Associations of topic-specific peer review outcomes and institute and center award rates with funding disparities at the National Institutes of Health

Michael S Lauer; Jamie Doyle; Joy Wang; Deepshikha Roychowdhury

doi:10.7554/eLife.67173

. 2021 Apr 13;10:e67173. doi: 10.7554/eLife.67173

Associations of topic-specific peer review outcomes and institute and center award rates with funding disparities at the National Institutes of Health

Michael S Lauer ^1,^✉, Jamie Doyle ², Joy Wang ³, Deepshikha Roychowdhury ³

Editors: Cliff J Rosen⁴, Mone Zaidi⁵

PMCID: PMC8087446 PMID: 33847562

Abstract

A previous report found an association of topic choice with race-based funding disparities among R01 applications submitted to the National Institutes of Health (‘NIH’) between 2011 and 2015. Applications submitted by African American or Black (‘AAB’) Principal Investigators (‘PIs’) skewed toward a small number of topics that were less likely to be funded (or ‘awarded’). It was suggested that lower award rates may be related to topic-related biases of peer reviewers. However, the report did not account for differential funding ecologies among NIH Institutes and Centers (‘ICs’). In a re-analysis, we find that 10% of 148 topics account for 50% of applications submitted by AAB PIs. These applications on ‘AAB Preferred’ topics were funded at lower rates, but peer review outcomes were similar. The lower rate of funding for these topics was primarily due to their assignment to ICs with lower award rates, not to peer-reviewer preferences.

Research organism: None

Introduction

Data recently reported by Hoppe et al., 2019 from the National Institutes of Health ('NIH') suggest that part of the well-documented funding disparity (Ginther et al., 2011) affecting African-American Black ('AAB') principal investigators ('PIs') may be related to the topic of their applications. The authors of that report (including the first author of this report) found that topic choice accounted for over 20% of the disparity and wondered whether biases on the part of peer reviewers might explain why some application topics fare less well when submitted to the NIH for consideration of funding. In other words, peer reviewers might prefer certain topics over others, and specifically may be biased against applications focused on topics preferred by AAB PI’s.

However, peer review outcomes are not the only determinant of funding. Across the agency, grants are not simply awarded in order of peer review scores (Taffe and Gilpin, 2021). One reason is that applications submitted to the NIH are not just submitted to NIH; they are assigned to one of 24 grant-issuing institutes or centers ('ICs') that in turn make decisions about which applications to fund. The proportion of applications funded (or 'award rate') varies across ICs; therefore, we can think of the NIH process as not one competition hinging entirely on peer review but rather as 24 separate competitions. The variability of award rates relates to differences in number of applications each IC receives, available funds, and IC priorities.

Hoppe et al., 2019 did not account for IC assignment or variation in IC-specific award rates. It is possible that the apparent link between topic choice and funding disparities may reflect differences in IC assignment, since ICs receive applications according to alignment with their stated mission. For example, applications focusing on cancer epidemiology are more likely to be assigned to the National Cancer Institute while those focusing on basic human biology are more likely to be assigned to the National Institute of General Medical Sciences. If award rates at the National Institutes of General Medical Sciences are higher than at the National Cancer Institute, it might appear that NIH 'prefers' basic human biology over cancer epidemiology. While the former topic does fare better with a higher likelihood of funding, this may be largely because of different IC award rates as opposed to differences in how the topics are received by peer reviewers.

We therefore re-analyzed the data from Hoppe et al., 2019 focusing on topic-specific peer review outcomes according to AAB PI preferences and on the possible role of IC assignment in application outcomes. To minimize well-documented biases related to repeated looks by the peer review system on individual proposals (from resubmissions [Lauer, 2017] or competing renewals [Lauer, 2016]) we focus on de novo applications submitted to the NIH for the first time.

