On reviewing scientific work from known sexual harassers, retaliators, bigots and generalized jerks of science

(by drugmonkey) May 15 2018

On a recent post, DNAMan asks:

If you were reviewing an NIH proposal from a PI who was a known (or widely rumored) sexual harasser, would you take that into account? How?

My immediate answer was:

I don't know about "widely rumored". But if I was convinced someone was a sexual harasser this would render me unable to fairly judge the application. So I would recuse myself and tell the SRO why I was doing so. As one is expected to do for any conflicts that one recognizes about the proposal.

I'm promoting this to a new post because this also came up in the Twitter discussion of Lander's toast of Jim Watson. Apparently this is not obvious to everyone.

One is supposed to refuse to review grant proposals, and manuscripts submitted for publication, if one feels that one has a conflict of interest that renders the review biased. This is very clear. Formal guidelines tend to concentrate on personal financial benefits (i.e. standing to gain from a company in which one has ownership or other financial interest), institutional benefits (i.e., you cannot review NIH grants submitted from your University since the University is, after all, the applicant and you are an agent of that University) and mentoring / collaborating interests (typically expressed as co-publication or mentoring formally in past three years). Nevertheless there is a clear expectation, spelled out in some cases, that you should refuse to take a review assignment if you feel that you cannot be unbiased.

This is beyond any formal guidelines. A general ethical principle.

There is a LOT of grey area.

As I frequently relate, in my early years when a famous Editor asked me to review a manuscript from one of my tighter science homies and I pointed out this relationship I was told "If I had to use that standard as the Editor I would never get anything reviewed. Just do it. I know you are friends.".

I may also have mentioned that when first on study section I queried an SRO about doing reviews for PIs who were scientifically sort of close to my work. I was told a similar thing about how reviews would never get done if vaguely working in the same areas and maybe one day competing on some topic were the standard for COI recusal.

So we are, for the most part, left up to our own devices and ethics about when we identify a bias in ourselves and refuse to do peer review because of this conflict.

I have occasionally refused to review an NIH grant because the PI was simply too good of a friend. I can't recall being asked to review a grant proposal from anyone I dislike personally or professionally enough to trigger my personal threshold.

I am convinced, however, that I would recuse myself from the review of proposals or manuscripts from any person that I know to be a sexual harasser, a retaliator and/or a bigot against women, underrepresented groups generally, LGBTQ, and the like.

There is a flavor of apologist for Jim Watson (et rascalia) that wants to pursue a "slippery slope" argument. Just Asking the Questions. You know the type. One or two of these popped up on twitter over the weekend but I'm too lazy to go back and find the thread.

The JAQ-off response is along the lines of "What about people who have politics you don't like? Would you recuse yourself from a Trump voter?".

The answer is no.

Now sure, the topic of implicit or unconscious bias came up and it is problematic for sure. We cannot recuse ourselves when we do not recognize our bias. But I would argue that this does not in any way suggest that we shouldn't recuse ourselves when we DO recognize our biases. There is a severity factor here. I may have implicit bias against someone in my field that I know to be a Republican. Or I may not. And when there is a clear and explicit bias, we should recuse.

I do not believe that people who have proven themselves to be sexual harassers or bigots on the scale of Jim Watson deserve NIH grant funding. I do not believe their science is going to be so much superior to all of the other applicants that it needs to be funded. And so if the NIH disagrees with me, by letting them participate in peer review, clearly I cannot do an unbiased job of what NIH is asking me to do.

The manuscript review issue is a bit different. It is not zero-sum and I never review that way, even for the supposedly most-selective journals that ask me to review. There is no particular reason to spread scoring, so to speak, as it would be done for grant application review. But I think it boils down to essentially the same thing. The Editor has decided that the paper should go out for review and it is likely that I will be more critical than otherwise.

So....can anyone see any huge problems here? Peer review of grants and manuscripts is opt-in. Nobody is really obliged to participate at all. And we are expected to manage the most obvious of biases by recusal.

38 responses so far

Eric Lander apologizes for toasting Jim Watson

(by drugmonkey) May 14 2018

Dr. Eric Lander, of the BROAD Institute, recently gave a toast honoring Jim Watson at the close of the Biology of Genomes meeting. See below Twitter thread from Jonathan Eisen for an archived video copy of the toast. (Picture via: Sarah Tishkoff tweet)

Lander has now apologized for doing so in a tweet:

The text reads:

Last week I agreed to toast James Watson for the Human Genome Project on his 90th birthday. My brief comment about his being “flawed” did not go nearly far enough. His views are abhorrent: racist, sexist, anti-semitic. I was wrong to toast. I apologize.

I applaud Dr. Lander for this apology.

This comes after a bit of a Twitter storm. If you wonder why some people see value in using social media to advance progressive ideas*, this is one example.

Some key threads from

Jonathan Eisen

Angus Johnson

Michael Eisen

One of the most amazing things in all of the twitter discussion over the weekend is that there are still people who want to try to claim that Watson's decades of abhorrent ranting about people he disdains, tied in many cases to the scientific topics he is discussing and in others to the people he thinks should be allowed or disallowed to participate in science, have nothing to do with public accolades "for his scientific accomplishments".

Additional Reading:
Snowflakes Falling

We've finally found out, thanks to Nature News, that the paltry academic salary on which poor Jim Watson has been forced to rely is $375,000 per year as "chancellor emeritus" at Cold Spring Harbor Laboratory. The current NIH salary limitation is $181,500, this is the maximum amount that can be charged to Federal grants. I'm here to tell you, most of us funded by NIH grants do not make anything like this as an annual salary.


