sub-field. If a lot of people are working on the same topic, they tend to generate a lot of publications with a lot of available citations. Citations which buoy up the metrics of investigators who happen to work in those fields. Did you know, my biomedical friends, that a JIF of 1.0 is awesome in some fields of science? This is where the Hoppe and Lauer papers are critical. They show that not all fields get the same amount of NIH funding, and do not get that funding as easily. This affects the available pool of citations. It affects the JIF of journals in those fields. It affects the competition for limited space in the “best” journals. It affects the perceived authority of some individuals in the field to prosecute their personal opinions about the “most impactful” science.

That funding to a sub-field, or to certain approaches (technical, theoretical, model, etc, etc) has a very broad and lasting impact on what is funded, what is viewed as the best science, etc.

So is it good advice to “Run to daylight”? If you are getting “scooped” on the regular is it your fault for wanting to work in a crowded subfield?

It really isn’t. I wish it were so but it is bad advice.

Better advice is to work in areas that are well populated and well-funded, using methods and approaches and theoretical structures that everyone else prefers and bray as hard as you can that your tiny incremental twist is “novel”.

The SABV Camel and the NIH tent flap

You know the old story.

In this new story, we have the NIH’s Sex As a Biological Variable (SABV) policy. When first discussed, just about everyone who took this seriously pointed out the problem of a zero sum, limited funding system adopting a mandate which would double the animal costs. To really consider SABV properly, we said, this is going to double our sample sizes…at the very least. Probably more than double.

That is coming from the perspective of a scientist who works with units of the whole experimental animal. There are many of us.

The official NIH response was a bunch of gaslighting.

“Oh no”, went the policy mavens of the NIH, “this is not what this means at all. Simply include equal numbers of male and female animals at your regular sample size. That’s it. Oh, yeah, you have to say you will stratify your data by sex and look at it. You know, just in case there’s anything there. But nothing insists you have to double your sample size.”

Sure, said we NIH watchers/applicants. Sure it will go like that. Have you met our reviewers? They are going to first of all demand that every study is fully powered to detect any sex difference. Then, they are going to immediately start banging on about swabbing and cycling the female rats and something something about powering up for cycle as well.

NIH: “No, of course not that would never happen why we will tell them not to do that and everything will be copacetic”

Things were not copacetic. As predicted, reviewers of grants have, since even before the mandate went into effect, demonstrated they are constitutionally unable to do what NIH claimed they should be doing and in fact do what they were predicted in advance to do. Make everything HAVE to be a sex differences study and HAVE to be a study of estrous cycle. Randomly. Variable. Yes. As with everything in NIH review. And who knows, maybe this is a selective cudgel (I call it Becca’s Bludgeon) used only when they just generally dislike the proposal.

The NIH mandate let the SABV camel’s nose under the tentflap and now that camel is puuuuuuuuussssshhhhing all the way in.

A new article in eLife by Garcia-Sifuentes and Maney is part of this campaign. It is chock full of insinuations and claims trying to justify the full camel in side the tent. Oh, they know perfectly well what the NIH policy was. But they are using all of the best #allegedprofession techniques to try to avoid admitting they are fully doing an end run.

From the Abstract: This new policy has been interpreted by some as a call to compare males and females with each other.

From the Intro: Although the NIH policy does not explicitly require that males and females be compared directly
with each other, the fact that more NIH-funded researchers must now study both sexes should lead to an increase in the frequency of such comparisons (insert self-citation). For example, there should be more testing for sex-specific
responses

“should”.

although the proportion of articles that included both sexes significantly increased (see also Will et al., 2017), the proportion that treated sex as a variable did not. [Note interesting goalpost move. or at least totally undefined insinuation] This finding contrasts sharply with expectations [whose “expectations” would those be?], given not only the NIH mandate but also numerous calls over the past decade to disaggregate all preclinical data by sex [yes, the mandate was to disaggregate by sex. correct.] and to test for sex differences [bzzzt, nope. here’s another slippery and dishonest little conflation]

One potential barrier to SABV implementation is a lack of relevant resources; for example, not all researchers have received training in experimental design and data analysis that would allow them to test for sex differences using appropriate statistical approaches. [oh what horseshit. sure, maybe there is a terrible lack of experimental design training. I agree those not trained in experimental psychology seem to be a bit lacking. But this is not specific to sex differences. A group is a group is a group. so is a factor. the “lack of relevant resources” is….money. grant money.]

any less-than-rigorous test for sex differences creates risk for misinterpretation of results and dissemination of misinformation to other scientists and to the public [There you have it. The entire NIH scheme to introduce SABV is not only flawed, it is, seemingly, even worse than doing nothing!]

Although a sex difference was claimed in a majority of articles (57%), not all of these differences were supported with statistical evidence. In more than a quarter of the articles reporting a sex difference, or 24/83 articles, the sexes were never actually compared statistically. [Yep, totally consistent with the assertions from NIH about what they were after. Anything else is a significant move of the goalposts. In the direction that was anticipated and EXPLICITLY denied as being the goal/end game by the NIH. In oh so many ways.]

In these cases, the authors claimed that the sexes responded differentially to a treatment when the effect of treatment was not statistically compared across sex. … Of the studies with a factorial design, 58% reported that the sexes responded differently to one or more other factors. The language used to state these conclusions often included the phrase ‘sex difference’ but could also include ‘sex-specific effect’ or that a treatment had an effect ‘in males but not females’ or vice versa. … Neither approach tests whether the treatment had different effects in females and males. Thus, a substantial majority of articles containing claims of sex-specific effects (70%) did not present statistical evidence to support those claims

This is also, utter a-scientific horseshit.

