Most funding agencies employ panels in which experts review proposals and assign scores to them based on a number of factors (such as expected impact and scientific quality). However, several studies have suggested significant problems with the current system of grant peer review. One problem is that the number of reviewers is typically inadequate to provide statistical precision (Kaplan et al., 2008). Researchers have also found considerable variation among scores and disagreement regarding review criteria (Mayo et al., 2006; Graves et al., 2011; Abdoul et al., 2012), and a Bayesian hierarchical statistical model of 18,959 applications to the NIH found evidence of reviewer bias that influenced as much as a quarter of funding decisions (Johnson, 2008).

Although there is general agreement that peer review can discriminate sound grant applications from those containing serious flaws, it is uncertain whether peer review can accurately predict those meritorious applications that are most likely to be productive. An analysis of over 400 competing renewal grant applications at one NIH institute (the National Institute of General Medical Sciences) found no correlation between percentile score and publication productivity of funded grants (Berg, 2013). A subsequent study of 1492 grants at another NIH institute (the National Heart, Lung and Blood Institute) similarly found no correlation between the percentile score and publication or citation productivity, even after correction for numerous variables (Danthi et al., 2014). These observations suggest that once grant applications have been determined to be meritorious, expert reviewers cannot accurately predict their productivity.

In contrast, a recent analysis of over 130,000 grant applications funded by the NIH between 1980 and 2008 concluded that better percentile scores consistently correlate with greater productivity (Li and Agha, 2015). Although the limitations of using retrospective publication/citation productivity to validate peer review are acknowledged (Lindner et al., 2015; Lauer and Nakamura, 2015), this large study has been interpreted as vindicating grant peer review (Mervis, 2015; Williams, 2015). However, the relevance of those findings for the current situation is questionable since the analysis included many funded grants with poor percentile scores (>40th percentile) that would not be considered competitive today. Moreover, this study did not examine the important question of whether percentile scores can accurately stratify meritorious applications to identify those most likely to be productive.

We therefore performed a re-analysis of the same dataset to specifically address this question. Our analysis focused on subset of grants in the earlier study (Li and Agha, 2015) that were awarded a percentile score of 20 or better: this subset contained 102,740 grants. This percentile range is most relevant because NIH paylines (that is, the lowest percentile score that is funded) seldom exceed the 20th percentile and have hovered around the 10th percentile for some institutes in recent years.

These observations suggest that despite the overall ability of reviewers to discriminate between extremely strong grant applications and the remainder, they have limited ability to accurately predict future productivity of meritorious applications in the range relevant to current paylines. This may contribute to a pervasive sense of arbitrariness with regard to funding decisions and dissatisfaction with the peer review system (Pagano, 2006; Fang and Casadevall, 2009; Costello, 2010; Germain, 2015). Perhaps most importantly, these findings contradict the notion that peer review can determine which applications are most likely to be productive. The excellent productivity exhibited by many projects with relatively poor scores and the poor productivity exhibited by some projects with outstanding scores demonstrate the inherent unpredictability of scientific research. The data also suggest that current paylines are inadequate to fund the most productive applications and that considerable potential productivity is being left on the table at current funding levels (Berg, 2011).

It should be noted that percentile scores and publication/citation productivity are not necessarily independent. Citation practices have evolved over time, and citations per paper approximately doubled between 1980 and 2004 (Wallace et al., 2009). This may have contributed to greater citation productivity for more recent grants, which would tend to have more favorable percentile scores than older ones, as paylines have declined over time, resulting in a correlation between percentile score and citation productivity that is unrelated to the discriminatory ability of the peer review process. In addition, applications that are funded despite scoring worse than the payline tend to be funded later in the fiscal year and are often subject to budgetary reductions that could adversely impact their future productivity. According to a report from the US Government Accountability Office (GAO), 13% of R01 applications funded in fiscal years 2003–2007 were exceptions with scores worse than the payline (GAO, 2009). This may be an additional contributing factor to the correlation between percentile score and publication and citation productivity that is not indicative of peer review discrimination.