Arrogant jerkwad creates meaningless kerfluffle, News at Eleven

Notorious arrogant bastard* and Nobel laureate, James Watson shoots off again, this time descending into race/intelligence minefield [Pharyngula, Zuska, denialism blog]. Consequently gets talk cancelled. The ass kick by Greg Laden here and here, pre-empts my need to get into the intelligence literature. Blogosphere and MSM goes nuts for a news cycle or two.

Famed Scientist Apologizes for Quoted Racial Remarks

James Watson: What I've Learned

Should you be allowed to make an anti-Semitic remark? Yes, because some anti-Semitism is justified....
Francis Crick said we should pay poor people not to have children. I think now we're in a terrible situation where we should pay the rich people to have children. If there is any correlation between success and genes, IQ will fall if the successful people don't have children. These are self-obvious facts.
If I had been married earlier in life, I wouldn't have seen the double helix. I would have been taking care of the kids on Saturday.

*Call it constantly angry performative social justice warrioring if you like. Whatever it takes. Just get er done.

47 responses so far

The Purchasing Power of the NIH Grant Continues to Erode

(by drugmonkey) May 11 2018

It has been some time since I made a figure depicting the erosion of the purchasing power of the NIH grant so this post is simply an excuse to update the figure.

In brief, the NIH modular budget system used for a lot of R01 awards limits the request to $250,000 in direct costs per year. A PI can ask for more but they have to use a more detailed budgeting process, and there are a bunch of reasons I'm not going to go into here that makes the "full-modular" a good starting point for discussion of the purchasing power of the typical NIH award.

The full modular limit was put in place at the inception of this system (i.e., for applications submitted after 6/1/1999) and has not been changed since. I've used the FY2001 as my starting point for the $250,000 and then adjusted it in two ways according to the year by year BRDPI* inflation numbers. The red bars indicate the reduction in purchasing power of a static $250,000 direct cost amount. The black bars indicate the amount the full-modular limit would have to be escalated year over year to retain the same purchasing power that $250,000 conferred in 2001.

(click to enlarge)

The executive summary is that the NIH would have to increase the modular limit to $450,000 $400,000** per year in direct costs for FY2018 in order for PIs to have the same purchasing power that came with a full-modular grant award in 2001.
*The BRDPI inflation numbers that I used can be downloaded from the NIH Office of Budget. The 2017 and 2018 numbers are projected.

**I blew it. The BRDPI spreadsheet actually projects inflation out to 2023 and I pulled the number from 2021 projection. The correct FY2018 equivalent is $413,020.

5 responses so far

Repost- Your Grant in Review: Competing Continuation, aka Renewal, Apps

(by drugmonkey) May 11 2018

Two recent posts discuss the topic of stabilizing NIH funding within a PI's career, triggered by a blog post from Mike Lauer and Francis Collins. In the latter, the two NIH honchos claim to be losing sleep over the uncertainty of funding in the NIH extramural granting system, specifically in application to those PIs who received funding as an ESI and are now trying to secure the next round of funding.

One key part of this, in my view, is how they (the NIH) and we (extramural researchers, particularly those reviewing applications for the NIH) think about the proper review of Renewal (formerly known as competing continuation) applications. I'm reposting some thoughts I had on this topic for your consideration.

This post originally appeared Jan 28, 2016.
In the NIH extramural grant funding world the maximum duration for a project is 5 years. It is possible at the end of a 5 year interval of support to apply to continue that project for another interval. The application for the next interval is competitively reviewed alongside of new project proposals in the relevant study sections, in general.

Comradde PhysioProffe addressed the continuation application at his Ftb joint. NIAID has a FAQ page.

The NIH Success Rate data shows that RPG success rates were 16.8% in 2013 and 18.1% in 2014. Comparable rates for competing continuation RPG applications were 35% in 2013 and 39% in 2014. So you can see why this is important.

I visited these themes before in a prior post. I think I covered most of the issues but in a slightly different way.

Today I want to try to get you folks to talk about prescriptives. How should a competing continuation / renewal NIH grant application be reviewed?

Now in my experience, the continuation application hinges on past-productivity in a way that a new application does not. Reviewers are explicitly considering the work that has been conducted under the support of the prior award. The application is supposed to include a list of publications that have resulted from the prior award. The application is supposed to detail a Progress Report that overviews what has been accomplished. So today I will be focusing on review mostly as it pertains to productivity. For reference, Berg's old post on the number of papers per grant dollar is here and shows an average output of 6 papers (IQR about 4-11) per $250K full modular award*.

Quoted bits are from my prior post.

Did you knock our socks off? This could be amazing ELEVENTY type findings, GlamourPub record (whether “expected” for your lab or not), unbelievably revolutionary advances, etc. If you have a record of this, nobody is going to think twice about what your Aims may have been. Probably won’t even give a hoot whether your work is a close match to the funding IC, for that matter.

We should probably separate these for discussion because after all, how often is a panel going to recognize a Nobel Prize type of publication has been supported by the award in the past 5 years? So maybe we should consider Glamour publications and amazing advances as two different scenarios. Are these going to push any renewal application over the hurdle for you even if the remaining items below are lacking? Does GlamMag substitute for direct attention to the experiments that were proposed or the Aims that guided the plan? In the extreme case, should we care if the work bears very little on the mission of the IC that has funded it?