I get this a lot from reviewers so I’m going to expand but only briefly. There is no such thing as canonical statistical interpretation techniques that are either “right” or “wrong”. Nor do statistical inference techniques alter the outcome of a study. The data are what they are. All else is shades of interpretation. At the very best you could say that different inferential statistical outcomes may mean there is stronger or weaker evidence for your interpretations of the data. at best.

But there is a broader hypocrisy here. Do you only build your knowledge within the context of one paper? Do you assemble your head space on whether something is likely or unlikely to be a valid assertion (say, “female rats self-administer more cocaine”) ONLY on papers that provide like-to-like perfectly parallel and statistically compared groups?

If you are an idiot, I suppose. Not being an idiot, I assert that most scientists build their opinions about the world of science that they inhabit on a pile of indirectly converging evidence. Taking variability in approach into account. Stratifying the strength of the evidence to their best ability. Weighting the results. Adding each new bit of evidence as they come across it.

And, in a scenario where 10 labs were conducting cocaine self-administration studies and five each tended to work on males and females independently, we would conclude some things. If we were not preening Experimental Design Spherical Cow 101 idiots. If, for example, no matter the differences in approach it appeared that in aggregate the females self-administered twice as many infusions of the cocaine.

We would consider this useful, valid information that gives us the tentative idea that perhaps there is a sex difference. We would not hold our hands over our eyes mumbling “blah blah blah I can’t hear you either” and insist that there is zero useful indication from this true fact. We would, however, as we do with literally every dataset, keep in mind the limitations of our inferences. We might even use these prior results to justify a better test of the newly developed hypothesis, to overcome some of the limitations.

That is how we build knowledge.

Not by insisting if a comparison of datasets/findings does not accord with strict ideas of experimental design rigor, it is totally invalid and meaningless.

Among the articles in which the sexes were pooled, the authors did so without testing for a sex difference almost half of the time (48%; Figure 3B). When authors did test for a sex difference before pooling, they sometimes found a significant difference yet pooled the sexes anyway; this occurred in 17% of the articles that pooled.[Yes, consistent with the NIH policy. Again with the moving the goalposts….]

Thus, the authors that complied with NIH guidelines to disaggregate data usually went beyond NIH guidelines to explicitly compare the sexes with each other. [hookay…..so where’s the problem? isn’t this a good thing?]

Making sense of the T32 effect on the NIH funding disparity

A new blog post from Mike Lauer, Deputy Director for Extramural Research at the NIH, presents new data on grant award (K and R01equivalent) to applicants assuming they had a prior appointment on a NIH funded T32 training grant. This particular post included some demographic analysis which addressed URM vresus majoritarian status. Leaving aside for a moment the obvious concerns about who gets selected for T32 appointments and where and with whom they are correspondingly training, I was curious about the relative advantage.

Now, the first thing you will recognize is that the critical data are collapsed across all URM groups, although this post doesn’t seem to specify if Asian is included in non-URM, the total N in Table 1 and Table 3 suggest this is the case. This is a little disappointing of course, since the original Ginther report found such striking differences in award rates across URM groups. There was an Asian PI disparity and a Hispanic PI lackthereof, so would it not be super important to keep these groups separate for this post-T32 analysis? Of course it would.

But what got me scratching my head was the presentation of the percentages in Table 3. The show, for example, that 12.9% of non-URM T32 trainees eventually became PI on a funded R01 whereas only 9.8% of URM trainees became PI on a funded R01. That’s all well and good but it obscures the success rate because it doesn’t account for the differential submission rate (24.5% of nonURM T32 trainees eventually submitted an R01, 21.8% of URM trainees).

Doing a little math here, I make it out to be a 52.9% award rate for non-URM applicants and a 45.1% rate for URM applicants. This is a 7.8 percentage point differential which means the URM applicants have a success rate that is 85.3% of the non-URM applicant success rate.

Now we can put this in familiar terms. The Hoppe report found that Black applicants had a success rate that was 60.5% of that enjoyed by white applicants (it was 60.4 in Ginther et al, 2011). So if we take this 24.8 percentage point differential and divide it by the 39.5 percentage point deficit for Black applicants in the Hoppe sample…we end up with about 63% of the Black/white gap reported in Hoppe. The combination of T32 training history, combining of all URM together and adding Asian PIs to the non-URM group closes 63% of the gap.

See my problem here? We want all this broken down so we can answer the most simple question, does T32 training close the gap between Black and white PIs and if so to what extent.

THEN we can go on to ask about other URM populations and the effect of adding the Asian* T32 participants to the non-URM pile.

*reminder that Ginther found that applications with Asian PIs with domestic doctorates had success rates similar to those with white PIs. There is a major complication here with T32 eligibility for Asian-Americans versus Asians who were not T32 eligible as postdocs.

NOSI for URM Investigators: Choose Wisely

I was recently describing Notice NOT-NS-21-049 Notice of Special Interest (NOSI): NIH Research Project Grant (R01) Applications from Individuals from Diverse Backgrounds, Including Under-Represented Minorities in the context of the prior NIH Director’s comments that ESI scores got worse after the news got around about relaxed paylines.

One thing that I had not originally appreciated was the fact that you are only allowed to put one NOSI in Box 4b.

Which means that you have to choose. If you qualify as an individual from diverse backgrounds you could use this, sure. But that means you cannot use a NOSI that is specific to the topic you are proposing.

This is the usual NIH blunder of stepping on their own junk. How many ways can I count?

Look, the benefit of NOSI (and the predecessor, the Program Announcement) is uncertain. It seemingly only comes into play when some element of Program wishes to fund an award out of the order of review. Wait, you say, can’t they just do that anyway for whatever priority appears in the NOSI? Yes, yes they can….when it comes to the topic of the grant. So why do NOSI exist at all?

