Mind the Gap

May 15 2014 Published by under Uncategorized

In an earlier post, I analyzed the pool of NIH R-mechanism funded investigators from FY2007-FY2013 on a longitudinal basis (here and here). An additional set of features that I did not examine in the previous analyses are gaps in funding. I tracked situations were a particular investigator had funding in 1 fiscal year, no funding listed in the next year, but then funding listed again in a subsequent year. By this definition, there were 10451 gaps among the 53528 investigators in this data set.

If a given investigator has a year with no reported funding, what is the likelihood that they will show funding again in a subsequent year? For investigators who were funded in FY2006, but not FY2007, there are 6 possible years (FY2008-FY2013) for them to be re-funded. For gaps in years after FY2007, there are fewer years of “follow-up” available. The results are shown below:

Gap plot 2

The “re-funding" curves are relatively consistent from year to year with a probability of being re-funded after a 1-year gap of approximately 18% and an overall re-funding probability approaching 45% after 6 or more years.

In order to examine the distribution of gaps over time, I extended the longitudinal analysis back to FY2000. Note that data about grant costs available through NIH RePORTER are limited prior to FY2000. In a subsequent post, I will examine the dynamics of the investigator pool over the period from FY2000 to FY2005. For my present purpose, I examined the distribution of gaps and over-ended breaks (i.e. breaks in funding where subsequent funding has not (yet) be obtained). The results are shown below:

Gap-Term Plot

This plot shows that the number of gaps per year increased by 22 percent (from 1909 to 2327) from 2001 to 2006 while the number of open-ended breaks increased by 44% over the same period.

The ends of these curves are distorted by two effects. First, the ARRA funding in FY2009 and FY2010 decreased the number of gaps. Second, the number of gaps falls (and the number of open-ended breaks increases) at the end of the period since there are no data for subsequent years (end-effects). It may be possible to correct approximately for these end-effects with the re-funding curves shown above although I have not yet sorted this out to my satisfaction.

Another parameter of interest is the number of investigators with a given number of years of uninterrupted funding as a function of time. For 4 consecutive years of funding, little change over time was observed. However, for 6 consecutive years of funding (frequently requiring renewal of an R01 grant), a downward trend is observed as shown below:

6 Year uninterrupted

The number of investigators with 6 years of uninterrupted funding fell from 10310 in the period from FY2000-FY2005 to 9127 in FY2008-FY2013, a drop of 11%.

These parameters provide some quantitative measures that capture the sense of uncertainty and insecurity that many investigators feel despite increased efforts to obtain sustained funding.

11 responses so far

  • drugmonkey says:

    I am a little unclear on what your last graph is depicting. I take it you are taking a given fiscal year and looking back? So the population funded in FY2013 that has a history of funding back to 2007?

    anyway, this trend is of substantial interest to the feeling of laboratory security, I agree. It comes along with the changes in PI behavior so despite our best efforts, that decline in security is relentless.

    • datahound says:

      The first point is the number of investigators who were funded each of the years from FY2000 to FY2005 (and maybe additional years as well). The second point is the number of investigators who were funded each of the years from FY2001 to FY2006, etc. The last point corresponds to the 6 years from FY2008 to FY2013.

  • drugmonkey says:

    How is a no-cost extension treated in your analysis of the gaps? Is there any way to account for that? My default stance is to request a no-cost extension for any mechanism that allows it, there are a number of reasons for doing so. I don't know if many people do this or if they tend to close their awards out on schedule. But you could be underestimating the effective gap in funding to some degree.

  • datahound says:

    I do not believe that no-cost extensions show up in RePORTER as new awards so I do not think I am underestimating the effective gap, but I will check and do let me know if I am mistaken.

    • drugmonkey says:

      My question isn't about new grants but in how you select the population that has suffered a gap. Whether someone who had only a NCE in FY07 but then got a new award in FY08 is counted as a one year gap or not?

  • datahound says:

    In order for the NCE in FY07 to be counted, it would have to be listed in RePORTER for 2007 with a substantial amount of total cost (>$10,000). I do not believe that NCEs are listed in this way so someone who had a non-competing renewal award in FY06, a NCE extending it into FY07 (but without a RePORTER listing) and a new award in FY08 would show up as a gap in my analysis.

  • Ola says:

    @DM - damn straight! If you're not asking for a NCE, even if you anticipate no gap, you're just asking for trouble. The problem many people (including myself) have had more recently, is requesting a NCE even though there's no or very little money left in the account. The reason I do this, is the account close-out/creation process at my institution is a royal PITA. It's a whole lot easier to just keep the account open and running on fumes, so when the new NOA comes in the account numbers stay the same and everything is less confusing.

  • […] and he brings experience and sensibility to understanding trends in funding. His latest post, on gaps in funding, seems typical of what he is doing. It analyzes the data, shows what happens when a person has a […]

  • […] success rate some time ago. It is higher than the per-application success rates. Jeremy Berg posted some data on the cumulative probability of restoring NIH funding after an interval of no-funding to show that […]

  • […] I try to put a positive spin on the Datahound analysis showing the probability of a PI becoming re-funded after losing her last NIH award, the fact is […]

  • […] considerations as well. I recommend you go back and read Longitudinal PI Analysis: Distributions, Mind the Gap and especially A longitudinal analysis of NIH R-Funded Investigators: 2006-2013. This latter one […]

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