Were you productive? Even if you didn’t WOW the world, if you’ve pumped out a respectable number of papers that have some discernible impact on a scientific field, you are in good shape. The more, the merrier. If you look “fabulously productive” and have contributed all kinds of interesting new science on the strength of your award(s), this is going to go down like gangbusters with the review panels. At this level of accomplishment you’d probably be safest at least be doing stuff that is vaguely in line with the IC that has funded your work.

Assuming that Glam may not be in the control of most PIs but that pedestrian, workaday scientific output is, should this be a major credit for the continuation application? We don't necessarily have to turn this into a LPU sausage-slicing discussion. Let's assume a quality of paper commensurate with the kind of work that most PIs with competitive applications in that particular study section publish. Meets the subfield standard. How important should raw productivity be?

Were you productive in addressing your overall goals? This is an important distinction from the Specific Aims. It is not necessary, in my view, that you hew closely to Aims first dreamed up 7 years prior to the conclusion of the actual study. But if you have moderate, or disappointing, productivity it is probably next most-helpful that you have published work related to the overall theme of the project. What was the big idea? What was mentioned in the first three sentences of your Specific Aims page? If you have published work related to this broad picture, that’s good.

This one is tricky. The reviewers do not have the prior grant application in front of them. They have the prior Summary Statement and the Abstract as published on RePORTER. It is a decent bet the prior Aims can be determined but broader themes may or may not come across. So for the most part if the applicant expects the reviewers to see that productivity has aligned with overarching programmatic goals, she has to tell them what those were. Presumably in the Progress Report part of the continuation application. How would you approach this as a reviewer? If the project wasn't overwhelmingly productive, didn't obviously address all of the Aims but at least generated some solid work along the general themes. Are you going to be satisfied? Or are you going to downgrade the failure to address each Aim? What if the project had to can an entire Aim or two? Would it matter? Is getting "stuck" in a single Aim a death knell when it comes time to review the next interval of support? As a related question if the same exact Aim has returned with the argument of "We didn't get to this in the past five years but it is still a good idea"? Neutral? Negative? AYFK?

Did you address your original Specific Aims? ...this can be a big obsession of certain reviewers. Not saying it isn’t a good idea to have papers that you can connect clearly to your prior Aims. ... A grant is not a contract. It is quite natural in the course of actual science that you will change your approaches and priorities for experiments. Maybe you’ve been beaten to the punch. Maybe your ongoing studies tell you that your original predictions were bad and you need to go in a whole new direction. Maybe the field as a whole has moved on. ... You might want to squeeze a drop out of a dry well to meet the “addressed Aims” criterion but maybe that money, effort and time would be better spent on a new direction which will lead to three pubs instead of one?

My original formulation of this isn't quite right for today's discussion. The last part is actually more relevant to the preceding point. For today, expand this to a continuation application that shows that the prior work essentially covers exactly what the application proposed. With data either published or included as ready-to-submit Preliminary Data in the renewal. Maybe this was accomplished with only a few papers in pedestrian journals (Lord knows just about every one of my manuscript reviews these days gets at least one critique that to calls for anywhere from 2 to 5 Specific Aims worth of data) so we're not talking about Glam or fabulous productivity. But should addressing all of the Aims and most if not all of the proposed experiments be enough? Is this a credit to a competing continuation application?

It will be unsurprising to you that by this point of my career, I've had competing continuation applications to which just about all of these scenarios apply, save Glam. We've had projects where we absolutely nailed everything we proposed to do. We've had projects get distracted/sidelined off onto a subsection of the proposal that nevertheless generated about the same number and quality of publications that would have otherwise resulted. We've had low productivity intervals of support that addressed all the Aims and ones that merely covered a subset of key themes. We've had projects with reasonably high productivity that have....wandered....from the specifics of the awarded proposal due to things that are happening in the subfield (including getting scooped). We've never been completely blanked on a project with zero related publications to my recollection, but we've had some very low productivity ones (albeit with excellent excuses).

I doubt we've ever had a perfect storm of sky-high productivity, all Aims addressed and the overarching themes satisfied. Certainly I have the review comments to suggest this**.

I have also been present during review panel discussions of continuation applications where reviewers have argued bitterly over the various productivity attributes of a prior interval of support. The "hugely productive" arguments are frequently over an application from a PI who has more than one award and tends to acknowledge more than one of them on each paper. This can also involve debates about so called "real scientific progress" versus papers published. This can be the Aims, the overall theme or just about the sneer of "they don't really do any interesting science".

I have for sure heard from people who are obsessed during review with whether each proposed experiment has been conducted (this was back in the days when summary statements could be fairly exhaustive and revealed what was in the prior application to a broader extent). More generally from reviewers who want to match publications up to the scope of the general scientific terrain described by the prior application.

I've also seen arguments about suggested controls or key additional experiments which were mentioned in the summary statement of the prior review, never addressed in the resulting publications and may still be a criticism of the renewal application.

Final question: Since the reviewers of the competing continuation see the prior summary statement, they see the score and percentile. Does this affect you as a reviewer? Should it? Especially if in your view this particular application should never have been funded at that score and is a likely a Programmatic pickup? Do you start steaming under the collar about special ESI paylines or bluehair/graybeard insider PO backslapping?

DISCLAMER: A per usual, I may have competing continuation applications under current or near-future review by NIH study sections. I am an interested party in how they are reviewed.
*This probably speaks to my point about how multi-award PIs attribute more than one grant on each paper. My experience has not been that people in my field view 5 papers published per interval of support (and remember the renewal application is submitted with the final year of funded support yet to go, if the project is to continue uninterrupted) as expected value. It is certainly not viewed as the kind of fabulous productivity that of course would justify continuing the project. It is more in line with the bare minimum***. Berg's data are per-grant-dollar of course and are not exactly the same as per-grant. But it is a close estimate. This blog post estimates "between 0.6 and 5 published papers per $100k in funding." which is one to 12 per year of a full-modular NIH R01. Big range and that high number seems nigh on impossible to me without other funding (like free trainee labor or data parasitism).

