Archive for: May, 2014

R01 Size Growth and the Modular Cap

May 28 2014 Published by under Uncategorized

In a previous post, I presented data on the growth of average sizes of Research Project Grants (RPGs) from FY1983 to FY2013. The growth in the average RPG size over this period exceeded inflation, even measured by the Biomedical Research and Development Price Index, BRDPI. However, I noted that the average may or may not be a representative measure of growth since the distribution of RPG sizes has changed with the addition of large grants on one end and small grants (R03s, R21s) on the other end. I proposed the hypothesis that the growth in average RPG size could be due to growth in the number of and size of larger awards with less due to the growth in the bulk of the RPG distribution which is made up of primarily R01s.

To address this hypothesis, I have now examined full R01 distributions for years FY2003 and FY2013. From these years, the average size of an RPG grew from $380K in annual total costs to $445K, a 17% increase in nominal value. Correcting for inflation measured by BRDPI, this corresponds to a 19% drop in buying power.

How does this RPG average compare with the R01 average? This distribution of R01 sizes for FY2003 is shown below:

2003 Histogram

In FY2003, the average annual total cost for an R01 was $338K and the median was $306K. For comparison, in FY2013, the average total cost was $430K, a 27% increase (or a 9% drop in buying power, correcting for BRDPI). In FY2013, the median total cost had grown to $360K, a more modest 18% increase (or a drop of 18% in buying power).

2003-2013 Histograms

Note that the left side of the distribution shifted by approximately $50K from FY2003 to FY2013 whereas the right side of the distribution was essentially fixed. This observation will be discussed further below.

Examination of the distribution reveals the presence of more than 250 R01s with annual total costs exceeding $1M in FY2003. Removing these large R01s has only a small effect on the growth in R01 size with the average increasing from $326K in FY2003 to $401K in FY2013, a 23% nominal increase and the median increasing from $305K in FY2003 to $357K, a 17% nominal increase.

These data reveal that the growth in the average R01 size actually exceeded the growth in the average RPG size over this period of time whereas the growth in the median R01 size closely matched the growth in average RPG size. Thus, my hypothesis was not correct. The growth in the average RPG size corresponds to the growth in R01 sizes in many regards.

The distribution of the sizes of R01s in FY2003 is approximated as a Gaussian remarkably well.

2003 Gaussian Fit

A Gaussian fits the size distribution on the left side quite well. The fit is less good on the right side with an "excess" number of R01s with annual total costs larger than approximately $400K. Based on a comparison of the distribution of annual total costs with annual direct costs, presented in my previous post, the boundary near $400K in annual total costs corresponds to the boundary of $250K in annual direct costs, that is, the cap for modular grants.

The size distribution for FY2013 is fit by a Gaussian less well.

2013 Gaussian

As noted above, the left side of the distribution shifted upwards by approximately $50K from FY2003 whereas the right side was essentially fixed at the annual total cost level corresponding to the modular cap. Consistent with the notion that R01 sizes are being limited by this cap, the actual distribution is slightly narrower than the best fit Gaussian.

The "excess" of grants above the modular cap has increased by 2.5 fold from FY2003 to FY2013. This excess corresponds to 14% of the area of the Gaussian distribution in FY2003 and 34% of that area in FY2013.

This analysis reveals at least two important points. First, the modular cap on direct costs is clearly influencing the distribution of R01 grant sizes awarded and this effect is much more pronounced in FY2013 than it was in FY2003. Second, the overall trends in the R01 grant pool matches those in the overall RPG pool so that issues related to the balance between grant sizes and stipend levels are not primarily related to the choice of average RPG size as a metric. However, the influence of the modular cap as well as the impact of increased non-stipend costs (tuition, fringe benefits) raised by commenters on previous posts must still be considered.

22 responses so far

RPG Distribution 2013

May 23 2014 Published by under Uncategorized

In my previous post, I compared pre- and post-doctoral stipends with the average size of a Research Project Grant (RPG) over time. In this post, I noted that the average RPG size may not reflect the full RPG distribution.

As a first pass to investigate this issue, I have examined the size distribution for RPGs for FY2013. From data from the NIH Office of Budget, the total spending on RPGs (not including SBIR/STTR) was $14,850,256 thousand and the number of competing and non-competing RPGs was 33,395.

