Author Archives: Dr. Jake Basson

About Dr. Jake Basson

Jake, who trained in biostatistics and cardiovascular genetics, is a statistical policy analyst in the NIGMS Office of Program Planning, Analysis, and Evaluation. He uses a diverse suite of data science tools to study the Institute’s research portfolios, training programs and funding policies.

More Information About New and Early Stage Investigator MIRA Outcomes

Dr. Peter Preusch
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There has been ongoing discussion—both here and in the general scientific community—related to the first MIRA awards to New and Early Stage Investigators (NI/ESI). One question that arose was why applications were administratively withdrawn. Both the NIH Center for Scientific Review and multiple NIGMS staff members, including the program director with a portfolio of grants closest to the applicant’s area of science, screened the applications. Of the withdrawn applications, a majority (~80%) were returned prior to review because they proposed research that fell outside of the NIGMS mission. Others were withdrawn because the applicant was not eligible for the FOA. After review, some applications were withdrawn because the PI accepted another award that was mutually exclusive with the MIRA. As recommended on the MIRA website and elsewhere, we encourage anyone who intends to apply for the Early Stage Investigator MIRA to discuss their plans with the appropriate NIGMS program director to determine whether the proposed research area is within the mission of the Institute and if the applicant is eligible to apply.

A major NIGMS goal is to support a broad portfolio that is diverse in research topics, approaches, institutions and investigators. This means we are looking carefully at the outcomes of awards, including gender and race/ethnicity data. We are also trying to take proactive steps to prevent bias during the review, for instance by covering the topic as part of reviewer orientations that take place several weeks before the MIRA study sections meet.

In our recent summary of MIRA applicant and awardee demographics, we looked to see how applications from underrepresented groups compared to those from well-represented groups (White and Asian). The p-value for a difference between the distributions of funded and unfunded applications from these groups was 0.63, meaning that there was no statistically significant difference between the two groups. We also compared the MIRA success rates to those of ESI applicants for NIGMS R01s in fiscal years (FY) 2011-2015 (Table 1).

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Trending Young in New and Early Stage Investigator MIRA

Dr. Peter Preusch
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Dr. Jon Lorsch

The MIRA presentation at the September 2016 Advisory Council meeting begins at 17:13.

Following up on the previous post regarding the first MIRA awards to New and Early Stage Investigators, we issued awards to a total of 94 grantees. In addition to ensuring that we are funding the highest quality science across areas associated with NIGMS’ mission, a major goal is to support a broad and diverse portfolio of research topics and investigators. One step in this effort is to make sure that existing skews in the system are not exacerbated during the MIRA selection process. To assess this, we compared the gender, race/ethnicity and age of those MIRA applicants who received an award with those of the applicants who did not receive an award, as well as with New and Early Stage Investigators who received competitive R01 awards in Fiscal Year (FY) 2015.

We did not observe any significant differences in the gender or race/ethnicity distributions of the MIRA grantees as compared to the MIRA applicants who did not receive an award. Both groups were roughly 25% female and included ≤10% of underrepresented racial/ethnic groups. These proportions were also not significantly different from those of the new and early stage R01 grantees. Thus although the MIRA selection process did not yet enhance these aspects of the diversity of the awardee pool relative to the other groups of grantees, it also did not exacerbate the existing skewed distribution.

We did observe significant differences among the mean ages of the MIRA grantees, MIRA applicants who did not receive an award and the R01-funded grantees. The MIRA grantees are 1.5 years younger on average than those MIRA applicants who did not receive an award (37.2 vs. 38.7 years, p<0.05), and about 2 years younger than the FY 2015 R01-funded Early Stage Investigators (37.2 vs. 39.1 years, p<0.001). The R01-funded New Investigators in FY 2015, a pool which includes a few individuals older than 60 years, average an age of 45.6 years. This selection for funding investigators earlier is a promising feature of the first round of MIRA awards to New and Early Stage Investigators. As noted at the recent meeting of our Advisory Council, where Jon presented these data, 37 years is still relatively late for investigators to be getting their first major NIH grant. We will continue to monitor this issue with the goal of further decreasing that figure.

Revisiting the Dependence of Scientific Productivity and Impact on Funding Level

Travis Dorsey
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A 2010 analysis by NIGMS and subsequent studies by others (Fortin and Currie, 2013; Gallo et al., 2014; Lauer et al., 2015; Doyle et al., 2015; Cook et al., 2015 Exit icon) have indicated that, on average, larger budgets and labs do not correspond to greater returns on our investment in fundamental science. We have discussed the topic here in A Shared Responsibility and in an iBiology talk Exit icon. In this updated analysis, we assessed measures of the recent productivity and scientific impact of NIGMS grantees as a function of their total NIH funding.

We identified the pool of principal investigators (PIs) who held at least one NIGMS P01 or R01-equivalent grant (R01, R23, R29, R37) in Fiscal Year 2010. We then determined each investigator’s total NIH funding from research project grants (RPGs) or center grants (P20, P30, P50, P60, PL1, U54) for Fiscal Years 2009 to 2011 and averaged it over this 3-year period. Because many center grants are not organized into discrete projects and cores, we associated the contact PI with the entire budget and all publications attributed to the grant. We applied the same methodology to P01s. Thus, all publications citing the support of the center or P01 grant were also attributed to the contact PI, preventing underrepresentation of their productivity relative to their funding levels. Figure 1 shows the distribution of PIs by funding level, with the number of PIs at each funding level shown above each bar.

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