**and also a pronounced lack of success renewing projects to go with it.

***I do not personally agree. At the point of submitting a competing continuation in year 4 a brand new research program (whether b/c noob PI or very new lab direction) may have really only been rocking for 2 years. And large integrated projects like a big human subjects effort may not even have enrolled all the subjects yet. Breeding, longitudinal development studies, etc - there are many models that can all take a long time to get to the point of publishing data. These considerations play....let us say variably, with reviewers. IME.

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Stability of funding versus the project-based funding model of the NIH

(by drugmonkey) May 09 2018

In response to a prior post, Morgan Price wonders about the apparent contrast of NIH's recent goal to stabilize research funding and the supposed "project-based" model.

I don't see how stability based funding is consistent with project-based funding and "funding the best science". It would be a radical change...?

NIH grants are supposed to be selected and awarded on the basis of the specific project that is proposed. That is why there is such extensive detailing of a very specific area of science, well specified Specific (not General!) Aims and a listing of specific experiments.

They are not awarded on the basis of a general program of research that seems to be promising for continued funding.

Note that there are indeed mechanisms of funding that operate on the program level to much greater extent. HHMI being one of the more famous ones of these. In program based award, the emphasis is on what the investigating team (and generally this means specifically the PI) has accomplished and published in recent years. There may be some hints about what the person plans to work on next but generally the emphasis is on past performance, rather than the specific nature of the future plan.

In the recent handwringing from NIH about how investigators that they have launched with special consideration for their newcomer status (e.g., the Early Stage Investigator PI applications can be funded at lower priority scores / percentile ranks than would be needed by an established investigator.

if we are going to nurture meritorious, productive mid-career investigators by stabilizing their funding streams, monies will have to come from somewhere.

"Stabilizing", Morgan Price assumes is the same thing as a radical change. It is not.

Here's the trick:

The NIH funding system has always been a hybrid which pays lip service to "project based funding" as a model while blithely using substantial, but variable, input from the "program based" logic. First off, the "Investigator" criterion of proposal review is one of 5 supposedly co-equal major criteria. The Biosketch, which details the past accomplishments and skills of the PI) is prominent in the application. This Biosketch lists both papers and prior research grant support* which inevitably leads to some degree of assessment of how productive the PI was with her prior awards. This then is used to judge the merit of the proposal that is under current review - sounds just a bit like HHMI, doesn't it?

The competing continuation application (called a Renewal application now) is another NIH beast that reveals the hybrid nature of the selection system. You are allowed to ask for no more than 5 years of support for a given project, but you can then ask for successive five year extensions via competitive application review. This type of proposal has a "Progress Report" and a list of papers resulting from the project required within the application. This, quite obviously, focuses the review in large part on the past accomplishment. Now, sure, the application also has to have a detailed proposal for the next interval. Specific Aims. Experiments listed. But it also has all of the prior accomplishments pushed into the center of the review.

So what is the problem? Why are Collins and Lauer proposing to make the NIH grant selection even more based on the research program? Well, times have changed. The figure here is a bit dated by now but I like to keep refreshing your view of it because NIH has this nasty tendency to truncate their graphs to only the past decade or so. The NIH does this to obscure just how good investigators had things in the 80s. That was when established investigators enjoyed success rates north of 40%. For all applications, not just for competing renewals. Many of the people who started their careers in those wonderful days are still very much with us, by the way. This graph shows that within a few years of the end of the doubling, the success rates for established investigators had dropped to about where the new investigators were in the 1980s. Success rates have only continued to get worse but thanks to policies enacted by Zerhouni, the established and new investigator success rates have been almost identical since 2007.
Interestingly, one of the things Zerhouni had to do was to insist that Program change their exception pay behavior. (This graph was recreated from a GAO report [PDF], page down to Page 56, PDF page 60.) It is relevant because it points to yet another way that the NIH system used to prioritize program qualities over the project qualities. POs historically were much more interested in "saving" previously funded, now unfunded, labs than they were in saving not-yet-funded labs.

Now we get to Morgan Price's point about "the best science". Should the NIH system be purely project-based? Can we get the best science one 5 year plan at a time?

I say no. Five years is not enough time to spool up a project of any heft into a well honed and highly productive gig. Successful intervals of 5 year grants depend on what has come before to a very large extent. Often times, adding the next 5 years of funding via Renewal leads to an even more productive time because it leverages what has come before. Stepping back a little bit, gaps in funding can be deadly for a project. A project that has been killed off just as it is getting good is not only not the "best" science it is hindered science. A lack of stability across the NIH system has the effect of making all of its work even more expensive because something headed off in Lab 1 (due to gaps in funding) can only be started up in Lab 2 at a handicap. Sure Lab 2 can leverage published results of Lab 1 but not the unpublished stuff and not all of the various forms of expertise locked up in the Lab 1 staff's heads.

Of course if too much of the NIH allocation goes to sinecure program-based funding to continue long-running research programs, this leads to another kind of inefficiency. The inefficiencies of opportunity cost, stagnation, inflexibility and dead-woodery.

So there is a balance. Which no doubt fails to satisfy most everyone's preferences.