Materials and methods

These analyses are based on R01 applications submitted to NIH between 2011 and 2015. Hoppe et al., 2019 described in detail NIH peer review processes and the 'Word2vec' algorithm Mikolov et al., 2013 used to designate a topic for each application. Briefly, each application is assigned to a peer review group; approximately 2% of applications are withdrawn prior to review for administrative reasons. After a preliminary pre-meeting review, approximately half are deemed to be potentially meritorious and are therefore discussed during a formally convened meeting. After the meeting, each discussed application receives a priority score ranging from 10 (best) to 90 (worst); many, but not all, applications also receive a 'percentile ranking' to account for differences in how individual peer review groups calibrate their scores.

Applications are not only assigned to peer review groups; they are also assigned to ICs. The Center of Scientific Review (CSR) Division of Receipt and Referral makes assignments (CSR, 2021) based on IC interests, specific Funding Opportunity Announcements (as appropriate), and sometimes on applicant request. Some applications receive a dual assignment (that is, two ICs with one primary and one secondary), but changes in primary IC assignment occur less than 2% of the time. ICs ultimately make decisions about which applications to fund, with funding decisions based on peer review scores, strategic priorities, and availability of funds (NIH, 2021c).

Data and descriptive measures

To eliminate biases due to prior reviews and to maximize the likelihood that the analysis data frame reflects comparable observations with independently distributed variables, we focused on applications coming to the NIH for the first time; in other words, we excluded resubmissions (Lauer, 2017) and competing renewals (Lauer, 2016) which are known to systematically fare better on peer review. Each application was designated to one of 148 topics identified by the Word2vec algorithm (Mikolov et al., 2013). Hoppe et al., 2019 stated there were 150 topics, but subsequently discovered data processing errors for two topics yielded 148 for the re-analysis.

For each topic, we counted the number of applications of submitted by AAB PIs. We assessed AAB preference for each topic t by calculating the proportion of all AAB applications within that topic, that is

P_{A A B} = (\sum_{i = 1}^{n} a a b) / (\sum_{i = 1}^{N} a a b)

(1)

where i refers to individual applications, n is the number of applications in topic t, aab is an indicator of whether application i had an African American or Black contact PI (1 if yes, 0 if no), and N is total number of applications (across all topics). For each IC, we calculated award rates A as number of applications funded divided by number of applications assigned, that is

A_{I C} = \frac{\sum_{i = 1}^{m} a}{m}

(2)

where m is the number of applications received by the IC and a is an indicator of whether an application i was awarded (1 if yes, 0 if no). Similarly, for each topic we calculated awards rates as

A_{t} = \frac{\sum_{i = 1}^{n} a}{n}

(3)

where again a is an indicator of whether an application i was awarded (1 if yes, 0 if no) and n is the number of applications in the topic. We estimated a predicted award rate for each topic by designating the probability for funding for each application i as its IC award rate (in other words, if an application i was assigned to NEI, the probability of funding was 0.15). Thus, the predicted topic award rate was

{\hat{A}}_{t} = \frac{\sum_{i = 1}^{n} \hat{a}}{n}

(4)

where $\hat{a}$ is the award rate for the IC to which each application i is assigned and n is the number of applications in the topic.

For each topic, we calculated two peer review outcomes. The proportion of applications making it to discussion (or 'discussed') was

D_{t} = \frac{\sum_{i = 1}^{n} d}{n}

(5)

where d is an indicator of whether an application i was discussed (1 if yes, 0 if no) and n is the number of applications in the topic. For those applications that were discussed and therefore received a priority score, we calculated a topic-specific mean score as

{\bar{s}}_{t} = \frac{\sum_{i = 1}^{l} s}{l}

(6)

where l is the number of applications in that topic that made it to discussion and s is the priority score of each application i.

Regression analyses

Figure 1 shows the association of cumulative percentage of AAB applications by the cumulative percentage of topics. Consistent with Hoppe et al., 2019, there was a nonrandom distribution whereby 10% of topics (or 15 topics) accounted for 50% of AAB applications, while 25% of the topics accounted for 70% of AAB applications. We designted a topic as 'AAB Preferred' if it was among the 15 topics that accounted for 50% of AAB applications.