Well…one presumes it is because elements of Program do not always agree on what should be funded out of order of review. And one presumes there is some sort of conflict resolution process. During which the argument that one grant is related to the Programmatic Interest formally expressed in the NOSI has some weight or currency. Prioritizing that grant’s selection for funding over the identically-percentiled grant that does not mention a NOSI.

One still might wonder about a topic that fits the NOSI but doesn’t mention the NOSI directly. Well, the threat language at the bottom of some of those NOSI, such as oh I don’t know this one, is pretty clear to me.

• For funding consideration, applicants must include “NOT-DA-21-006” (without quotation marks) in the Agency Routing Identifier field (box 4B) of the SF424 R&R form. Applications without this information in box 4B will not be considered for this initiative.

Applications nonresponsive to terms of this NOSI will not be considered for the NOSI initiative.

So what is a PI to do? Presumably the NOSI has some non-negligible value and everyone is motivated to use those if possible. Maybe it will be the difference between a grey zone pickup and not, right? If your ideas for this particular grant proposal fit with something that your favorite IC has gone to the trouble if mentioning in a NOSI…well….dang it….you want to get noticed for that!

So what can you do if you are a person underrepresented who qualifies for the NOSI NOT-NS-21-049 ? The value of this one is uncertain. The value of any other NOSI for your particular application is likewise uncertain. We know perfectly well the NIH as a whole is running scared of right wing political forces when it comes to picking up grants. We know that this NOSI may be related to the well meaning ICs’ staff having difficulty getting PI demographic information and could simply be a data collection strategy for them.

Cynical you say? Well I had a few exchanges with a fairly high up Program person who suggested to me that perhaps the strategy was to sneak the “extra” NOSI into the Abstract of the proposal. This would somehow get it in front of Program eyes. But….but….there’s the “will not be considered” boilerplate. Right? What does this mean? It is absolutely maddening for PIs who might like to take advantage of this new NOSI which one might think would be used to fix the Ginther Gap. It is generally enraging for anyone who wants to see the Ginther Gap addressed.

It makes me positively incandescent to contemplate the possibility that the mere announcing of this NOSI will lead to study sections giving even worse scores to those applications, without any real assistance coming from Program.

A couple of more thoughts. This doesn’t apply to anything other than an R01 application, which is nuts. Why not apply it to all investigator initiated mechanisms? Trust me, underrepresented folks would like a leg up on R21 and R03 apps as well. These very likely help with later R01 getting, on a NIH wide statistical basis. You know, the basis of the Ginther Gap. So why not include other mechs?

And did you notice that no other ICs have joined? NINDS issued the NOT on May 3 and they were rapidly joined by NIDA (May 6) and NIAAA (May 11). All in good time for the June 5 and July 5 submission rounds. Since then….crickets. No other ICs have joined in. Weird, right?

I was on a Zoom thing awhile back where a highly authoritative Program person claimed that the Office of the Director (read: Francis Collins) had put a hold on any more ICs joining the NINDS NOSI.

Why? Allegedly because there was a plan to make this more general, more NIH wide all at one fell swoop.

Why? Who the heck knows. To cover up the reluctance of some of the ICs that would not be joining the NOSI if left up to their own devices? If so, this is HORRENDOUS, especially give the above mentioned considerations for choosing only one NOSI for Box 4b. Right? If they do extend this across all NIH, how would any PI know that their particular IC has no intention whatsoever of using this NOSI to fund some grants? So maybe they choose to use it, for no help, while bypassing another NOSI that might have been of use to them.

NINDS Issues NOSI Requesting Applications from URM Investigators

Notice NOT-NS-21-049 Notice of Special Interest (NOSI): NIH Research Project Grant (R01) Applications from Individuals from Diverse Backgrounds, Including Under-Represented Minorities was released on May 3, 2021.

The NOSI is the new Program Announcement, for those who haven’t been keeping track. As with the old PA the direct benefit is not obvious. There is no set aside funding or promise to fund any applications at all. In the context of Ginther et al 2011, Hoppe et al 2019, the discussions of 2020 and the overall change in tone from the NIH on diversity matters, it is pretty obvious that this is designed merely to be the excuse. This PA, sorry NOSI, is what will permit Program to pick up grants on the (partial?) basis of the PI’s identity.

What identities? Well the NOSI re-states the categories A, B and C that are familiar from other similar documents.

A. Individuals from racial and ethnic groups that have been shown by the National Science Foundation to be underrepresented in health-related sciences on a national basis…

B. Individuals with disabilities, who are defined as those with a physical or mental impairment that substantially limits one or more major life activities

C. Individuals from disadvantaged backgrounds, defined as those who meet two or more of the following criteria…

And then there is a statement about gender intersectionality to close out.

GREAT! Right?

Yeah, it is. To the extent this is used to figure out a way to start working the naked, quota based, top down, heavy handed affirmative action that has been benefiting ESI applicants since 2007 on the NIH funding disparity identified in Ginther and Hoppe, this is a win. From the very start of hearing about Ginther I‘ve been talking about exception pay rapid solutions and this only heated up with the disingenuous claim that exception pay was not accelerating the disparity which was made in Hoppe et al. The NOSI allows pickups/exception pay, for sure, under the “special interest” idea. I don’t know if this will end up generating explicit payline benefits on a universal categorical basis as has been done at many ICs for ESI applications. The difference, of course, is that the former is much more variable and subject to biases that are expressed by Program Officers individually and collectively. Explicit rules would be better…..ish. It’s complicated.