Collins and Lauer propose to do a bit of re-balancing of the program-based versus project-based relationship, particularly when it comes to younger investigators. This is not radical change. It might even be viewed in part as a selective restoration of past realities of grant funded science careers.

*In theory the PI's grants are listed on the Biosketch merely to show the PI is capable of leading a project something like the one under review. Correspondingly, it would in theory be okay to just list the most successful ones and leave out the grant awards with under-impressive outcomes. After all, do you have to put in every paper? no. Do you have to put every bit of bad data that you thought might be preliminary data into the app? no. So why do you have to** list all of your grants? This is the program-based aspects of the system at work.

**dude, you have to. this is one of those culture of review things. You will be looked up on RePORTER and woe be to you if you try to hide some project, successful or not, that has active funding within the past three years.

14 responses so far

Addressing the Insomnia of Francis Collins and Mike Lauer

(by drugmonkey) May 07 2018

The Director of the NIH and the Deputy Director in charge of the office of extramural research have posted a blog post about The Issue that Keeps Us Awake at Night. It is the plight of the young investigator, going from what they have written.

The Working Group is also wrestling with the issue that keeps us awake at night – considering how to make well-informed strategic investment decisions to nurture and further diversify the biomedical research workforce in an environment filled with high-stakes opportunity costs. If we are going to support more promising early career investigators, and if we are going to nurture meritorious, productive mid-career investigators by stabilizing their funding streams, monies will have to come from somewhere. That will likely mean some belt-tightening in other quarters, which is rarely welcomed by the those whose belts are being taken in by a notch or two.

They plan to address this by relying on data and reports that are currently being generated. I suspect this will not be enough to address their goal.

I recently posted a link to the NIH summary of their history of trying to address the smooth transition of newly minted PIs into NIH-grant funded laboratories, without much comment. Most of my Readers are probably aware by now that handwringing from the NIH about the fate of new investigators has been an occasional feature since at least the Johnson Administration. The historical website details the most well known attempts to fix the problem. From the R23 to the R29 FIRST to the New Investigator check box, to the "sudden realization"* they needed to invent a true Noob New Investigator (ESI) category, to the latest designation of the aforementioned ESIs as Early Established Investigators for continued breaks and affirmative action. It should be obvious from the ongoing reinvention of the wheel that the NIH periodically recognizes that the most recent fix isn't working (and may have unintended detrimental consequences).

One of the reasons these attempts never truly work and have to be adjusted or scrapped and replaced by the next fun new attempt was identified by Zerhouni (a prior NIH Director) in about 2007. This was right after the "sudden realization" and the invention of the ESI. Zerhouni was quoted in a Science news bit as saying that study sections were responding to the ESI special payline boost by handing out ever worsening scores to the ESI applications.

Told about the quotas, study sections began “punishing the young investigators with bad scores,” says Zerhouni.

Now, I would argue that viewing this trend of worsening scores as "punishing" is at best only partially correct. We can broaden this to incorporate a simple appreciation that study sections adapt their biases, preferences and evolved cultural ideas about grant review to the extant rules. One way to view worsening ESI scores may have to do with the pronounced tendency reviewers have to think in terms of fund it / don't fund it, despite the fact that SROs regularly exhort them not to do this. When I was on study section regularly, the scores tended to pile up around the perceived payline. I've seen the data for one section across multiple rounds. Reviewers were pretty sensitive to the scuttlebutt about what sort of score was going to be a fundable one. So it would be no surprise whatsoever to me if there was a bias driven by this tendency, once it was announced that ESI applications would get a special (higher) payline for funding.

This tendency might also be driven in part by a "Get in line, youngun, don't get too big for your britches" phenomenon. I've written about this tendency a time or two. I came up as a postdoc towards the end of the R29 / FIRST award era and got a very explicit understanding that some established PIs thought that newbies had to get the R29 award as their first award. Presumably there was a worsening bias against giving out an R01 to a newly minted assistant professor as their first award**, because hey, the R29 was literally the FIRST award, amirite?


Then we come to hazing, which is the even nastier relative of the "Don't get to big for your britches". Oh, nobody will admit that it is hazing, but there is definitely a subcurrent of this in the review behavior of some people that think that noob PIs have to prove their worth by battling the system. If they sustain the effort to keep coming back with improved versions, then hey, join the club kiddo! (Here's an ice pack for the bruising). If the PI can't sustain the effort to submit a bunch of revisions and new attempts, hey, she doesn't really have what it takes, right? Ugh.

Scientific gate-keeping. This tends to cover a multitude of sins of various severity but there are definitely reviewers that want newcomers to their field to prove that they belong. Is this person really an alcohol researcher? Or is she just going to take our*** money and run away to do whatever basic science amazeballs sounded super innovative to the panel?

Career gate-keeping. We've gone many rounds on this one within the science blog- and twittospheres. Who "deserves" a grant? Well, reviewers have opinions and biases and despite their best intentions and wounded protestations...these attitudes affect review. In no particular order we can run down the favorite targets of the "Do it to Julia, not me, JULIA!" sentiment. Soft money job categories. High overhead Universities. Well funded labs. Translational research taking all the money away from good honest basic researchers***. Elite coastal Universities. Big Universities. R1s. The post-normative-retirement crowd. Riff-raff plodders.

Layered over the top of this is favoritism. It interacts with all of the above, of course. If some category of PI is to be discriminated against, there is very likely someone getting the benefit. The category of which people approve. Our club. Our kind. People who we like who must be allowed to keep their funding first, before we let some newbie get any sniff of a grant.