These observations have important implications for the grant peer review system. If reviewers are unable to reliably predict which meritorious applications are most likely to be productive, then reviewers might save time and resources by simply identifying the top 20% and awarding funding within this group on a random basis or according to programmatic priorities. In this regard, we refer to our recent suggestion that the NIH consider a modified lottery system (Fang and Casadevall, 2014) and note that the New Zealand Health Research Council has already moved to a lottery system to select proposals for funding in its Explorer Grants program (Health Research Council of New Zealand, 2015).

In summary, while our analysis confirms that peer review has some ability to discriminate between the quality of proposals based on citation productivity over the entire range of percentile scores (Li and Agha, 2015), this does not extend to the critical range of percentile scores between 3 and 20, which is the most relevant subset of applications at current paylines. Furthermore, the best science is not necessarily receiving support under the present system since most applications in this percentile range, whether above or below current paylines, are indistinguishable with regard to citation productivity. The manner in which scarce research funds are allocated deserves greater attention.

A database derived from 102,740 research project (R01) grants funded by the NIH from 1980 through 2008 was obtained from Danielle Li (Harvard University) and Richard Nakamura (NIH Center for Scientific Review). The data included the number of publications acknowledging grant support within 5 years of grant approval (from PubMed), the number of citations of those publications through 2013 (Web of Science) and the percentile score of the application (rounded to the nearest whole number). Parametric statistical analyses were performed in Prism (GraphPad Software, San Diego, CA). The receiver operating characteristic (ROC) and random forest analyses were performed using R (Vienna, Austria) and the pROC (Robin et al., 2011) and randomForest (Liaw and Wiener, 2002) packages.

1. 1. Abdoul H
   2. Perrey C
   3. Amiel P
   4. Tubach F
   5. Gottot S
   6. Durand-Zaleski I
   7. Alberti C
   8. Gagnier JJ

   (2012) Peer review of grant applications: criteria used and qualitative study of reviewer practices

   PLoS ONE 7:e46054.

   https://doi.org/10.1371/journal.pone.0046054

   • Google Scholar
2. 1. Berg J

   (2011)

   Productivity metrics and peer review scores

   NIGMS Feedback Loop Blog.

   • Google Scholar
3. 1. Berg J

   (2013)

   On deck chairs and lifeboats

   ASBMB Today.

   • Google Scholar
4. 1. Costello LC

   (2010) Is NIH funding the “best science by the best scientists”? A critique of the NIH R01 research grant review policies

   Academic Medicine 85:775–779.

   https://doi.org/10.1097/ACM.0b013e3181d74256

   • Google Scholar
5. 1. Danthi N
   2. Wu CO
   3. Shi P
   4. Lauer M

   (2014) Percentile ranking and citation impact of a large cohort of national heart, lung, and blood institute-funded cardiovascular R01 grants

   Circulation Research 114:600–606.

   https://doi.org/10.1161/CIRCRESAHA.114.302656

   • Google Scholar
6. 1. Fang FC
   2. Casadevall A

   (2009) NIH peer review reform--change we need, or lipstick on a pig?

   Infection and Immunity 77:929–932.

   https://doi.org/10.1128/IAI.01567-08

   • Google Scholar
7. 1. Fang FC
   2. Casadevall A

   (2014)

   Taking the powerball approach to medical research

   Wall Street Journal.