In a news piece by Jocelyn Kaiser, the prior NIH Director Elias Zerhouni was quoted saying that study sections responded to his 2006/2007 ESI push by “punishing the young investigators with bad scores”. As I have tried to explain numerous times, phrasing this as a matter of malign intent on the part of study section members is a mistake. While it may be true that many reviewers opposed the idea that ESI applicants should get special breaks, adjusting scores to keep the ESI application at the same chances as before Zerhouni’s policies took effect is just a special case of a more general phenomenon.

NIH grant reviewers have a pronounced tendency to review grant proposals with an eye to “fund it” versus “don’t fund it”. Continual exhortations from SROs that panels do not make funding decisions, that they should review merit on a mostly continuous scale and that they should spread scores has minimal impact on this. Reviewers have a general idea of what scores will result in funding and what will not and they score accordingly*. I have mentioned that when I first started on study section the SRO would actually send us score distributions as part of the effort to get us to spread scores. INVEVITABLY the scores would stack up around the perceived funding line. Across a couple of years one could even see this move in tandem (with a round or two lag, obv) with what was funding at the 2-3 ICs that most grants were assigned to.

One interpretation of the “punishing” phenomenon is simply that panels were doing what they always do (as I assert anyway) in matching scoring to their perception of the payline and their gut feeling about whether a given app was deserving. What this assumes, of course, is that whatever biases were keeping the applications of the ESI-qualifying individuals from getting good scores in the past were still present and the reviewers were simply continuing the same behavior.

My concern with the new NOSI is, of course, that something similar will happen with the applications that qualify for this NOSI. There is the potential for a growing general suspicion (assumption? bias?) among study section reviewers that “oh, those URM PIs get special breaks” and then the scores will get even worse than they were before this idea started to percolate around the culture. It might be accelerated if the ICs generate explicit statements of relaxed paylines…but the campfire chatter about how the NOSI is being used will be sufficient.

Vigilance!

Vigilance is the thing. NIH cannot be permitted to put this in place, pay no attention to the results and “suddenly realize” five or ten years later that things are not working according to design.

__

*generally. Reviewers can be mistaken about paylines. They can be miscalibrated about scores and percentiles. They have a limited picture which only reflects their own knowledge of what is being funded. But still, there is an aggregate effect.

Joe Biden, Hunter Biden, Stimulant Use Disorders and ARPA-H

There was a press release issued by NIDA today, trumpeting a report of a Phase III trial of medication for methamphetamine use disorders. As a very brief recap, we have no approved medications to assist with methamphetamine (or any psychostimulant) use disorder and the distressing health consequences for addiction to cocaine, methamphetamine and some other related drugs are very severe. According to 2019 data from SAMHSA (Table 5.14A), some 534,000 folks over age 25 were in treatment for cocaine use disorder, 566,000 for methamphetamine and this contrasts with 594,000 for heroin, 552,000 for marijuana and 1,957,000 for alcohol. Table 5.1A points out that some 756,000 of folks in this range probably had a cocaine use disorder, 904,000 had a methamphetamine use disorder and 10,959,000 had an alcohol use disorder.

Not everyone who needs treatment gets it. Not even close.

Hunter Biden, the President’s second (and still living) son has recently published a memoir detailing his struggles with addiction to cocaine and alcohol. Joe Biden, the President, issued a recent press release calling for a $6.5 billion launch of an Advanced Research Projects Agency for Health (ARPA-H) within the National Institutes of Health.

With an initial focus on cancer and other diseases such as diabetes and Alzheimer’s, this major investment in federal research and development will drive transformational innovation in health research and speed application and implementation of health breakthroughs.

Notably missing is any prominent mention of substance use disorder in ARPA-H.

On the relative scale of progress in treating cancer and diabetes, and yes even Alzheimer’s, I would argue that treatments for substance use disorders have been woefully under researched. Funding has lagged for the development of treatments and medications both in the public and private sectors. This means novel discovery, of course, but the real glaring deficit is in the routine churning of clinical trial after clinical trial for evaluating pretty run of the mill stuff. As they did in this recent Phase III trial.

Methamphetamine, as they say, is a helluva drug. From Brecht and Herbeck, 2014, we see the following relapse survival curve for a sample of methamphetamine users admitted to the Los Angeles County substance use treatment system. the followup period ranged from 22-90 months over which 23% maintained abstinence from methamphetamine. That means 77% relapsed, with a range of 0-79 months until relapse. As you can see from the below, 36% returned to methamphetamine use immediately upon discharge (Nb, this is not a sample selected for desire to quit), 14% more relapsed by 6 months and a total of 61% had relapsed within a year of entry. The good news, if there is any, is that this should be low hanging fruit. Anything, anything at all, that seems to work will be a huge gain versus the situation at present.

The new trial conducted by Trivedi et al. found that depot injection of naltrexone combined with daily oral buproprion (a cathinone-derivative, aka “bathsalt”) was effective, versus placebo control, in treating methamphetamine use disorder.

“Effective”.

Meaning that within a population of methamphetamine users with “moderate to severe” use disorder who intended to quit, 11.4% responded. Where a response was 3 out of four urine samples negative for methamphetamine during weeks 11-12. Only 1.8% in the placebo group had this “response”. Let’s round that out to 10% efficacy.

Now, the glass is most emphatically half full. Ten percent is not very impressive sounding but it is something. It is some improvement for some folks. Ten percent of the ~904,000 estimated with a methamphetamine use disorder is a lot of people and their families that have improved lives. We are moved by the stories of single individuals- like Hunter Biden, and Nic Sheff and William C. Moyers. Let us apply that same empathy we feel for these men and their relative success at recover to each and every other person with a stimulant use disorder.