This, btw, is a place where the focus must land squarely on Program Officers as well. The POs have all the same biases mentioned above, of course. And their versions of the biases have meaningful impact. But when it comes to thought of "we must save our long term investigators" they have a very special role to play in this debacle. If they are not on board with the ESI worries that keep Collins and Lauer awake at night, well, they are ideally situated to sabotage the effort. Consciously or not.

So, Director Collins and Deputy Director Lauer, you have to fix study section and you have to fix Program if you expect to have any sort of lasting change.

I have only a few suggestions and none of this is a silver bullet.

I remain convinced that the only tried and true method to minimize the effects of biases (covert and overt) is the competition of opposing biases. I've remarked frequently that study sections would be improved and fairer if less-experienced investigators had more power. I think the purge of Assistant Professors effected by the last head of the CSR (Scarpa) was a mistake. I note that CSR is charged with balancing study sections on geography, sex, ethnicity, university type and even scientific subdomains...while explicitly discriminating against younger investigators. Is it any wonder if there is a problem getting the newcomers funded?

I suggest you also pay attention to fairness. I know you won't, because administrators invariably respond to a situation of perceived past injustice with "ok, that was the past and we can't do anything about it, moving forward please!". But this is going to limit your ability to shift the needle. People may not agree on what represents fair treatment but they sure as heck are motivated by fairness. Their perception of whether a new initiative is fair or unfair will tend to shape their behavior when reviewing. This can get in the way of NIH's new agenda if reviewers perceive themselves as being mistreated by it.

Many of the above mentioned reviewer quirks are hardened by acculturation. PIs who are asked to serve on study section have been through the study section wringer as newbies. They are susceptible to the idea that it is fair if the next generation has it just about as hard as they did and that it is unfair if newbies these days are given a cake walk. Particularly, if said established investigators feel like they are still struggling. Ahem. It may not seem logical but it is simple psychology. I anticipate that the "Early Established Investigator" category is going to suffer the same fate as the ESI category. Scores will worsen, compared to pre-EEI days. Some of this will be the previously mentioned tracking of scores to the perceived payline. But some of this will be people**** who missed the ESI assistance who feel that it is unfair that the generation behind them gets yet another handout to go along with the K99/R00 and ESI plums. The intent to stabilize the careers of established investigators is a good one. But limiting this to "early" established investigators, i.e., those who already enjoyed the ESI era, is a serious mistake.

I think Lauer is either aware, or verging on awareness, of something that I've mentioned repeatedly on this blog. I.e. that a lot of the pressure on the grant system- increasing numbers of applications, PIs seemingly applying greedily for grants when already well funded, they revision queuing traffic pattern hold - comes from a vicious cycle of the attempt to maintain stable funding. When, as a VeryEstablished colleague put it to me suprisingly recently "I just put in a grant when I need another one and it gets funded" is the expected value, PIs can be efficient with their grant behavior. If they need to put in eight proposals to have a decent chance of one landing, they do that. And if they need to start submitting apps 2 years before they "need" one, the randomness is going to mean they seem overfunded now and again. This applies to everyone all across the NIH system. Thinking that it is only those on their second round of funding that have this stability problem is a huge mistake for Lauer and Collins to be making. And if you stabilize some at the expense of others, this will not be viewed as fair. It will not be viewed as shared pain.

If you can't get more people on board with a mission of shared sacrifice, or unshared sacrifice for that matter, then I believe NIH will continue to wring its hands about the fate of new investigators for another forty years. There are too many applicants for too few funds. It amps up the desperation and amps up the biases for and against. It decreases the resistance of peer reviewers to do anything to Julia that they expect might give a tiny boost to the applications of them and theirs. You cannot say "do better" and expect reviewers to change, when the power of the grant game contingencies is so overwhelming for most of us. You cannot expect program officers who still to this day appear entirely clueless about they way things really work in extramural grant-funded careers to suddenly do better because you are losing sleep. You need to delve into these psychologies and biases and cultures and actually address them.

I'll leave you with an exhortation to walk the earth, like Caine. I've had the opportunity to watch some administrative frustration, inability and nervousness verging on panic in the past couple of years that has brought me to a realization. Management needs to talk to the humblest of their workforce instead of the upper crust. In the case of the NIH, you need to stop convening preening symposia from the usual suspects, taking the calls of your GlamHound buddies and responding only to reps of learn-ed societies. Walk the earth. Talk to real applicants. Get CSR to identify some of your most frustrated applicants and see what is making them fail. Find out which of the apparently well-funded applicants have to work their tails off to maintain funding. Compare and contrast to prior eras. Ask everyone what it would take to Fix the NIH.

Of course this will make things harder for you in the short term. Everyone perceives the RealProblem as that guy, over there. And the solutions that will FixTheNIH are whatever makes their own situation easier.

But I think you need to hear this. You need to hear the desperation and the desire most of us have simply to do our jobs. You need to hear just how deeply broken the NIH award system is for everyone, not just the ESI and EEI category.

PS. How's it going solving the problem identified by Ginther? We haven't seen any data lately but at last check everything was as bad as ever so...

PPS. Are you just not approving comments on your blog? Or is this a third rail issue nobody wants to comment on?
*I make fun of the "sudden realization" because it took me about 2 h of my very first study section meeting ever to realize that "New Investigator" checkbox applicants from genuine newbies did very poorly and all of these were being scooped up by very well established and accomplished investigators who simply hadn't been NIH funded. Perhaps they were from foreign institutions, now hired in the US. Or perhaps lived on NSF or CDC or DOD awards. The idea that it took NIH something like 8-10 years to realize this is difficult to stomach.

