Archive for: January, 2015

Estimated Publication Outputs as a Function of Number of R01 Grants per PI

Jan 30 2015 Published by under Uncategorized

In a recent post, I presented the distribution of the number of investigators who were PIs of from 1 to 6 R01 grants in fiscal year 2014.

The significance of this distribution is hard to access without some knowledge of the outputs from these grants. This is a challenging problem. Here, I attempt to estimate some of these outputs. The approach I used in very much an approximation, but some important trends can be discerned.

To estimate outputs, I collected the results in PubMed for publications from 2012 to the present for the 7 PIs with 6 R01s, the 31 PIs with 5 R01s, and randomly selected samples of 30 PIs with 1-4 R01s (screened only for name ambiguity issues or the present of large amounts of non-R01 funding).

The distributions of the number of publications over this period as a function of the number of R01s is shown below:

Pub Number Plot-250

The median number of publications for the sample of PIs with 1 R01 is 11.5. This doubles to 23.5 for the sample of PIs with 2 R01s. Essentially the same median was found for the sample of PIs with 3 R01s. The median number then increases monotonically to 44 for the PIs with 6 R01s.

In addition to the trends, it is important to note the substantial variations that occur at each level of funding. This reflects difference between publication patterns in different fields as well as the performance of individual PIs.

These data are re-plotted after normalizing the number of publications by the number of R01 awards below:

Pub Ct Normalized plot

This graph shows that the median number of publications per R01 grant is essentially constant at 11.5 for PIs with one or two grants and then drops to approximately 7.5 for PIs with 3-6 R01s.

A criticism of my earlier analyses of this type has been the lack of separation of publications in high impact journals (for this purpose Science, Nature, and Cell) because of the presumed large cost of generating such publications. The numbers of such publications as a function of PI from the assembled publication database were determined and added to the plot below.

Pub Count with CNS

PIs have between 0 and 16 Science, Nature, and Cell publications over this period. Overall, 40% of the PIs have at least 1 such paper including 17% of the PIs with a single R01 and between 37-57% of the other groups. The number of Science, Nature, and Cell publication is modestly correlated with the total number of publications with a correlation coefficient of 0.26.

The average number of Science, Nature, and Cell papers versus average total number of publications for the six groups of PIs is plotted below:

Fraction of CNS paper plot

A nearly linear relationship is observed with a slope of approximately 3.5 Science, Nature, and Cell papers per 100 total publications. From these samples, this proportion does not vary dramatically as a function of the number of R01 grants.

Again, this is a rough estimation to much many caveats apply. First, of course, publication numbers without further analysis, is a limited measure of true productivity. Second, the publication records are assembled over a period of time over which funding likely varies to some (and differing) extents for each PI. Third, relatively small samples were used for the groups with 1-4 R01s. Nonetheless, I hope these data will provide a framework for discussions about the implications of the current distribution of R01 resources across PIs and those of potential alternatives.

UPDATE:

In response to the comment from Drugmonkey, I have plotted the number of publications normalized to the number of R01 grants with each Science, Nature, and Cell paper counted as 5 publications below:

Pub Count-CNS-5 graph

 

This graph looks very similar to the previous one while such papers were counted as single publications. This similarity is to be expected given that the fraction of Science, Nature, and Cell papers out of total publications does not depend substantially on the number of R01 grants as shown by the approximately linear relationship above.  Indeed, this graph looks similar even when Science, Nature, and Cell papers are weighted as 10 publications.

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R01 Size Distribution for PIs with a Single R01/R37 Grant in FY2014

Jan 28 2015 Published by under Uncategorized

A comment to my previous post asked about the size distribution for R01s for investigators with a single R01 grant. This distribution is shown below:

Single R01 histogram figure

The median is $371K (total costs) with an interquartile range of ($464K - $318K) = $146K. This distribution is very similar to that for all R01s for FY2013 presented in an earlier post.  Approximately 1/3 of the grants fall outside of a Gaussian distribution centered around the mode of the distribution. These represent grants that are outside of the range determined by the modular cap.

UPDATE:

Here is a plot of the cumulative fraction of total R01 funding going to PIs with single R01 grants as a function of R01 size from smallest to largest:

Single R01 Cum graph

 

Grants up to the modular cap limit of approximately $375K total costs account for approximately 55% of the total R01 expenditure for PIs with single R01 grants.