To try to reproduce these values, I downloaded from NIH RePORTER all non-SBIR/STTR grants in the RPG Spending Category from FY2013. This included the following mechanisms: DP1, DP2, P01, R00, R01, R03, R15, R21, R33, R34, R37, R56, RC4, RF1, RL1, U01, U19, and UM1. Once these were downloaded, the data needed to be cleaned up to avoid double-counting of subprojects for P01s and U01s, counting extensions for foreign grants that were counted in previous years, eliminating entries for very small amounts that are probably made for accounting purposes, and so on. I do not know all of the NIH accounting principles and do not know if the data in RePORTER completely match the official NIH data. Nonetheless, I was able to generate a list of 35,333 entries totaling $14,363,160 thousand. Of these grants, 26,098 correspond to R01 or R37 (MERIT) awards. Thus, while my results do not perfectly match the official NIH data, I think the major conclusions drawn below are very likely to be valid in most important ways.

This distribution of total costs of these grants are shown in the stacked bar graph below with R01 (and R37) grants shown in white and all other mechanisms shown in black.

Total Cost Bar Plot

Examination of this graph reveals that this distribution is certainly not a normal distribution with more than approximately 1300 grants with annual total costs over $1 M. This leads to a large difference in the mean grant size of $407K and the median grant size of $333K. The R01/R37 distribution is much closer to a normal distribution with an mean of $380K and a median of $350K. The non-R01 distribution includes peaks corresponding to R03s and R21s at the low end the large peak above $1M at the high end.

For FY2013 (and FY2012) information is also available in NIH RePORTER for direct costs. The distribution of annual direct costs for the same collection of grants is shown below.

Direct Cost Plot


Note that this distribution is much more peaked with a large number of R01 grants with annual direct costs between $200K and the modular cap limit of $250K. For R01s, the mean annual direct costs is $258K and the median is $231K. Again, the overall distribution for all grants is quite skewed by the large grants with a mean of $289K and a median of $217K.

Comparison of the total costs with the direct costs for these grants reveals an overall indirect cost rate of approximately 44% of all grants and 48% for R01/R37s.

While more work is necessary to understand the differences between my numbers and the official NIH numbers, this data set an analysis provides an framework for comparing average RPG values with median grant (including R01) sizes and other parameters of interest to individual investigators.

4 responses so far

Historical Trends in Predoc and Postdoc Stipends and Average Grant Sizes

May 20 2014 Published by under Uncategorized

A topic of much discussion in many circles relates to appropriate levels for predoctoral and postdoctoral stipends. In order to inform these discussions, I have analyzed historical trends for predoctoral and postdoctoral stipends provided through the National Research Service Award (NRSA) program.

Consider first the predoctoral stipend. In FY1983, this stipend was set at $5292 per year. Based on my personal experience, this was somewhat (although not dramatically) lower than the stipends provided by many Ph.D. programs. Starting in FY1985, NIH began to make a series of adjustments to move this stipend to a level that more closely reflected stipends being provided in graduate programs (and to make these stipends more consistent with the cost of living in many areas). These adjustments continued through 1998 and the beginning the NIH budget doubling, when more substantial corrections were made. At the end of the doubling with the associated nearly flat budgets, no adjustments were made although modest adjustments have been made in recent years. These stipend levels are compared with those derived by inflation adjustment of the FY1983 levels below:

Predoc Stipend

This shows that the stipend increases substantially exceeded inflation based on the FY1983 value. However, as noted above, the base value was relatively low. Based on the FY2013 level, the corresponding level for FY1983, correcting backwards for inflation, would be $9430.

The first-year postdoctoral NRSA stipend for FY1983 was $14040 (more than 2.6 times the predoctoral stipend level). Modest adjustments were made starting in FY1985 until the doubling when more substantial changes were made (e.g. $21000 to $26250 from FY1998 to FY1999). These adjustments continued through the doubling with more modest adjustments over the past decade. The results compared with those based on inflation of the FY1983 are shown below:

Postdoc stipend

Since most predoctoral and postdoctoral stipends are paid from research grants and not from training grants or fellowships (but these NRSA levels are frequently used as guidelines for other stipend levels), a key point of comparison involves changes in the sizes of Research Project Grants (RPGs) over this same time period. In FY1983, the average annual total cost (TDC) for an RPG was $124,080. The RPG average size increased steadily through FY2012 before dropping somewhat in FY2013 (associated with the sequester cut). The RPG cost data are compared with the FY1983 value corrected for inflation or corrected for the BRDPI (Biomedical Research and Development Price Index) below:

RPG plot 3 curves

The average RPG size increased faster than either inflation or BRDPI over this period. However, a couple of caveats are appropriate. First, while the average size has increased, I do not know how the distribution of grant sizes has changed over this period of time. For example, it could be that the increase in the average size has been driven substantially by increases in the sizes of large grants while the sizes of many grants near the median may not have increased as much. This is a hypothesis that must be investigated further.