To further assess the association of topic choice with peer review outcomes, we focused on applications that were discussed and therefore received a priority score. We constructed a plot of topic-specific mean peer review scores according to number of applications in each topic. We expected to find a greater variance of mean scores for topics receiving fewer applications ('regression to the mean'). We generated a linear regression model to estimate a predicted mean score for each topic based on topic size (namely the number of submitted applications that were discussed for each topic), and calculated a residual for each topic by subtracting from each topic-specific mean score the model-based predicted mean score. We compared the distribution of residuals for AAB preferred topics and other topics.

We next performed a series of probit regression analyses of application funding as

Y_{i} = β_{0} + β_{1} P I_{A A B} + β_{2} T_{A A B P} + β_{3} A_{I C} + β X + ϵ

(7)

where $P I_{A A B}$ is an indicator variable for whether application i had an African American or Black PI (yes or no), $T_{A A B P}$ is an indicator variable for whether application i focused on an AAB Preferred topic (yes or no), $A_{I C}$ is the IC award rate (Equation 2), and $𝐗$ corresponds to variables including 'Early Stage Investigator' status, whether application i designated more than one PI, and whether the proposed research in application i involved animal and/or human subjects. Early Stage Investigators were those PIs within 10 years of their terminal research degree or completion of clinical training. Akaike Information Criteria, Bayesian Information Criteria, and Log Likelihood values informed model strength.

All analyses used R (R Foundation, 2021) packages, including tidyverse (Wickham et al., 2019), ggplot2 (Wickham, 2016), finalfit (Harrison, 2020), and texreg (Leifeld, 2013).

Results

Of 157,405 applications received, there were, after exclusion of pre-review withdrawals, resubmissions and competing renewals, 96,697 applications considered and reviewed by NIH for the first time. Of these 8381 were funded, for an overall award rate of 9%. The vast majority of applications (88%) were reviewed in the NIH Center for Scientific Review (or CSR). There were 1643 applications, or 2%, submitted by AAB PIs. Table 1 shows IC-specific values for applications received, applications funded, award rates (Equation 2), and percent applications coming from AAB PIs. Of note, award rates varied from 6% to 15%, while the proportion of applications with AAB PIs ranged from <1% to nearly 15%. We also show 2015 appropriations for each IC (NIH, 2021b).

Table 1. Application characteristics according to Institute or Center (IC), as ranked by award rate (Equation 2, highest to lowest).

AAB = African American or Black; PI = Principal Investigator; EY = National Eye Institute; DC = National Institute of Deafness and Communications Disorders; GM = National Institute of General Medical Sciences; DE = National Institute of Dental and Craniofacial Research; MH = National Institute of Mental Health; DA = National Institute on Drug Abuse; NS = National Institute of Neurological Disorders and Stroke; NR = National Institute of Nursing Research; HL = National Heart, Lung, and Blood Institute; AI = National Institute of Allergy and Infectious Diseases; ES = National Institute of Environmental Health Sciences; DK = National Institute of Diabetes and Digestive and Kidney Disease; AA = National Institute on Alcohol Abuse and Alcoholism; AG = National Institute on Aging; EB = National Institute of Biomedical Imaging and Bioengineering; CA = National Cancer Institute; HD = Eunice Kennedy Shriver National Institute of Child Health and Human Development; MD = National Institute on Minority Health and Health Disparities; AR = National Institute of Arthritis and Musculoskeletal and Skin Diseases. Data for ICs with cell sizes not exceeding 11 are not shown due to privacy concerns. Final columns shows 2015 appropriations in billions of dollars.