And we have nowhere to go but up, with discovery of any additional strategies that, btw, likely will also help with cocaine use disorder.

Do we need DARPA-like innovation? of course. Anti-drug vaccines (something I’ve worked on, for disclosure) have been languishing in a twilight of basic biological efficacy but need a big kick in the pants to advance to real-world efficacy. Wearable technology has several immediately imaginable future uses. Deep brain stimulation. TMS. Individualized therapy based on genotyping. There is no reason to think that we could not go big with ARPA-H for substance use.

It is more than a little bothersome that Joe Biden, who so explicitly ties his interest in Cancer Moonshots and the like to the fate of his older son, does not exhibit the same motivations for the trials of his younger son. Who, btw, is not dead and is at continual risk of relapse given his history.

Trivedi MH, et al. Trial of Bupropion and Naltrexone in Methamphetamine Use Disorder. New England Journal of Medicine. January 14, 2020.

NIH tries, again, to keep grant reviewers from “too much money” bias

The recent NOT-OD-21-073 Upcoming Changes to the Biographical Sketch and Other Support Format Page for Due Dates on or after May 25, 2021 indicates one planned change to the Biosketch which is both amusing and of considerable interest to us “process of NIH” fans.

For the non-Fellowship Biosketch, Section D. has been removed. … As applicable, all applicants may include details on ongoing and completed research projects from the past three years that they want to draw attention to within the personal statement, Section A.

Section D is “Additional Information: Research Support and/or Scholastic Performance“. The prior set of instructions read:

List ongoing and completed research projects from the past three years that you want to draw attention to. Briefly indicate the overall goals of the projects and your responsibilities. Do not include the number of person months or direct costs.

And if the part about “want to draw attention to” was not clear enough they also added:

Do not confuse “Research Support” with “Other Support.” Other Support information is not collected at the time of application submission.”

Don’t answer yet, there’s more!

Research Support: As part of the Biosketch section of the application, “Research Support” highlights your accomplishments, and those of your colleagues, as scientists. This information will be used by the reviewers in the assessment of each your qualifications for a specific role in the proposed project, as well as to evaluate the overall qualifications of the research team.

This is one of those areas where the NIH intent has been fought bitterly by the culture of peer review, in my experience (meaning in my ~two decades of being an applicant and slightly less time as a reviewer). These policy positions, instructions, etc and the segregation of the dollars and all total research funding into the Other Support documentation make it very clear to the naive reader that the NIH does not want reviewers contaminating their assessment of the merit of a proposal with their own ideas about whether the PI (or other investigators) have too much other funding. They do not want this at all. It is VERY clear and this new update to the Biosketch enhances this by deleting any obligatory spot where funding information seemingly has to go.

But they are paddling upstream in a rushing, spring flood, rapids Cat V river. Good luck, say I.

Whenever this has come up, I think I’ve usually reiterated the reasons why a person might be motivated to omit certain funding from their Biosketch. Perhaps you had an unfortunate period of funding that was simply not very productive for any of a thousand reasons. Perhaps you do have what looks to some eyes like “too much funding” for your age, tenure, institution type, sex or race. Or for your overall productivity level. Perhaps you have some funding that looks like it might overlap with the current proposal. Or maybe even funding from some source that some folks might find controversial. The NIH has always (i.e. during my time in the system) endorsed your ability to do so and the notion that these consideration should not influence the assessment of merit.

I have also, I hope consistently, warned folks not to ever, ever try to omit funding (within the past three years) from their Biosketch, particularly if it can be found in any way on the internet. This includes those foundation sites bragging about their awards, your own lab website and your institutional PR game which put out a brag on you. The reason is that reviewers just can’t help themselves. You know this. How many discussions have we had on science blogs and now science twitter that revolve around “solutions” to NIH funding stresses that boil down to “those guys over there have too much money and if we just limit them, all will be better”? Scores.

Believe me, all the attitudes and biases that come out in our little chats also are present in the heads of study section members. We have all sorts of ideas about who “deserves” funding. Sometimes these notions emerge during study section discussion or in the comments. Yeah, reviewers know they aren’t supposed to be judging this so it often come up obliquely. Amount of time committed to this project. Productivity, either in general or associated with specific other awards. Even ones that have nothing to do with the current proposal.

My most hilariously vicious personal attack summary statement critique ever was clearly motivated by the notion that I had “too much money”. One of the more disgusting aspects of what this person did was to assume incorrectly that I had a tap on resources associated with a Center in my department. Despite no indication anywhere that I had access to substantial funds from that source. A long time later I also grasped an even more hilarious part of this. The Center in question was basically a NIH funded Center with minimal other dollars involved. However, this Center has what appear to be peer Centers elsewhere that are different beasts entirely. These are Centers that have a huge non-federal warchest involving more local income and an endowment built over decades. With incomes that put R21 and even R01 money into individual laboratories that are involved in the Center. There was no evidence anywhere that I had these sorts of covert resources, and I did not. Yet this reviewer felt fully comfortable teeing off on my for “productivity” in a way that was tied to the assumption I had more resources than were represented by my NIH grants.

Note that I am not saying many other reviews of my grant applications have not been contaminated by notions that I have “too much”. At times I am certain they were. Based on my age at first. Based on my institution and job type, certainly. And on perceptions of my productivity, of course. And now in the post-Hoppe analysis….on my race? Who the fuck knows. Probably.

But the evidence is not usually clear.

What IS clear is that reviewers, who are your peers with the same attitudes they express around the water cooler, on average have strong notions about whether PIs “deserve” more funding based on the funding they currently have and have had in the past.

NIH is asking, yet again, for reviewers to please stop doing this. To please stop assessing merit in a way that is contaminated by other funding.