The changing stipend levels can be compared with the changing average RPG level by looking at the ratio of the stipend levels to the inflation-corrected value for each year divided by the ratio of the RPG average value to the BRDPI-corrected value as shown below:

Predoc-Postdoc ratio vs RPG

This plot confirms that the predoctoral stipend level (corrected for inflation) has grown faster than the level of the RPG average size. In contrast, the postdoctoral stipend did not rise as fast as the average RPG size increased until the increase in the postdoctoral stipend associated with the doubling.

This figure reveals three phases. In the first phase, from approximately 1983 to 1997, the predoctoral stipend grew slightly faster than the average RPG growth while the postdoctoral stipend grew more slowly than the average RPG size.

In the second phase (from 1998 to 2003 (that is, the period of the NIH budget doubling), the growth in the postdoctoral and, particularly, the predoctoral stipend exceeded that for the average RPG size. The predoctoral stipend increased by 70% from $11748 in 1998 to $19968 in 2003 while the postdoctoral stipend increased by 63% from $21000 in 1998 to $34200. Corrected for inflation, these changes amount to 51% for the predoctoral stipend and 44% for the postdoctoral stipend. Over this same period of time, the average RPG size increased from $277700 to $379900, an increase of 37%, or 16% correcting for BRDPI.

The third phase runs from 2004 to the present.  During this period, growth in both stipends and average RPG size has been relatively modest. Predoctoral stipends increased by 6% from $20772 to $22032. This is a drop in value of 14% correcting for inflation. The postdoctoral stipend increased from by 10% $35568 to $39264. This is a drop in value of 10% correcting for inflation. Over the same period, the average RPG size increased from $393700 to $444900. This an increase of 13% but a drop in value of 15% correcting for BRDPI. Thus, the balance between stipend levels and RPG size has been approximately maintained but with a slight loss in relative RPG size due to the larger effect of BRDPI versus normal inflation.

13 responses so far

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

Indirect Cost Distribution Analysis-FY2013

May 13 2014 Published by under Uncategorized

In a recent post, I surveyed indirect cost rates across 50 institutions. In the course of the data analysis related to this post, a colleague pointed out that indirect cost information is available in NIH RePORTER. This surprised me since it did not used to be available and, indeed, further checking revealed that such data are only available for FY2012 and beyond. Nonetheless, the availability of these data does enable analysis of indirect cost expenditures aggregated over the NIH portfolio (as opposed to indirect cost rate data).

I downloaded such direct and indirect cost data for all R01 grants for FY2013 and calculated the ratio of indirect costs (IDC) to direct costs (DC) for all grants with direct costs over $10,000 and with non-zero indirect costs. These 23773 grants are broken down in three groups:  Type 5 (non-competing renewal) grants, Type 1 (new) grants, and Type 2 (competing renewal) grants. For this analysis, I did not include supplements (Type 3) or other types. The IDC/DC ratios are plotted below. Also shown are lines corresponding to ratios of 0.40, 0.50, and 0.60.

IDC vs DC plot 2013-New

The ratios of total indirect costs to total direct costs are 0.473, 0.469, and 0.514 for Type 5, Type 1, and Type 2 grants, respectively. For the whole collection, this ratio is 0.475. Note that indirect costs on a given grant may be less than that expected based on the indirect cost rates because of exclusions such as equipment or, less frequently, greater due to subcontracting costs.

Given the small differences in overall IDC/DC ratios for the three Types of R01 grants, how do the distributions of IDC/DC ratios differ? These distributions are shown below.

Type 5,1,2 Histogram

These distributions are quite similar. There does appear to be a smaller fraction of Type 2 grants with relatively low IDC/DC ratios compared with Type 5 and Type 1 grants. This may reflect differences in the institutions from which investigators typically succeed in renewing RO1 grants, although this hypothesis will require further analysis.