IC	Applications(N)	Awards(N)	Award rate(%)	AAB PI	AAB(%)	Appropriations($B)
EY	2026	315	15.5	14	0.69	0.684
GM	9781	1266	12.9	113	1.16	2.371
DC	1145	141	12.3	13	1.14	0.405
DE	1282	140	10.9	26	2.03	0.400
MH	5067	536	10.6	87	1.72	1.463
DA	3606	359	10.0	66	1.83	1.029
NS	6786	668	9.8	77	1.13	1.605
NR	1180	107	9.1	55	4.66	0.141
HL	11,325	995	8.8	171	1.51	2.998
ES	2193	188	8.6	44	2.01	0.745
AI	9421	791	8.4	196	2.08	4.359
DK	6986	538	7.7	110	1.57	1.900
AA	1423	105	7.4	16	1.12	0.447
EB	1777	131	7.4	15	0.84	0.330
AG	4211	302	7.2	52	1.23	1.199
CA	15,906	1045	6.6	206	1.30	4.950
HD	5454	340	6.2	168	3.08	1.287
MD	829	51	6.2	123	14.84	0.269
AR	2656	161	6.1	30	1.13	0.522

Open in a new tab

Table 2 shows review and funding outcomes for applications according to whether the assignment was to an IC in the top quartile of proportion of applications with AAB PIs ('Higher AAB'). These ICs were the National Institute of Allergy and Infectious Diseases, the National Institute of Dental and Craniofacial Research, the National Institute of Child Health and Human Development, the National Institute of Minority Health and Disparities, the National Institute of Nursing Research, and the Fogarty International Center. Applications assigned to Higher AAB ICs were three times more likely to come from AAB PIs. Review outcomes – proportion discussed and, for those applications that were discussed at peer review meetings, priority scores and percentile rankings – were similar in both groups. Despite the similar review outcomes, these applications were 10% less likely to be funded. These descriptive data show that two IC-defined cohorts of applications with similar review outcomes can have substantively different funding outcomes.

Table 2. Application review and funding outcomes according to whether Institute or Center received a higher or lower proportion of applications from AAB principal investigators.

AAB = African American or Black; PI = Principal Investigator.

Characteristic or outcome		Higher AAB	Lower AAB
Total N (%)		18262 (18.9)	78435 (81.1)
PI AAB	Yes	579 (3.2)	1064 (1.4)
Discussed	Yes	8809 (48.2)	35845 (45.7)
Priority Score	Median (IQR)	39.0 (30.0 to 48.0)	40.0 (30.0 to 48.0)
Percentile Ranking	Median (IQR)	34.0 (21.0 to 45.0)	33.0 (21.0 to 44.0)
Awarded	Yes	1440 (7.9)	6941 (8.8)

Open in a new tab

Review outcomes according to topic

Figure 2 shows the topic-based peer review outcomes according to the proportion of total AAB applications linked to each topic. There was a non-significant trend whereby applications focusing on topics that accounted for a greater proportion of AAB applications (Equation 1) were more likely to make it to discussion (Equation 5)(Panel A, p=0.12). When we focused on applications that were discussed and therefore received a priority score, there was no association of mean score for each topic (Equation 6) with proportion of total AAB applications linked to each topic (Equation 1) (Panel B, p=0.87).

To gain greater insights into possible peer review biases against topics preferred by AAB PIs, Figure 3, Panel A, shows the mean priority score by topic (Equation 6; again, only discussed applications receive priority scores) according to the topic size, namely the number of submitted applications that were discussed for each topic. As would be expected topics of smaller size showed greater variability, a manifestation of regression to the mean.

The line in Figure 3, Panel A, is based on a linear regression of mean score according to topic size. Although the slope was slightly negative (coeffecient −0.0002634), the association was not significant (p=0.63). Among AAB preferred topics (orange dots), there were 5 more than 1 point above the line (meaning with scores worse than predicted), while there were 3 more than 1 point below the line (meaning with scores better than predicted). The remaining seven topics had mean scores that were within 1 point of the predicted value.