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R01 Distribution for 2014 by PI and IC

Jan 26 2015 Published by under Uncategorized

There has been much discussion (including recently e.g. here and here) about the distribution of the number of grants per PI. Here I examine the distribution of the R01-equivalent (R01 and R37 (MERIT) awards for Fiscal Year 2014.

New (Type 1), Competing Renewal (Type 2), and Non-Competing Continuation (Type 5) award information was downloaded from NIH RePORTER. This yielded 23526 awards. Awards were groups according to the PI. For awards with multiple PIs, only the corresponding PI was considered. The number of awards ranges from 1 to 6. This distribution is shown below:

R01 PI distribution-2

PIs with 1 R01 grant account for 77.8% of the awards whereas those with 6 awards account for 0.04% of the awards.

This can also be viewed in terms of the fraction of the dollars that are devoted to each group of PIs as shown below:

R01 grant amount pie chart

The fraction of the $9.9 B in this pool (not that this does not include supplements) going to investigators with single R01s is 60.0%. The average size of an R01 award does not vary significantly between the different groups of PIs so that PIs with 2 R01s have (on average) twice as much R01 support as those with 1. The percentage of R01 funds going to PIs with 2 awards is 27.7%. The percentage of R01 funds going to those with 6 awards is 0.17%.

I also examined the distribution of funds to PIs as a function of the NIH institutes and centers (ICs) that fund them. I have previously examined the distribution of R01 funds by IC. Here, PIs are classified according to the IC or ICs that fund them. If a PI has R01s from more than 1 IC, then they are classified as investigators of all ICs and all funds that this PI receives are counted for the involved ICs. This results in the double-counting of funds, but it is germane to issues such as the special scrutiny given to well-funded investigators.

The distributions of the number of PIs as a function of annual total cost R01 support for the four largest institutes are shown below:

4 IC PI Curves-1

 

Both the first and second peaks for NIGMS and NCI are shifted to smaller amounts relative to the corresponding peaks for NIAID and NHLBI. This may reflect policies of cutting budgets upon award and, in some cases, in out years.

The region of this graph for annual total R01 costs greater than $1 M is shown below:

4 IC Pi Curves-2

 

The curve for NIGMS is below those for the other three institutes, likely due to the NIGMS well-funded investigator policy (although some of the difference could be due to differences in the costs of the types of research supported by different institutes).

These data, together with some analysis of the publication patterns for these groups of investigators currently underway, may be useful as new policies regarding the distribution of funds across groups of investigators are proposed and discussed.

UPDATE

I presented graphs of the number of PIs as a function of funding level for just the four largest institutes. Below is a table that shows the number of PIs for each institute and center (recalling that a PI is associated with an IC if they have at least 1 R01 with that ICs; PIs with R01s from multiple ICs are associated with multiple ICs) as well as the amounts (in $ X1000) for the 10th, 50th, 90th, and 99th percentiles for each IC. Thus, for NIGMS, 10% of the PIs have total R01 funding less than or equal to $277K annual total costs while 99% of NIGMS PIs have less than or equal to $1604K in annual total R01 costs. The ICs with the lowest levels of the 99th percentile are NLM, FIC, NIDCR, NCCAM, and NIGMS. Those with the largest levels of the 99th percentile are NIDA, NICHD, NHGRI, NIAAA, and NIDCR.

IC Number of PIs 10th %ile 50th %ile 90th %ile 99th %ile
NIGMS 2939 277 348 845 1604
NCI 2675 283 394 1005 1979
NHLBI 1996 363 500 1207 2212
NIAID 1884 344 463 1138 2171
NINDS 1640 316 384 1036 1856
NIDDK 1633 309 426 1034 1889
NIMH 1212 346 568 1226 2231
NICHD 894 286 496 1106 2535
NEI 802 331 394 920 1810
NIDA 797 304 570 1318 2633
NIA 792 297 517 1223 2291
NIAMS 595 315 386 1052 2052
NIDCD 491 308 400 909 1394
NIEHS 356 310 458 1015 1897
NIAAA 339 297 507 1127 2354
NIDCR 318 364 474 1161 2333
NIBIB 308 315 482 1187 2251
NINR 162 370 511 946 1971
NHGRI 119 301 505 1200 2442
NCCAM 114 329 552 1166 1584
NIMDH 76 337 393 1013 1931
NLM 57 283 399 1041 1293
FIC 22 49 53 388 1393

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