Archive for: May, 2015

R01-equivalent PIs: 1985-2014

May 28 2015 Published by under Uncategorized

I recently posted data on the number of unique NIH PIs for all mechanisms listed in the NIH RePORT database.

I have now analyzed data for R01-equivalent grants (primarily R01s but also R23, R29, and R37 (MERIT) awards) as shown below:

R01 PI plot

This shows curves for all PIs (including multiple PIs) and for Contact PIs only. These curves clearly reveal the impact of the NIH budget "doubling" from FY1998 to 2003) and the subsequent decline due to the worse-than-flat NIH budget over the past 12 years (with the exception of the ARRA) funding.

The correction for multiple PIs is significant (although, of course, being PI on a multiple PI grant likely provides fewer resources than being the sole PI on an award of the same size). The 3564 New (Type 1) R01 grants in FY2014, 771 had multiple PIs.

8 responses so far

The Number of NIH PIs 1985-2014: The Effect of Multiple PIs

May 28 2015 Published by under Uncategorized

I recently posted a somewhat startling curve showing the total number of NIH contact PIs for all mechanisms in the NIH RePORT database. This showed a drop in the total number of PIs from FY2010 to the present.

As I lay awake thinking about this curve and what might mean, I thought it might change somewhat if I included all PIs instead of just Contact PIs. Recall that the NIH multiple PI policy only went into effect in around 2005.

I was able to examine this point relatively quickly. The results are shown below:

NIH PI Plot wNonContact


This shows that the inclusion of all PIs decreases the magnitude of the drop since FY2010.

Some other interesting statistics about non-Contact PIs are:

Total Contact PIs:  216,521

Total PIs listed as other than Contact PI:  11,504

PIs who have never been Contact PI:  2,873


10 responses so far

Analysis of Subsequent Years of K99-R00 Program

May 28 2015 Published by under Uncategorized

I had previous done some analysis of the NIH K99-R00 program for the first two cohorts.  I wrote R scripts to assemble information about the R00 and R01 (as well as DP1 and DP2) awards subsequently obtained by K99 recipients and to analyze these results. I included precise grant start and end times rather than simply fiscal years as I had done in my initial analysis.

The results for the first K99 cohort (from fiscal year 2007) are shown below. This shows the number of investigators (out of 182 initial K99 awardees) who had K99 awards, R00 awards, or R01 (or DP1, or DP2) awards aligned with the start dates for the initial K99 award at time 0.

2007 K99 Cohort Plot-3

This shows that more than 90% of these K99 awardees transitioned to the R00 phase and that more than 100 of these PIs had obtained at least one R01 (or equivalent) award as shown previously but now with more precision about the timing of these awards.

With these scripts in hand, it was straightforward to analyze subsequent K99 cohorts. The results are shown below:


K99 Awards Plot


This graph reveals that the overall pattern for the K99 phase is remarkably consistent from year to year, with substantial transitions at the end of year 1, a steady decline and then a sharp drop at the end of year 2, and the remaining ~20% of PIs transitioning off the K99 by the end of year 3.

The results for the R00 phase are shown below:

R00 Award Plot


Again, the pattern is quite consistent. The fraction of K99 awardees who have transitioned to the R00 phase is approximately 50% at the end of year 2 (since the start of the K99 award) and peaks at between 80 and 90% in the middle of year 3. The curves are different for the FY2010, FY2011, and FY12 K99 cohorts since they have not yet had time to fully transition, but the curves look quite similar for the regions that overlap the other curves.

The final curve shows the transition to R01 awards (I also included DP1 (Pioneer) and DP2 (New Innovator) awards).

R01 Award Plot-2


Here, the curves are more different. For the first (FY2007) cohort, more than 50% of the K99 awardees have transitioned to R01 funding. More than 40% of the FY2008 cohort have transitioned, but comparison of the FY2007 and FY2008 curves suggests that this cohort is transitioning more slowly or will not achieve the same level of the FY2007 cohort. This trend continues with the FY2009 cohort. Of course, these attempted transitions to R01 funding are occurring over the period where the overall number of NIH supported PIs dropped (as revealed in my previous post). The FY2010 cohort showed an initial burst above the FY2008 and FY2009 curves but has slowed since then. It is too early to say much about the FY2011 and FY2012 cohorts.

The ability to analyze these data in kinetic detail with relative ease allowed some comparisons that were much harder to make in my previous analysis. I am impressed with the continuing development of R by a large open community (especially Hadley Wickham) that are making R an ever-more-powerful tool.

14 responses so far

Analyzing NIH Data with R

May 28 2015 Published by under Uncategorized

Most of the analysis of NIH data that I have done with NIH data has been done using Excel. While Excel does have some useful features, it has many limitations. My son who, as an actuary, does considerable data analysis for a living, urged me to migrate to a more powerful platform, R, for my analyses. He can be quite convincing and I have spent time over the past month developing some rudimentary R skills (in part through an on-line course). I am now fully convinced that he was right.

I downloaded all of the data used by NIH RePORTER (from NIH ExPORTER) and wrote R scripts to parse the data into a forms that could be easily analyzed by R. The full file has 1,907,841 grant records with readable contact PI numbers for fiscal years 1985 to 2014. These correspond to 216,521 unique contact PIs.