For each topic, we calculated a residual by subtracting from the topic-specific mean priority score (Equation 6) the predicted mean priority score; we weighted the residuals by the topic size, as the larger topics contribute more information. Figure 3, Panel B, shows the distribution of the weighted residuals according to topic type. Residuals were more positive (i.e. worse) for AAB preferred topics. However, the absolute differences were small, much less than one priority score point (over a possible range of 10–90, with topic-specific mean values ranging from 35 to 45).

Funding outcomes according to topic: descriptive findings

Table 3 shows peer review and funding outcomes according to whether applications were focused on the 15 (or 10% of) topics that made up 50% of all applications with AAB PIs ('AAB Preferred' topics). Consistent with the absence of association with proportion of AAB applications linked to each topic (Figure 2), peer review outcomes were similar in the two groups. However, applications focusing on AAB Preferred topics were 7% less likely to be funded. Table 4 shows similar findings when focusing on the 37 (or 25% of) topics that made up 70% of all applications with AAB PIs.

Table 3. Application review and funding outcomes according to whether topic was among those that accounted for half of all AAB applications.

Abbreviations as in Table 2.

Characteristic or outcome		AAB preferred	Other
Total N (%)		24416 (25.3)	72281 (74.7)
PI AAB	Yes	824 (3.4)	819 (1.1)
Discussed	Yes	11515 (47.2)	33139 (45.8)
Priority Score	Median (IQR)	40.0 (31.0 to 48.0)	39.0 (30.0 to 48.0)
Percentile Ranking	Median (IQR)	34.0 (22.0 to 45.0)	33.0 (20.0 to 44.0)
Assigned IC AAB Proportion	Higher AAB	5925 (24.3)	12337 (17.1)
Awarded	Yes	1992 (8.2)	6389 (8.8)

Open in a new tab

Table 4. Application review and funding outcomes according to whether topic was among those that accounted for 70% of all AAB applications.

Abbreviations as in Table 2.

Characteristic or outcome		AAB preferred	Other
Total N (%)		46108 (47.7)	50589 (52.3)
PI AAB	Yes	1152 (2.5)	491 (1.0)
Discussed	Yes	21485 (46.6)	23169 (45.8)
Priority Score	Median (IQR)	40.0 (31.0 to 48.0)	39.0 (30.0 to 48.0)
Percentile Ranking	Median (IQR)	34.0 (21.0 to 45.0)	33.0 (20.0 to 44.0)
Assigned IC AAB Proportion	Higher AAB	11424 (24.8)	6838 (13.5)
Awarded	Yes	3852 (8.4)	4529 (9.0)

Open in a new tab

Why do applications on AAB Preferred topics have worse funding outcomes despite similar peer review assessments? Table 3 shows that applications on AAB Preferred topics (those 10% of topics that accounted for 50% of all AAB applications) were 42% more likely to be assigned to Higher AAB ICs. The scatter plot in Figure 4 shows IC award rate according to the proportion of applications assigned to it that focus on AAB Preferred topics. ICs with receiving a higher percentage of AAB Preferred topic applications tended to have lower award rates (r = −0.35, p=0.08).

Figure 4. — In other words, the X-axis value would be 33% if one-third of all IC applications focused on AAB Preferred topics, namely those 15 topics that together accounted for 50% of all applications with AAB PIs. The orange-colored ICs are those in which the proportion of applications from AAB PIs were in the top quartile. AAB = African American or Black; IC = Institute or Center; EY = National Eye Institute; DC = National Institute of Deafness and Communications Disorders; GM = National Institute of General Medical Sciences; DE = National Institute of Dental and Craniofacial Research; MH = National Institute of Mental Health; DA = National Institute on Drug Abuse; NS = National Institute of Neurological Disorders and Stroke; NR = National Institute of Nursing Research; HL = National Heart, Lung, and Blood Institute; AI = National Institute of Allergy and Infectious Diseases; ES = National Institute of Environmental Health Sciences; DK = National Institute of Diabetes and Digestive and Kidney Disease; AA = National Institute on Alcohol Abuse and Alcoholism; AG = National Institute on Aging; EB = National Institute of Biomedical Imaging and Bioengineering; CA = National Cancer Institute; HD = Eunice Kennedy Shriver National Institute of Child Health and Human Development; MD = National Institute on Minority Health and Health Disparities; AR = National Institute of Arthritis and Musculoskeletal and Skin Diseases. ICs that receive relatively more applications on AAB Preferred topics tended to have lower award rates (r = −0.35, p=0.08). Data for ICs with cell sizes not exceeding 11 are not shown due to privacy concerns.