Some commenters have suggested that a disproportionate fraction of grants going to a set of institutions with relatively high indirect cost rates could distort the overall distribution of funds. As a model of this behavior, suppose that grants were ranked from the highest IDC/DC ratio to the lowest IDC/DC ratio and that the top (highest IDC/DC) grant had direct costs 0f 1.2 times the average and the bottom (lowest IDC/DC) grant had direct costs 0f 0.8 times with average with grants in between funded at intermediate values linearly varying between 1.2 and 0.8 of the average. To analyze this distribution, let us plot the cumulative total funding versus the rank from highest to lowest IDC/DC as shown below.

Integrated cost simulation 0.2 plot

The plot shows that the curve is concave down. The dotted red line shows the expectation with no differences between grant amount at different IDC/DC ratios (i.e. a straight line).

How does the FY2013 distribution compare with this simulation? The corresponding plot is shown below:

Integrated 2013 plot

In contrast to the simulation, the curve is nearly straight for high values of the IDC/DC ratio and then slightly concave up (rather than down) for lower values. Although there are certainly other ways to analyze this distribution, this first pass analysis does not support the notion that the distribution of a disproportionate amount of funding going to a subset of institutions with relatively high IDC rates results in a substantial increase in overall indirect cost expenditures compared with other distributions.

As I noted above, data are also available for FY2012. It will be interesting to see if there are any significant differences between FY2013 and FY2012 although I will be surprised if there are. In addition, I am trying to find out if NIH can release data for a wide range of years so that trends over time could be examined.


7 responses so far

Indirect Cost Rate Survey

May 10 2014 Published by under Uncategorized

In a recent post, DrugMonkey pointed out an interesting analysis posted by regarding funding trajectories of a list of the top 50 (actually 49) institutions in terms of the level of NIH support.

This post reminded me that, some time ago, I had searched for a list of indirect cost rates (a.k.a. F&A rates) for a range of institutions in order to have a factual framework for some of the notions about the range of these rates that fly around the internet. Although I believe such a list used to be available online, I Googled in vain. I had started to compile such a list by searching for data from individual institutions but I never finished it. Motivated by the new post, I finished the list for these 49 institutions...

Institution                               Rate(%) Link

Johns Hopkins University     62.0    JHU

University of Pennsylvania   60.0    Penn

University of Michigan          55.5    UMich

UCSF                                         56.5    UCSF

University of Washington     54.5     UWash

Yale University                        66.0     Yale

UCLA                                         54.0     UCLA

Washington U.-St. Louis        52.0     WashU

University of Pittsburgh         52.5     Pitt

U. Wisconsin, Madison           53.0    Wisc

UC San Diego                            55.0    UCSD

Columbia University               60.0    Columbia

Stanford University                 60.5     Stanford

UNC-Chapel Hill                      52.0     UNC

Duke University                       57.0     Duke

University of Minnesota         52.0     Minn

Harvard University                  61.5      Harvard

Vanderbilt University              56.0     Vanderbilt

Mass General Hospital           74.0      MGH

Emory University                     56.0     Emory

Baylor College of Medicine     56.5     BCM

Case Western University        58.5      Case

U. Alabama at Birmingham    47.0      UAB

University of Rochester          53.5      Rochester

Scripps Research Inst.             89.5      N/A (see below)

Brigham-Women's Hosp.       76.5       BWH

New York University               69.5       NYU

UT Southwestern Med            59.0       UTSW

Northwestern U.-Chicago       54.5       Northwestern

University of Iowa                    51.0       Iowa

University of Chicago              58.0       UofC

University of Virginia              58.0       UVa

Boston University                    62.5        BU

U. of Colorado, Denver           54.5        CU-Denver

UC Davis                                   54.5        UCDavis

University of Utah                  49.0         Utah

Oregon Health-Science U.     54.0         OHSU

U. of Southern Cal.                 64.0          USC

UC Berkeley                             56.5          Cal

Univ. of Florida                       49.0          Florida

Ohio State University             53.5          OSU

U. of Maryland, Baltimore    53.5           UMd-Baltimore

Mt. Sinai School of Med.      69.5            MtSinai

Penn State University           49.5            PSU

Tufts University                     65.0           Tufts

UC Irvine                                 54.0           UCI

U. Mass. Med. Worcester     66.5            UMassMed

M.D. Anderson                       58.0           N/A (See below)

Mass. Inst. Technology         56.0           MIT

The rates were available online for almost all of the institutions, usually on the website of the institutional Office of Sponsored Projects. However, for a couple of institutions, I could not find the rates. However, a thoughtful colleague pointed out that such information should be deducible from data in NIH RePORTER by plotting indirect versus direct costs for a series of grants from a given institution. The success of this approach is shown below for R01 data from FY2013 for the University of Pittsburgh.Pitt-2013-R01 plot

The data show a clear limiting line that corresponds to the published rate of 52.5%. The points with lower overall indirect cost rates presumably reflect grants with equipment costs excluded from indirect costs. The small number of points with higher overall indirect costs presumably reflect the impact of subcontracts.