I look forward with fascination to see if NIH can managed to get this ship turned around with this latest gambit.

The very first evidence will be to monitor Biosketches in review to see if our peers are sticking with the old dictum of “for God’s sake don’t look like you are hiding anything” or if they will take the leap of faith that the new rules will be followed in spirit and nobody will go snooping around on RePORTER and Google to see if the PI has “too much funding”.

Thoughts on the NIH policy on SABV

There is a new review by Shansky and Murphy out this month which addresses the NIH policy on considering sex as a biological variable (SABV).

Shansky, RM and Murphy, AZ. Considering sex as a biological variable will require a global shift in science culture. Nat Neurosci, 2021 Mar 1. doi: 10.1038/s41593-021-00806-8. Online ahead of print.

To get this out of the way, score me as one who is generally on board with the sentiments behind SABV and one who started trying to change my own approach to my research when this first started being discussed. I even started trying to address this in my grant proposals several cycles before it became obligatory. I have now, as it happens, published papers involving both male and female subjects and continue to do so. We currently have experiments being conducted that involve both male and female subjects and my plan is to continue to do so. Also, I have had many exchanges with Dr. Shansky over the years about these issues and have learned much from her views and suggestions. This post is going to address where I object to things in this new review,for the most part, so I thought I should make these declarations, for what they are worth.

In Box 1, the review addresses a scientist who claims that s/he will first do the work in males and then followup in females as follows:

“We started this work in males, so it makes sense to keep going in males. We will follow up with females when this project is finished.” Be honest, when is a project ever truly finished? There is always another level of ‘mechanistic insight’ one can claim to need. Playing catch-up can be daunting, but it is better to do as much work in both sexes at the same
time, rather than a streamlined follow-up study in females years after the original male work was published. This latter approach risks framing the female work as a lower-impact ‘replication study’ instead of equally valuable to scientific knowledge.

This then dovetails with a comment in Box 2 about the proper way to conduct our research going forward:

At the bare minimum, adhering to SABV means using experimental cohorts that include both males and females in every experiment, without necessarily analyzing data by sex.

I still can’t get past this. I understand that this is the place that the NIH policy on SABV has landed. I do. We should run 50/50 cohorts for every study, as Shansky and Murphy are suggesting here. I cannot for the life of me see the logic in this. I can’t. In my work, behavioral work with rats for the most part, there is so much variability that I am loathe to even run half-size pilot studies. In a lot of the work that I do, N=8 is a pretty good starting size for the minimal ability to conclude much of anything. N=4? tough, especially as a starting size of the groups.

The piece eventually gets around to the notion of how we enforce the NIH SABV policy. As I have pointed out before and as is a central component of this review, we are moving rapidly into a time when the laboratories who claim NIH support for their studies are referencing grant proposals that were submitted under SABV rules.

NOT-OD-15-102 appeared in June of 2015 and warned that SABV policy would “will take effect for applications submitted for the January 25, 2016, due date, and thereafter“. Which means grants to be reviewed in summer 2016, considered at Council in the Fall rounds and potentially funded Dec 1, 2016. This means, with the usual problems with Dec 1 funding dates, that we are finishing up year 4 of some of these initial awards.

One of the main things that Shansky and Murphy address is in the section “Moving forward-who is responsible?“.

whether they have [addressed SABV] in their actual research remains to be seen. NIH grants are nonbinding, meaning that awardees are not required to conduct the exact experiments they propose. Moreover, there is no explicit language from NIH stating that SABV adherence will be enforced once the funds are awarded. Without accountability measures in place, no one is prevented from exclusively using male subjects in research funded under SABV policies.

Right? It is a central issue if we wish to budge the needle on considering sex as a biological variable. And the primary mechanism of enforcement is, well, us. The peers who are reviewing the subsequent grant applications from investigators who have been funded in the SABV era. The authors sortof mention this: “Researchers should be held accountable by making documentation of SABV compliance mandatory in yearly progress reports and by using compliance as a contingency for grant renewals (both noncompetitive and competitive).” Actually, the way this is structured, combined with the following sentence about manuscript review, almost sidesteps the critical issue. I will not sidestep in this post.

We, peer scientists who are reviewing the grant proposals, are the ones who must take primary responsibility to assess whether a PI and associated Investigators have made a good faith attempt to follow/adopt SABV policy or not. Leaving this in the hands of Program to sort out, based on tepid review comments, is a dereliction of duty and will result in frustrating variablity of review that we all hate. So….we are the ones who will either let PIs off the hook, thereby undercutting everything NIH has tried to accomplish, or we will assist NIH by awarding poor scores to applications with a team that has not demonstrably taken SABV seriously. We are at a critical and tenuous point. Will PIs believe that their grants will still be funded with a carefully crafted SABV statement, regardless of whether they have followed through? Or will PIs believe that their grant getting is in serious jeopardy if they do not take the spirit of the SABV policy to heart? The only way this is decided is if the peer review scores reward those who take it seriously and punish those who do not.

So now we are back to the main point of this post which is how we are to assess good-faith efforts. I absolutely agree with Shansky and Murphy that an application (competing or not) that basically says “we’re going to follow up in the females later“, where later means “Oh we didn’t do it yet, but we pinky swear we will do it in this next interval of funding” should not be let off the hook.

However. What about a strategy that falls short of the “bare minimum”, as the authors insist on in Box 2, of including males and females in 50/50 proportion in every experiment, not powered to really confirm any potential sex difference?