**The R29 was crippled in terms of budget, btw. and had other interesting features.


****Yep, that would be my demographic.

12 responses so far

NIH's long sordid history of failing to launch new investigators fairly and cleanly

(by drugmonkey) May 03 2018

Actually, they call it "A History of Commitment"

It starts with the launch of the R23 in 1977, covers the invention and elimination of the R29 FIRST and goes all the way to the 2017 announcement that prior ESI still need help, this time for their second and third rounds of funding as "Early Established Investigators".


Updated to add:
Mike Lauer is wringing his hands on the blog about The Issue that (allegedly) keeps us (NIH officialdom) awake at night [needs citation].

We pledge to do everything we can to incorporate those recommendations, along with those of the NASEM panel, in our ongoing efforts to design, test, implement, and evaluate policies that will assure the success of the next generation of talented biomedical researchers.

5 responses so far

NIH reminds Universities not to keep paying harasser PIs from grant funds while suspended

(by drugmonkey) May 03 2018

On the May 1, 2018 the NIH issued NOT-OD-18-172 to clarify that:

NIH seeks to remind the extramural community that prior approval is required anytime there is a change in status of the PD/PI or other senior/key personnel where that change will impact his/her ability to carry out the approved research at the location of, and on behalf of, the recipient institution. In particular, changes in status of the PI or other senior/key personnel requiring prior approval would include restrictions that the institution imposes on such individuals after the time of award, including but not limited to any restrictions on access to the institution or to the institution’s resources, or changes in their (employment or leave) status at the institution. These changes may impact the ability of the PD/PI or other senior/key personnel to effectively contribute to the project as described in the application; therefore, NIH prior approval is necessary to ensure that the changes are acceptable.

Hard on the heels of the news breaking about long term and very well-funded NIH grant Principal Investigators Thomas Jessel and Inder Verma being suspended from duties at Columbia University and The Salk Institute for Biological Studies, respectively, one cannot help but draw the obvious conclusion.

I don't know what prompted this Notice but I welcome it.

Now, I realize that many of us would prefer to see some harsher stuff here. Changing the PI of a grant still keeps the sweet sweet indirects flowing into the University or Institute. So there is really no punishment when an applicant institution is proven to have looked the other way for years (decades) when their well-funded PIs are accused repeatedly of sexual harassment, gender-based discrimination, retaliation on whistleblowers and the like.

But this Notice is still welcome. It indicates that perhaps someone is actually paying a tiny little bit of attention now in this post-Weinstein era.

4 responses so far

Time to N-up!

(by drugmonkey) May 02 2018

Chatter on the Twitts today brought my attention to a paper by Weber and colleagues that had a rather startlingly honest admission.

Weber F, Hoang Do JP, Chung S, Beier KT, Bikov M, Saffari Doost M, Dan Y.Regulation of REM and Non-REM Sleep by Periaqueductal GABAergic Neurons. Nat Commun. 2018 Jan 24;9(1):354. doi: 10.1038/s41467-017-02765-w.

If you page all the way down to the end of the Methods of this paper, you will find a statement on sample size determination. I took a brief stab at trying to find the author guidelines for Nature Communications because a standalone statement of how sample size was arrived upon is somewhat unusual to me. Not that I object, I just don't find this to be common in the journal articles that I read. I was unable to locate it quickly so..moving along to the main point of the day. The statement reads partially:

Sample sizes

For optogenetic activation experiments, cell-type-specific ablation experiments, and in vivo recordings (optrode recordings and calcium imaging), we continuously increased the number of animals until statistical significance was reached to support our conclusions.

Wow. WOW!

This flies in the face of everything I have ever understood about proper research design. In the ResearchDesign 101 approach, you determine* your ideal sample size in advance. You collect your data in essentially one go and then you conduct your analysis. You then draw your conclusions about whether the collected data support, or fail to support, rejection of a null hypothesis. This can then allow you to infer things about the hypothesis that is under investigation.

In the real world, we modify this a bit. And what I am musing today is why some of the ways that we stray from ResearchDesign orthodoxy are okay and some are not.

We talk colloquially about finding support for (or against) the hypothesis under investigation. We then proceed to discuss the results in terms of whether they tend to support a given interpretation of the state of the world or a different interpretation. We draw our conclusions from the available evidence- from our study and from related prior work. We are not, I would argue, supposed to be setting out to find the data that "support our conclusions" as mentioned above. It's a small thing and may simply reflect poor expression of the idea. Or it could be an accurate reflection that these authors really set out to do experiments until the right support for a priori conclusions has been obtained. This, you will recognize, is my central problem with people who say that they "storyboard" their papers. It sounds like a recipe for seeking support, rather than drawing conclusions. This way lies data fakery and fraud.

We also, importantly, make the best of partially successful experiments. We may conclude that there was such a technical flaw in the conduct of the experiment that it is not a good test of the null hypothesis. And essentially treat it in the Discussion section as inconclusive rather than a good test of the null hypothesis.

One of those technical flaws may be the failure to collect the ideal sample size, again as determined in advance*. So what do we do?

So one approach is simply to repeat the experiment correctly. To scrap all the prior data, put fixes in place to address the reasons for the technical failure, and run the experiment again. Even if the technical failure hit only a part of the experiment. If it affected only some of the "in vivo recordings", for example. Orthodox design mavens may say it is only kosher to re run the whole shebang.

In the real world, we often have scenarios where we attempt to replace the flawed data and combine it with the good data to achieve our target sample size. This appears to be more or less the space in which this paper is operating.