The association between IC award rates and application success is demonstrated in Figure 5, Panel A, which shows that observed topic-specific award rates (Equation 3) were strongly correlated with predicted award rates (Equation 4), that is award rates that would be expected based solely on IC-specific award rates (r = 0.62, p<0.0001). Figure 5, Panel B shows a tendency toward lower predicted topic-specific award rates (Equation 4) with increasing proportion of AAB applications (Equation 1) linked to each topic (p=0.06). Panel C shows that the actual topic-specific award rates (Equation 3) were non-significantly negatively associated with increasing proportion of AAB applications (Equation 1) (p=0.76).

Funding outcomes according to topic: probit regression models

Table 5 shows the association of an application with an AAB PI with the probability of funding. Consistent with Hoppe et al., 2019 and prior literature (Ginther et al., 2011), AAB PI applications had a lower likelihood of funding (Model 1, negative regression coefficient for AAB Principal Investigator, p<0.001). Adjusting for the topic (AAB Preferred or Other) reduced the absolute value of the regression coefficient for race by 5% (Model 2); similarly adjusting for IC assignment (Higher or Lower AAB) reduced the absolute value of the regression coefficient by 5% (Model 3). However, adjusting for the award rate of the assigned IC reduced the absolute value of the regression coefficient for race by 14% (Model 4).

Table 5. Probit Regression Models (regression coefficients and standard errors) with focus on the PI.

Model 1 shows the univariable association of funding success according to whether the PI is AAB. The negative non-zero cofficient indicates that applications with AAB PIs are less likely to be funded; the p-value refers to the likelihood that the difference from zero would be due to chance were the null hypothesis of no association to be true. Model 2 adjusts for topic, Model 3 adjusts for IC assignment, and Model 4 adjusts for IC award rate. Note that the absolute value for the regression coefficient linking AAB PI to funding outcome decreases with each of these adjustments, with the greatest reduction after adjusting for IC award rate. AIC = Akaike Information Criterion; BIC = Bayesian Information Criterion; Num. obs. = Number of Observations. Other abbreviations same as in Tables 1 and 2.

	Model 1	Model 2	Model 3	Model 4
Intercept	−1.359***	−1.349***	−1.348***	−1.903***
	(0.006)	(0.007)	(0.006)	(0.020)
AAB Principal Investigator	−0.184***	−0.174***	−0.174***	−0.158**
	(0.049)	(0.049)	(0.049)	(0.050)
AAB Preferred Topic		−0.040**
		(0.013)
IC Higher AAB Applications			−0.060***
			(0.015)
IC Award Rate				0.061***
				(0.002)
AIC	56996.279	56989.156	56982.092	56175.409
BIC	57015.238	57017.594	57010.530	56203.847
Log Likelihood	−28496.140	−28491.578	−28488.046	−28084.705
Deviance	56992.279	56983.156	56976.092	56169.409
Num. obs.	96697	96697	96697	96697

Open in a new tab

${}^{*, * *}p < 0.001$ ; ${}^{* *}p < 0.01$ ; ${}^{*}p < 0.05$ .