I believe we need a little more flexibility in our consideration of whether the research of the PI is making a good faith effort or not. What I would like to see is simply that male and female studies are conducted within the same general research program. Sure, it can be the 50/50 group design. But it can also be that sometimes experiments are in males, sometimes in females. Particularly if there is no particular sense that one sex is always run first and the other is trivially “checked, or that one sex dominates the experimental rationale. Pubs might include both sexes within one paper, that’s the easiest call, but they might also appear as two separate publications. I think this can often be the right approach, personally.

This will require additional advocacy, thinking, pushback, etc, on one of the fundamental principles that many investigators have struggled with in the SABV era. As is detailed in Box 1 and 2 of the review, SABV does not mean that each study is a direct study of sex differences nor that every study in female mammals becomes a study of estrous cycle / ovarian hormones. My experience, as both an applicant and a reviewer, is that NIH study section members often have trouble with this notion. There has not been, in my experience on panels, a loud and general chorus rebutting any such notions during discussion either, we have much ground still to cover.

So we will definitely have to achieve greater agreement on what represents a good faith effort on SABV, I would argue, if we are to advocate strongly for NIH study sections to police SABV with the firm hand that it will require.

I object to what might be an obvious take-away from Shansky and Murphy, i.e., that the 50/50 sample approach is the essential minimum. I believe that other strategies and approaches to SABV can be done which both involve full single-sex sample sizes and do not require every study to be a direct contrast of the sexes in an experimentally clean manner.

A lesson for DEI strategies from the NIH ESI policy

The Director of the NIH, in the wake of a presentation to the Advisory Committee to the Director meeting, has issued a statement of NIH’s commitment to dismantle structural racism.

Toward that end, NIH has launched an effort to end structural racism and racial inequities in biomedical research through a new initiative called UNITE, which has already begun to identify short-term and long-term actions. The UNITE initiative’s efforts are being informed by five committees with experts across all 27 NIH institutes and centers who are passionate about racial diversity, equity, and inclusion. NIH also is seeking advice and guidance from outside of the agency through the Advisory Committee to the Director (ACD), informed by the ACD Working Group on Diversity, and through a Request for Information (RFI) issued today seeking input from the public and stakeholder organizations. The RFI is open through April 9, 2021, and responses to the RFI will be made publicly available. You can learn more about NIH’s efforts, actions, policies, and procedures via a newly launched NIH webpage on Ending Structural Racism aimed at increasing our transparency on this important issue.

This is very much welcome, coming along as it does, a decade after Ginther and colleagues showed that Black PIs faced a huge disadvantage in getting their NIH grants funded. R01 applications with Black PIs were funded at only 58% of the rate that applications with white PIs were funded.

Many people in the intervening years, accelerated after the publication of Hoppe et al 2019 and even further in the wake of the murder of George Floyd at the hands of the Minneapolis police in 2020, have wondered why the NIH does not simply adopt the same solution they applied to the ESI problem. In 2006/2007 the then-Director of NIH, Elias Zerhouni, dictated that the NIH would practice affirmative action to fund the grants of Early Stage Investigators. As detailed in Science by Jocelyn Kaiser

Instead of relying solely on peer review to apportion grants, [Zerhouni] set a floor—a numerical quota—for the number of awards made to new investigators in 2007 and 2008.

A quota. The Big Bad word of anti-equity warriors since forever. Gawd forbid we should use quotas. And in case that wasn’t clear enough

The notice states that NIH “intends to support new investigators at success rates comparable to those for established investigators submitting new applications.” In 2009, that will mean at least 1650 awards to new investigators for R01s, NIH’s most common research grant.

As we saw from Hoppe et al, the NIH funded 256 R01s with Black PIs in the interval from 2011-2015, or 51 per year. In a prior blog post I detailed how some 119 awards to poorer-scoring applications with white PIs could have been devoted to better-scoring proposals with Black PIs. I also mentioned how doing so would have moved the success rate for applications with Black PIs fro 10.7% to 15.6% whereas the white PI success rate would decline from 17.7% to 17.56%. Even funding every single discussed application with a Black PI (44% of the submissions) by subtracting those 1057 applications from the pool awarded with white PIs would reduce the latter applications’ hit rate only to 16.7% which is still a 56% higher rate than the 10.7% rate that the applications with Black PIs actually experienced.

I have been, and continue to be, an advocate for stop-gap measures that immediately redress the funding rate disparity by mandating at least equivalent success rates, just as Zerhouni mandated for ESI proposals. But we need to draw a key lesson from that episode. As detailed in the Science piece

Some program directors grumbled at first, NIH officials say, but came on board when NIH noticed a change in behavior by peer reviewers. Told about the quotas, study sections began “punishing the young investigators with bad scores,” says Zerhouni. That is, a previous slight gap in review scores for new grant applications from first-time and seasoned investigators widened in 2007 and 2008, [then NIGMS Director Jeremy] Berg says. It revealed a bias against new investigators, Zerhouni says.

I don’t know for sure that this continued, but the FY 2012 funding data published by Kienholz and Berg certainly suggest that several NIH ICs continued to fund ESI applications at much lower priority scores/percentiles than were generally required for non-ESI applications to receive awards. And if you examine those NIH ICs pages that publish their funding strategy each year [see the writedit blog for current policy links], you will see that they continue to use a lower payline for ESIs. So. From 2007 to 2021 that is a long interval of policy which is not “affirmative action”, just, in the words of Zerhouni, “leveling the playing field”.

The important point here is that the NIH has never done anything to get to the “real reason” for the fact that early stage investigators proposals were being scored by peer review at lower priority than they, NIH, desired. They have not undergone spasms of reviewer “implicit bias” training. They have not masked the identity of the PI or done anything suggesting they think they can “fix” the review outcome for ESI PIs.

They have accepted the fact that they just need to counter the bias.