Having validated this approach, I was able to determine the indirect cost rates for Scripps Research Institute and M.D. Anderson Cancer Center which I could not find online. The relatively high indirect cost rate of 89.5% for Scripps compares with other independent research institutes such the Salk Institute (90.0%) and Cold Spring Harbor Laboratory (88.5%).

Note that these rates are not determined by NIH. Instead, they are the product of elaborate negotiations between each institution and the "cognizant federal agency" according to an Office of Management and Budget document, Circular A-21. The cognizant federal agency is often the Department of Health and Human Services, but it can also be other agencies such as the Office of Naval Research. The indirect cost rate is calculated based on 9 "cost pools."  These include 5 facilities pools (building depreciation and use allowances, interest on debt associated with selected equipment and buildings, equipment depreciation, operations and maintenance expenses, and library expenses) and 4 administrative pools (general administration, departmental administration, sponsored projects administration, and student services and administration). The administration component has been capped at 26% for a number of years. For those with severe insomnia, the University of Cincinnati website has a relatively clear description of their indirect cost calculation and allocation.

My impression is that the portion of the NIH budget going to indirect costs has increased slightly over the past decade, but only slightly. Although I have requested such data from NIH, they do not appear to have been compiled. A project for another day...


23 responses so far

The Recovery from the Recovery Act-Part Deux

May 05 2014 Published by under Uncategorized

In a previous post, I examined the awards made as part of the American Recovery and Reinvestment Act (ARRA) and sorted them according whether these awards were made to investigators who had other funding in FY2008 ("Existing") or to investigators who did not have funding in FY2008 ("New"). I have now repeated the analysis in terms of investigators rather than awards, have examined the funding status of these investigators prior to receiving ARRA funding in a more refined way, and have investigated the funding status of the investigators subsequent to FY2010 when ARRA funding terminated.

The key results from this analysis are as follows:

5182 investigators received 1 or more non-supplemental ARRA award.

Of these:

2526 were funded with some NIH in FY2008

449 were funded in FY2007 but not FY2008

273 were funded in FY2006 but not 2007 or 2008

1934 were not funded in FY2006, FY2007, or FY2008. Some of these appear to be "New Investigators" by the NIH definition while others either have had R-mechanism funding in the past or have been funded by other NIH mechanisms.

Further examination of these groups revealed a substantial difference in the mechanisms by which these investigators were funded with ARRA funds.

ARRA Mechanism plot

More than 40% of the investigators who had been funded in FY2006, FY2007, or FY2008 received an R01 award through ARRA. The majority of these are likely 2-year R01s although the distribution of these awards between 2-year R01s and longer R01s with additional years funded through non-ARRA funds was not investigated further. In contrast, less than 20% of the awards to investigators who had no R-funding from FY2006-FY2008 were R01s. Instead, this group of investigators were funded through a mixture of mechanisms with smaller budgets including R21s, R03s, and R15s. The fraction of ARRA-specific RC1 ("Challenge Grants") and RC2 ("Grand Opportunity Grants") was approximately the same across all four groups.

With these lists of investigators available, it was possible to examine funding subsequent to ARRA. Overall, 2418 of the 5182 investigators were funded in FY2011 (47%). This breaks down as follows:

1760 of 2526 investigators who were funded in FY2008 (70%)

174 of 449 investigators who were funded in FY2007 (but not FY2008) (39%)

88 of 273 investigators who were funded in FY2006 (but not FY2008 or FY2007) (32%)

396 of 1934 investigators who were not funded in FY2008, FY2007, or FY2006 (20%)

In FY2012, 2532 ARRA funded investigators were now funded with 2109 of those also funded in FY2011, 423 newly funded in FY2012, and 309 funded in FY2011 but not FY2012.

This analysis reveals approximately 60% of the non-supplemental ARRA awards went to investigators who were already funded or had been recently funding prior to ARRA while approximately 40% of these awards went to investigators with no recent history of R-mechanism funding. Of the later group, about 20% were refunded in FY2011. About 9% of this group were refunded in FY2012.