   • Google Scholar
8. 1. GAO

   (2009)

   National institutes of health: completion of comprehensive risk management program essential to effective oversight

   • Google Scholar
9. 1. Germain RN

   (2015) Healing the NIH-funded biomedical research enterprise

   Cell 161:1485–1491.

   https://doi.org/10.1016/j.cell.2015.05.052

   • Google Scholar
10. 1. Graves N
    2. Barnett AG
    3. Clarke P

    (2011) Funding grant proposals for scientific research: retrospective analysis of scores by members of grant review panel

    BMJ 343:d4797.

    https://doi.org/10.1136/bmj.d4797

    • Google Scholar
11. Website

    1. Health Research Council of New Zealand

    (2015) Explorer Grants

    http://www.hrc.govt.nz/funding-opportunities/researcher-initiated-proposals/explorer-grants
12. 1. Johnson VE

    (2008) Statistical analysis of the National Institutes of Health peer review system

    Proceedings of the National Academy of Sciences of the USA 105:11076–11080.

    https://doi.org/10.1073/pnas.0804538105

    • Google Scholar
13. 1. Kaplan D
    2. Lacetera N
    3. Kaplan C
    4. Tregenza T

    (2008) Sample size and precision in NIH peer review

    PLoS ONE 3:e2761.

    https://doi.org/10.1371/journal.pone.0002761

    • Google Scholar
14. 1. Lauer MS
    2. Nakamura R

    (2015) Reviewing peer review at the NIH

    New England Journal of Medicine 373:1893–1895.

    https://doi.org/10.1056/NEJMp1507427

    • Google Scholar
15. 1. Li D
    2. Agha L

    (2015) Big names or big ideas: do peer-review panels select the best science proposals?

    Science 348:434–438.

    https://doi.org/10.1126/science.aaa0185

    • Google Scholar
16. 1. Liaw A
    2. Wiener M

    (2002)

    Classification and regression by randomForest

    R News 2:18–22.

    • Google Scholar
17. 1. Lindner MD
    2. Nakamura RK
    3. Smalheiser NR

    (2015) Examining the predictive validity of NIH peer review scores

    PLoS ONE 10:e0126938.

    https://doi.org/10.1371/journal.pone.0126938

    • Google Scholar
18. 1. Mayo NE
    2. Brophy J
    3. Goldberg MS
    4. Klein MB
    5. Miller S
    6. Platt RW
    7. Ritchie J

    (2006) Peering at peer review revealed high degree of chance associated with funding of grant applications

    Journal of Clinical Epidemiology 59:842–848.

    https://doi.org/10.1016/j.jclinepi.2005.12.007

    • Google Scholar
19. 1. Mervis J

    (2015) NIH's peer review stands up to scrutiny

    Science 348:384.

    https://doi.org/10.1126/science.348.6233.384

    • Google Scholar
20. 1. Pagano M

    (2006) American idol and NIH grant review

    Cell 126:637–638.

    https://doi.org/10.1016/j.cell.2006.08.004

    • Google Scholar
21. 1. Robin X
    2. Turck N
    3. Hainard A
    4. Tiberti N
    5. Lisacek F
    6. Sanchez J-C
    7. Müller M

    (2011) PROC: an open-source package for r and s+ to analyze and compare ROC curves

    BMC Bioinformatics 12:77.

    https://doi.org/10.1186/1471-2105-12-77

    • Google Scholar
22. 1. Wallace ML
    2. Larivière V
    3. Gingras Y

    (2009) Modeling a century of citation distributions

    Journal of Informetrics 3:296–303.

    https://doi.org/10.1016/j.joi.2009.03.010

    • Google Scholar
23. 1. Williams RS

    (2015)

    Study: peer review predicts success

    The Scientist.

    • Google Scholar

Author details

1. Ferric C Fang

   Departments of Laboratory Medicine and Microbiology, University of Washington School of Medicine, Seattle, United States

   Contribution

   FCF, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   fcfang@u.washington.edu

   Competing interests

   The authors declare that no competing interests exist.

2. Anthony Bowen

   Department of Microbiology and Immunology, Albert Einstein College of Medicine, New York, United States

   Contribution

   AB, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   anthony.bowen@med.einstein.yu.edu

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0003-1064-8372

3. Arturo Casadevall

   Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States

   Contribution

   AC, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   acasade1@jhu.edu

   Competing interests

   The authors declare that no competing interests exist.

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