NIH will likewise need to accept that they will need to fund Black PI applications with a different payline for a very long time. They need to accept that study sections will “punish*” those applications with even worse scores. They will even punish those applications with higher ND rates. And to some extent, merely by talking about it this horse has left the stall and cannot be easily recalled. We exist in a world where, despite all evidence, white men regularly assert with great confidence that women or minority individuals have all the advantages in hiring and career success.

So all of this entirely predictable behavior needs to be accounted for, expected and tolerated.

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*I don’t happen to see it as “punishing” ESI apps even further than whatever the base rate is. I think reviewers are very sensitive to perceived funding lines and to reviewing grants from a sort of binary “fund/don’t fund” mindset. Broadcasting a relaxed payline for ESI applications almost inevitably led to reviewers moving their perceived payline for those applications.

On targeting NIH funding opportunities to URMs: The Lauer edition

I have long standing doubts about certain aspects of funding mechanisms that are targeted to underrepresented individuals. This almost always has come up in the past in the context of graduate or postdoctoral fellowships and when there is a FOA open to all, and a related or parallel FOA that is directed explicitly at underrepresented individuals. For example see NINDS F31, K99/R00 , NIGMS K99/R00 initiatives, and there is actually a NIH parent F32 – diversity as well).

At first blush, this looks awesome! Targeted opportunity, presumably grant panel review that gives some minimal attention to the merits of the FOA and, again presumably, some Program traction to fund at least a few.

My Grinchy old heart is, however, suspicious about the real opportunity here. Perhaps more importantly, I am concerned about the real opportunity versus the opportunity that might be provided by eliminating any disparity of review that exists for the review of applications that come in via the un-targeted FOA. No matter the FOA, the review of NIH grants is competitive and zero sum. Sure, pools of money can be shifted from one program to another (say from the regular F31 to the F31-diversity) but it is rarely the case there is any new money coming in. Arguing about the degree to which funding is targeted by decision of Congress, of the NIH Director, of IC Directors or any associated Advisory Councils is a distraction. Sure NIGMS gets a PR hit from announcing and funding some MOSAIC K99/R00 awards…but they could just use those moneys to fund the apps coming in through their existing call that happen to have PIs who are underrepresented in science.

The extreme example here is the highly competitive K99 application from a URM postdoc. If it goes in to the regular competition, it is so good that it wins an award and displaces, statistically, a less-meritorious one that happens to have a white PI. If it goes in to the MOSAIC competition, it also gets selected, but in this case by displacing a less-meritorious one that happens to have a URM PI. Guaranteed.

These special FOA have the tendency to put all the URM in competition with each other. This is true whether they would be competitive against the biased review of the regular FOA or, more subtly, whether they would be competitive for funding in a regular FOA review that had been made bias-free(r).

I was listening to a presentation from Professor Nick Gilpin today on his thoughts on the whole Ginther/Hoppe situation (see his Feature at eLife with Mike Taffe) and was struck by comments on the Lauer pre-print. Mike Lauer, head of NIH’s office of extramural awards, blogged and pre-printed an analysis of how the success rates at various NIH ICs may influence the funding rate for AA/B PIs. It will not surprise you that this was yet another attempt to suggest it was AA/B PIs’ fault that they suffer a funding disparity. For the sample of grants reviewed by Lauer (from the Hoppe sample), 2% were submitted with AA/B PIs, NIH-wide. The percentage submitted to the 19 individual funding ICs he covered ranged from 0.73% to 14.7%. This latter institute was the National Institute on Minority Health and Health Disparities (NIMHD). Other notable ICs of disproportionate relevance to the grants submitted with AA/B PIs include NINR (4.6% AA/B applications) and NICHD (3%).

So what struck me, as I listened to Nick’s take on these data, is that this is the IC assignment version of the targeted FOA. It puts applications with AA/B investigators in higher competition with each other. “Yeahbutt”, you say. It is not comparable. Because there is no open competition version of the IC assignment.

Oh no? Of course there is, particularly when it comes to NIMHD. Because these grants will very often look like a grant right down the center of those of interest to the larger, topic-focused ICs….save that it is relevant to a population considered to be minority or suffering a health disparity. Seriously, go to RePORTER and look at new NIMHD R01s. Or heck, NIMHD is small enough you can look at the out year NIMHD R01s without breaking your brain since NIHMH only gets about 0.8% of the NIH budget allocation. With a judicious eye to topics, some related searches across ICs, and some clicking on the PI names to see what else they may have as funded grants, you can quickly convince yourself that plenty of NIMHD awards could easily be funded by a related I or C with their much larger budgets*. Perhaps the contrary is also true, grants funded by the parent / topic IC which you might also argue would fit at NIMHD, but I bet the relative percentage goes the first way.

If I am right in my suspicions, the existence of NIMHD does not necessarily put more aggregate money into health disparities research. That is, more than that which could just as easily come out of the “regular” budget. The existence of NIMHD means that the parent IC can shrug off their responsibility for minority health issues or disparity issues within their primary domains of drug abuse, cancer, mental health, alcoholism or what have you. Which means they are likewise shrugging off the AA/B investigators who are disproportionately submitting applications with those NIMHD-relevant topics and being put in sharp competition with each other. Competition not just within a health domain, but across all health domains covered by the NIH.

It just seems to me that putting the applications with Black PIs preferentially in competition with themselves, as opposed to making it a fair competition for the entire pool of money allocated to the purpose, is sub optimal.

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*Check out the descriptions for MD010362 and CA224537 for some idea of what I mean. The entire NIMHD budget is 5% as large as the NCI budget. Why, you might ask, is NCI not picking up this one as well?