Table 6 focuses on topic and funding outcomes. Without consideration of other variables, an AAB preferred topic was associated with a lower probability of funding (Model 1, negative regression coefficient for AAB preferred topic, p<0.01). However, after adjusting for IC award rate alone (Model 2, regression coefficient for AAB preferred topic close to zero, p=NS) as well as IC award rate and other characteristics (whether the PI is an early stage investigator, whether the application included more than one PI, and whether the proposed research included human and/or animal subjects), there was no association between AAB preferred topics and funding (Model 3, regression coefficient for AAB preferred topic close to zero, p=NS). The IC award rate was strongly associated with likelihood of funding (Models 2 and 3, regression coefficient positive, p<0.001).

Table 6. Probit Regression Models (regression coefficients and standard errors) with focus on topic type.

Model 1 shows the univariable association of funding success according to whether the topic is AAB preferred. Model 2 shows results according to topic choice and IC award rate. Model 3 includes early stage investigator status, whether applications had multiple PIs, and whether the application included research on human subjects and/or animal subjects. AIC = Akaike Information Criterion; BIC = Bayesian Information Criterion; Num. obs. = Number of Observations. Other abbreviations same as in Tables 1 and 2.

	Model 1	Model 2	Model 3
Intercept	−1.351***	−1.904***	−1.869***
	(0.007)	(0.021)	(0.027)
AAB Preferred Topic	−0.044**	−0.005	−0.013
	(0.013)	(0.014)	(0.015)
IC Award Rate		0.061***	0.057***
		(0.002)	(0.002)
AAB Principal Investigator			−0.170***
			(0.050)
Early Stage Investigator			0.149***
			(0.015)
Multi-PI Application			0.099***
			(0.015)
Human Subjects			−0.056
			(0.014)
Animal Subjects			−0.052***
			(0.014)
AIC	57000.243	56185.926	56047.521
BIC	57019.201	56214.364	56123.355
Log Likelihood	−28498.121	−28089.963	−28015.760
Deviance	56996.243	56179.926	56031.521
Num. obs.	96697	96697	96697

Open in a new tab

${}^{*, * *}p < 0.001$ ; ${}^{* *}p < 0.01$ ; ${}^{*}p < 0.05$ .

Resubmission and single-PI applications

We repeated the main analyses, but this time focusing on resubmission applications, which as a general rule are assigned to the same IC as the original submission. The findings were similar, except that, as expected, the absolute award rates were higher. We also conducted a separate series of analyses which repeated our primarily analyses but focusing solely on single-PI applications. Again, findings were similar (see Appendix 1 and Appendix 2).

Discussion

Among over 96,000 R01 applications submitted to NIH between 2011 and 2015, 2% were submitted by AAB PIs. Their applications were skewed towards a relatively small group of 'AAB Preferred' topics (Figure 1); 10% of 148 topics accounted for 50% of AAB applications. Applications on AAB Preferred topics had similar review outcomes as those on other topics (Figure 2 and Table 3) but were less likely to be funded. The lower award rates for applications on AAB Preferred applications (Table 5, Model 1) were no longer seen after accounting for IC award rates (Table 6, Models 2 and 3).

These observations reflect that there are two factors at play in determining whether an application submitted to NIH will be funded. The first, well known to all involved with NIH system, is peer review; those applications that receive better scores are more likely to be funded. But there is a second factor, namely the funding ecology of the IC to which the application is assigned. As shown in Table 2 applications with similar peer review outcomes are less likely to be funded if they are assigned to ICs with lower overall award rates. AAB PIs are more likely to submit applications to ICs with lower award rates, and applications (whether submitted by AAB or other PIs) that focus on AAB Preferred topics tended to be assigned to ICs with lower award rates (Figure 4).

Hoppe et al., 2019 found that topic choice accounted for over 20% of funding disparities that adversely effect AAB PIs. We confirm this, but find that IC assignment (which, of course, is linked to topic) explains the disparities just as well, and that IC award rates explain the disparities even better (Table 5, 14% vs 5% reduction in absolute value of regression coefficient for AAB PI). Furthermore, after accounting for IC award rate, we found no association between topic choice and funding outcome (Table 6).