As an initial exercise with these data, I decided to plot the number of unique contact PIs as a function of fiscal years. The result is shown below:

Unique PI Plot-2


What I attempted as a test of my data analysis skills revealed a striking result. The number of unique contact PIs had grown almost linearly from 1985 to about 2009-2010 (the ARRA years) but subsequently dropped quite sharply from 2010 to 2014. This graph provide much clearer evidence for "the cull" than I anticipated.

Despite this bottom line, considerable work remains to be done to probe this further since this includes a wide variety of mechanisms. With the powerful file manipulation and analysis tools in R, this should be relatively straightforward.

Let the analysis begin!

33 responses so far

Please comment: NIH RFI on "Optimizing Funding Policies..."

May 07 2015 Published by under Uncategorized

NIH released an RFI on April 2 on Optimizing Funding Policies and Other Strategies to Improve the Impact and Sustainability of Biomedical Research. Responses are Due by May 17th (10 more days).

Please take the time to go and provide input. My recent post on the potential emeritus award RFI should make it very clear that your input is necessary if you don't want the response to be dominated by those with quite different perspectives from yours.

Here is the link for the RFI and the comment areas are listed below to get your thinking started.

Please limit comment to a maximum of 500 words.

Please limit comment to a maximum of 500 words.

Please limit comment to a maximum of 500 words.

Please limit comment to a maximum of 500 words.
Now's your chance. There is really no excuse for not contributing your thoughts.

7 responses so far

NIH "Emeritus Award" RFI Results-Update

May 07 2015 Published by under Uncategorized

Following on my previous post on the responses to the NIH RFI regarding a potential "emeritus" award, several commenters asked to see the responses. Unfortunately, WordPress does not appear to have a mechanism for posting such files. However, I have posted the spreadsheet through GoogleDocs. Please feel free to share your reactions.

As a further update, as first pointed out to me by @ChrisPickett5, the latest draft of the 21st Century Cures Act currently being developed by the House Energy and Commerce Committee includes a section about a "Capstone Award" (pg. 26-27).  It is quite odd to see a new grant mechanism from NIH being discussed as an addition to the law that governs NIH, as opposed to being developed by NIH using existing authorities. It is unclear if this is coming from the NIH or from one or more members of Congress interested in facilitating senior faculty transitioning out of NIH-supported research.

36 responses so far

NIH "Emeritus Award" RFI Results-FOIA Request-Initial Observations

May 06 2015 Published by under Uncategorized

After hearing comments at the Experimental Biology meeting that responses to the NIH Request for Information (RFI) about a potential "emeritus" award were substantially more positive that those posted on the Rock Talk blog on the subject, I submitted a Freedom of Information Act (FOIA) request to obtain what I could about the RFI responses.

Yesterday (less than 6 weeks after I made the request), I received the response. The key item was an Excel spreadsheet with meaningful responses from 195 individuals and 3 scientific societies (American Society for Biochemistry and Molecular Biology, Genetics Society of America, American Association of Immunologists). The names and email addresses of the individuals (as well as some other bits of information) were redacted although institutional affiliation information was included where provided.

As a first pass at the analysis, I coded each response as Supportive of an Emeritus Award, Not Supportive of an Emeritus Award, or Mixed. The results were almost evenly divided with 92 Supportive, 85 Not Supportive, and 21 Mixed.

Some of the responses disclosed that the respondent was a senior scientist who would potentially have been or would be a potential applicant for an emeritus award. I searched the responses for such disclosures and identified 17 individuals. All 17 were supportive of the concept of a potential emeritus award.

I also examined the institutional affiliations of the respondents where provided. The institutions for which more than 2 responses were received included:

Harvard Medical School (including Brigham and Women's, Mass General, and Beth Israel Deaconess Hospitals) 11

Johns Hopkins University 6

University of Colorado 5

University of Washington 4

University of Michigan 4

University of Maryland 4

University of Massachusetts Medical School 3

Tufts University 3

University of Kentucky 3


Note that this parallels, to some extent, institutions that have a large number of grantees (Harvard Medical School, Johns Hopkins , University of Michigan, and University of Washington are in the top ten in terms of overall NIH funding. However, Harvard Medical School and the three affiliated hospitals listed account for approximately $300M in NIH funding (or ~1 %) yet they accounted for 11/198 = 5.5% of the responses; 7 out of these 11 responses were scored as positive.

I will continue to examine the responses and share some of the more interesting comments.

What are the take-home lessons here?

First, the response rate is typical for this sort of RFI at a few hundred responses. This represents a very small selection of the biomedical research community, substantially less than 1% of grantees and applicants. Note that I used the term selection instead of sample since their is certainly bias in who chose to take the time to respond.

Second, the responses are more substantially more positive than those seen on blogs. Of course, the blog response is likely biased toward those who are younger and more likely to be negative while the RFI response may be biased toward those with self-interested positions.

Third, the FOIA process here was relatively painless and quick in this case.

I urge you whenever NIH issues an RFI on a topic of interest to you or your colleagues, take the time to take a look at it and respond as appropriate. Your voice can't be heard if you don't speak out and it only takes a few minutes to respond.

26 responses so far