23 responses so far

EB Sustainability Discussion...Input Sought

May 01 2014 Published by under Uncategorized

At the recent Experimental Biology meeting, the ASBMB Public Affairs Advisory Committee organized a panel discussion on building a more sustainable biomedical research enterprise. This builds on a white paper produced earlier by the group.

To frame this discussion, I presented some data relating to impact of the NIH budget doubling

NIH Fig 1on the number of investigators competing for NIH support

426LineGraphand the number of PhD recipients.


The doubling occurred from 1998 to 2003 and the NIH appropriation has been slightly worse than flat since FY2003 (with the exception of the large bolus of funds associated with ARRA). The doubling, however, did encourage institutions to increase research capacity by hiring faculty, constructing research space, and building departments. Of course, this took time with some of the growth occurring during the last couple of the years of the doubling, but most of it coming after the doubling ended and the NIH budget was nearly flat. As can be seen above, the number of distinct investigators applying for grants over a five-year period grew from approximately 56,000 in 1998-2002 to approximately 83,000 in 2008-2012, an increase of 48%.

In parallel, the increase in available research funds during the doubling led to increases in the sizes of graduate school classes. These new PhD students started graduating in numbers in 2003. This produced an increase in basic biomedical PhDs from approximately 5300 in 2003 to approximately 7800 in 2009 and an increase from 3000 to 4000 PhDs in clinical sciences over the same period. Many of these young scientists went on to postdoctoral fellowships and have been entering the market for both academic and non-academic careers in recent years.

In response to the panel, BiochemBelle (who live-tweeted the panel discussion for ASBMB) posted concerns from trainees about how to avoid being excluded from discussions about building a more sustainable biomedical research enterprise. At least from my perspective, these sustainability discussions are largely about such young scientists and those who will follow them. Please feel free to use this forum to raise concerns, make suggestions, request data or analyses, or suggest other ways in which you would like your voices heard.

2 responses so far

Longitudinal PI Analysis: Distributions

May 01 2014 Published by under Uncategorized

For a previous post, I examined the pool of R-funded investigators over the period from FY2007 to FY2013. Of course, some of these 53526 investigators were funded for all 8 years over this period whereas others were funded for a smaller number of years. The distribution of the number of investigators who were funded from 1 though 8 years over this period is shown below:

The distribution of the number of years funded (from 1 to 8) over the 8-year period from FY06 to FY13.

The distribution of the number of years funded (from 1 to 8) over the 8-year period from FY06 to FY13.

The distribution is shows peaks at 2 years (at 19.2%), likely related to 2-year award mechanisms such as R21s, and at 8 years (at 13.5%), related to established investigators with continuing R01 funding.

The distribution of median funding per investigator as a function of the number of years funded is shown below:

Median annual R-mechanism funding as a function of the number of years funded.

Median annual R-mechanism funding as a function of the number of years funded.

The median annual funding per investigator falls almost linearly from the high for investigators funded all 8 years.

The distribution of total funding as a function of the number of years funded is shown below:

The distribution of total funding allocated as a function of the number of years investigators have been funded over FY2006-FY2013

The distribution of total funding allocated as a function of the number of years investigators have been funded over FY2006-FY2013

This figure reflects both the fact that investigators have been funded for more years have more opportunities to accrue total funding as well as the differences in median funding levels shown above. The investigators funded for all 8 years account for 39% of the total funding over this period for these mechanisms (which is $105 B).

The distribution of annual grant support for the investigators who were funded all 8 years is shown below:

The distribution of the number of investigators (all of whom were funded for all 8 years) as a function of their average annual total funding.

The distribution of the number of investigators (all of whom were funded for all 8 years) as a function of their average annual total funding.

As anticipated from the earlier figure, the median annual total cost funding for this group of investigators is approximately $600,000. Note that this generally reflects multiple awards per investigator per year. A substantial tail extending to well above $2 M per year in annual funding is apparent. The largest of these reflects large epidemiological studies that are supported by R01 grants. Others reflect investigators with a larger number of smaller R01s and other awards.

Finally, the distribution of mean funding per investigator is shown below:

Mean annual R-funding per investigator per year from FY2006 to FY2013

Mean annual R-funding per investigator per year from FY2006 to FY2013

This plot reveals a substantial (approximately 10%) increase in mean funding per investigator in going from FY2008 to FY2009, associated with ARRA, as discussed in the previous post.

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