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What Contributes to the Success of Early Career Scientists? – A NIAID Look

At NIH, we are heavily invested in our workforce and in understanding the barriers they face. What characteristics do they share? How do they compete in the current hypercompetitive environment? When do they stop applying to NIH (drop out), even after receiving their first award?

Staff from the National Institute of Allergy and Infectious Diseases (NIAID) delve into these questions in a paper published recently in PLOS ONE , whose findings I’d like to highlight today. Here, Drs. Patricia Haggerty and Matthew Fenton looked at factors that may contribute to the success of early-career investigators and if these factors affect all junior researchers equally.

This analysis focused on a cohort of investigators who received their first NIH R01-equivalent research grant (referred to as R01-e) awards (which included R01s, program projects and cooperative agreements) specifically from NIAID between fiscal years 2003 and 2010 (n=1,496). By following their subsequent grant applications through 2016 (or through their final application, whichever came first), our colleagues tracked funding outcomes of this cohort of investigators – referred to as “Early NIH-funded Investigators” (ENI) in the paper. ENI who obtained another R01-e award (from any NIH research Institute) were referred to as “successful” or “funded” ENI, whereas those did not were referred to as “unfunded” ENI.

As Table 1 shows, 57 percent of the cohort ENI were successful – that is, they obtained at least one additional R01-e award – by 2016. ENI from the first four cohort years (2003-2006) had a higher funding rate (60 percent) than those from the latter 4 years (53 percent). This finding may be due in part, the authors suggest, to higher R01 paylines at that time.

Table 1 displays funding outcomes per cohort year.

 

 

 

 

Table 1

Among study ENI who continued to apply for NIH grants for at least 5 years after receiving their first award – whether or not they had obtained new funding by then – 77 percent remained, and 23 percent had dropped out. The authors suggest this may represent an improvement compared to earlier data on NIH first-time R01-e awardees. The Kaplan–Meier curve in Figure 1 below shows the length of time awardees remained in the NIH applicant pool after their first R01-e award. For NIAID investigators (using a 1998-2003 cohort, blue line) the dropout rate was steepest between 4 and 5 years (straight red line), by which time 32 percent of investigators had dropped out of the pool (68 percent remained).   For other NIH investigators (using 1989, 1997 and 2003 cohorts), the dropout rate was similarly steep between 4 and 5 years, by which time 43 percent had dropped out (57 percent remained).

Figure 1 shows a Kaplan–Meier analysis describing the length of time awardees remain in the NIH applicant pool after their first R01 or equivalent Award. The X axis represents the number of years since receiving their first R01 or equivalent award, while the Y axis is the percentage of investigators in each cohort who later received an additional research project grant award. The blue, orange, and red lines represent NIAID awardees, other NIH awardees, and the dropout slope between 4 and 5 years, respectively.

Figure 1

Funded ENI, when compared to their unfunded peers, scored better on their initial application (see Table 11 within paper).  They also wrote better quality subsequent applications which were more likely to be judged as competitive during peer review. They submitted more applications per year, had a shorter amount of time between their first award and next application, submitted to multiple NIH Institutes, submitted more renewal applications, and had a longer active time between their first award and final grant application.

Between 2003 and 2016, study ENI submitted a total of 10,228 applications subsequent to their first award (Figure 2).  The funded ENI submitted 8,026 applications, of which just 39 percent were judged as non-competitive. On the other hand, those who did not compete successfully for additional support submitted only 2,202 applications, of which 63 percent were judged as non-competitive.

Figure 2 shows applications scored and judged non-competitive from unfunded and funded investigators in the cohort as a measure of application quality. The x-axis is the fiscal year from 2003 to 2016, while the y-axis is the number of applications from 0 to 1,200.

Figure 2

The authors suggest that successful investigators “displayed remarkable within-person consistency, not only in grant submission behavior, but across multiple behaviors associated with a higher likelihood of future funding.” They go on to mention that those who were successful “developed superior grant writing skills, superior grant submission strategies, and projects with broad relevance and scope.”

The data presented here provide some interesting insights into the characteristics of successful early career investigators. By drawing on such knowledge, we may be better equipped to establish and adapt programs, like the Next Generation Researchers Initiative, that aim to strengthen the future of the biomedical research workforce and bolster the prospects for these scientists for years to come.

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2 thoughts on “What Contributes to the Success of Early Career Scientists? – A NIAID Look

  1. The characteristics of success described as “They submitted more applications per year, had a shorter amount of time between their first award and next application, submitted to multiple NIH Institutes, submitted more renewal applications” could be interpreted as playing the odds. If grantees were selected at random, then of course the people who submit the most grants would ultimately be the most successful. I have seen this basic correlation presented in multiple different NIH reports, editorials ect… , as if it is supposed to be some grand insight into what grant writers are doing wrong. But, I have yet to an investigation or explanation into how or why submitting many more grants than your peers produces a higher success rate if it isn’t just playing the odds. If it is random chance, then we are talking about the wrong problem here, and its not something grant writers are doing wrong but instead something wrong with the process of selecting grants to fund.

  2. In writing “successful investigators “displayed remarkable within-person consistency, not only in grant submission behavior, but across multiple behaviors associated with a higher likelihood of future funding” “, one is left to wonder why it would be viewed as “remarkable”. The straightforward interpretation of the data is that NIH is pushing too hard to inflate the success rate for New / ESI applicants who, at the outset, were weaker and scored less well, and the relatively weaker application (for initial RPG funding as R01-equivalent) was a predictor of being a relatively weaker PI for the long haul. Since each 1 -2 such non-sustainable investments tends to knock one established PI out of the business, it should be asked, “was it wise to have made that choice?” Indeed, simple arithmetic using the average number of R01-equivalent grants awarded to New/ESI PIs per year times what would be a sustained period of contribution (20-25 year) reaches a number greater than or equal to the total number of RPG. In a rational world, that would mean dialing back somewhat on the amount of “outside the payline” or ‘select pay’ grace provided to New / ESI applicants. But, ‘rational world’ and ‘Congressional mandate / NIH policy’ are not quite oxymoron but more than ‘imperfectly correlated’.

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