"N-up". Adding more replicates (cells, subjects, what have you) until you reach the desired target. Now, I would argue that re-running the experiment with the goal of reaching the target N that you determined in advance* is not that bad. It's the target. It's the goal of the experiment. Who cares if you messed up half of them every time you tried to run the experiment? Where "messed up" is some sort of defined technical failure rather than an outcome you don't like, I rush to emphasize!

On the other hand, if you are spamming out low-replicate "experiments" until one of the scenarios "looks promising", i.e. looks to support your desired conclusions, and selectively "n-up" that particular experiment, well this seems over the line to me. It is much more likely to result in false positives. Well, I suppose running all of these trial experiments at the full power is just as likely it is just that you are not able to do as many trial experiments at full power. So I would argue the sheer number of potential experiments is greater for the low-replicate, n-up-if-promising approach.

These authors appear to have done this strategy even one worse. Because their target is not just an a priori determined sample size to be achieved only when the pilot "looks promising". In this case they take the additional step of only running replicates up to the point where they reach statistical significance. And this seems like an additional way to get an extra helping of false-positive results to me.

Anyway, you can google up information on false positive rates and p-hacking and all that to convince yourself of the math. I was more interested in trying to probe why I got such a visceral feeling that this was not okay. Even if I personally think it is okay to re-run an experiment and combine replicates (subjects in my case) to reach the a priori sample size if it blows up and you have technical failure on half of the data.

*I believe the proper manner for determining sample size is entirely apart from the error the authors have admitted to here. This isn't about failing to complete a power analysis or the like.

27 responses so far

Diversity statements from faculty candidates are an unfair test of ideology?

(by drugmonkey) Apr 30 2018

Someone on the twitts posted an objection:

to UCSD's policy of requiring applicants for faculty positions to supply a Statement of Contribution to Diversity with their application.

Mark J Perry linked to his own blog piece posted at the American Enterprise Institute* with the following observation:

All applicants for faculty positions at UCSD now required to submit a Contribution to Diversity Statement (aka Ideological Conformity Statements/Pledge of Allegiance to Left-Liberal Orthodoxy Statements)

Then some other twitter person chimed in with opinion on how this policy was unfair because it was so difficult for him to help his postdocs students with it.

Huh? A simple google search lands us on UCSD's page on this topic.

The Contributions to Diversity Statement should describe your past efforts, as well as future plans to advance diversity, equity and inclusion. It should demonstrate an understanding of the barriers facing women and underrepresented minorities and of UC San Diego’s mission to meet the educational needs of our diverse student population.

The page has links to a full set of guidelines [PDF] as well as specific examples in Biology, Engineering and Physical Sciences (hmm, I wonder if these are the disciplines they find need the most help?). I took a look at the guidelines and examples. It's pretty easy sailing. Sorry, but any PI who is complaining that they cannot help their postdocs figure out how to write the required statement are lying being disingenuous. What they really mean is that they disagree with having to prepare such a statement at all.

Like this guy Bieniasz, points for honesty:

I am particularly perplexed with this assertion that "The UCSD statement instructions (Part A) read like a test of opinions/ideology. Not appropriate for a faculty application".

Ok, so is it a test of opinion/ideology? Let's go to the guidelines provided by UCSD.

Describe your understanding of the barriers that exist for historically under-represented groups in higher education and/or your field. This may be evidenced by personal experience and educational background. For purposes of evaluating contributions to diversity, under-represented groups (URGs) includes under-represented ethnic or racial minorities (URM), women, LGBTQ, first-generation college, people with disabilities, and people from underprivileged backgrounds.

Pretty simple. Are you able to understand facts that have been well established in academia? This only asks you to describe your understanding. That's it. If you are not aware of any of these barriers *cough*Ginther*cough*cough*, you are deficient as a candidate for a position as a University professor.

So the first part of this is merely asking if the candidate is aware of things about academia that are incredibly well documented. Facts. These are sort of important for Professors and any University is well within it's rights to probe factual knowledge. This part does not ask anything about causes or solutions.

Now the other parts do ask you about your past activities and future plans to contribute to diversity and equity. Significantly, it starts with this friendly acceptance: "Some faculty candidates may not have substantial past activities. If such cases, we recommend focusing on future plans in your statement.". See? It isn't a rule-out type of thing, it allows for candidates to realize their deficits right now and to make a statement about what they might do in the future.

Let's stop right there. This is not different in any way to the other major components of a professorial hire application package. For most of my audience, the "evidence of teaching experience and philosophy" is probably the more understandable example. Many postdocs with excellent science chops have pretty minimal teaching experience. Is it somehow unfair to ask them about their experience and philosophy? To give credit for those with experience and to ask those without to have at least thought about what they might do as a future professor?

Is it "liberal orthodoxy" if a person who insists that teaching is a waste of time and gets in the way of their real purpose (research) gets pushed downward on the priority list for the job?

What about service? Is it rude to ask a candidate for evidence of service to their Institutions and academic societies?

Is it unfair to prioritize candidates with a more complete record of accomplishment than those without? Of course it is fair.

What about scientific discipline, subfield, research orientations and theoretical underpinnings? Totally okay to ask candidates about these things.

Are those somehow "loyalty pledges"? or a requirement to "conform to orthodoxy"?

If they are, then we've been doing that in the academy a fair bit with nary a peep from these right wing think tank types.

*"Mark J. Perry is concurrently a scholar at AEI and a professor of economics and finance at the University of Michigan's Flint campus." This is a political opinion-making "think-tank" so take that into consideration.

84 responses so far

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