There is variability in how well different topics fare at peer review, but inspection of Figure 3 suggests that much of this variability reflects instability of estimates stemming from smaller sample sizes. Many topics that are not favored by AAB PIs receive better (lower) priority scores than the overall average, but many other such topics receive worse scores (Figure 3, Panel A). An inspection of weighted residuals suggest that AAB Preferred topics may fare a bit worse (Figure 3, Panel B), but to a lower degree than the difference of award rates among assigned ICs (Table 1). Furthermore, it should be noted that applications on these topics were more likely to make it past the first hurdle of peer review, that is reaching the point of formal discussion (Figure 2 and Table 3, see line 'Discussed'); thus, if anything, peer reviewers may be slightly biased in favor of AAB-preferred topics.

Our primary analysis focused on first-time submissions of de novo applications in which the award rates were low (<10%). Nonetheless, our analyses of resubmission applications yielded similar findings (Appendix 1). It is also important to note that this was an analysis of applications, not persons. This is an issue when considering multi-PI applications, since all PI’s, not just the contact PI, plays a role in choosing the topic of their proposal. Nonetheless, an analysis confined to single PI applications yielded similar findings (Appendix 2).

Only 2% of PIs in this study were African American or Black and thus it is not possible that this small percentage of scientists cover the entire breadth of what NIH funds. The small number of AAB PIs is a source of error for describing AAB scientific interests as reflected in grant applications. Nonetheless, in recent years, NIH has increasingly recognized the importance of enhancing research in targeted topics, as exemplified by a recently announced Common Fund program to enhance transformative research in health disparities and health equity (NIH, 2021a) and by the large investment in RADx-UP, a COVID-19 diagnostic testing program in underserved communities (Tromberg et al., 2020).

Conclusion

The lower rate of funding for applications focused on AAB Preferred topics is likely primarily due to their assignment to ICs with lower award rates. These applications have similar peer review outcomes as those focused on other topics (Table 3). When AAB preference for each topic (Equation 1) was considered as a continuous variable, there was similarly no association between AAB preference and peer review outcomes (Figure 2). Topic choice does partially explain race-based funding disparities (Table 5, Model 2, absolute value of regression coefficient reduced by 5%), but IC-specific award rates explain the disparities to an even greater degree (Table 5, Model 4, absolute value of regression coefficient reduced by 14%). After accounting for IC-specific award rates, we find no association between topic choice and funding outcomes (Table 6, Models 2 and 3).

Appendix 1

Resubmission applications

In our primary analyses, we excluded resubmission applications and competing renewals. Since many applications are not funded on the first try, we repeated our analyses focusing on resubmissions. Our findings are similar.

Of 31,900 resubmission applications received and reviewed, 9605 were funded, for an overall award rate of 30%. There were 448 applications, or 1%, submitted by AAB PIs.

Appendix 1—figure 1 shows the association of cumulative percentage of AAB applications by the cumulative percentage of topics. There was a nonrandom distribution whereby 11 percent of topics (or 16 topics) accounted for 50 percent of AAB applications. We designted a topic as 'AAB Preferred' if it was among the 16 topics that accounted for 50% of AAB applications.

Appendix 1—figure 2 shows the topic-based peer review outcomes according to the proportion of total AAB applications linked to each topic. There was a non-significant trend whereby applications focusing on topics that accounted for a greater proportion of AAB applications (Equation 1) were more likely to make it to discussion (Equation 5)(Panel A, p=0.34). When we focused on applications that were discussed and therefore received a priority score, there was no association of mean score for each topic (Equation 6) with proportion of total AAB applications linked to each topic (Equation 1) (Panel B, p=0.14)

Appendix 1—table 1 shows peer review and funding outcomes according to whether applications were focused on the 16 topics that made up 50% of all resubmission applications with AAB PIs ('AAB Preferred' topics). Peer review outcomes were similar in the two groups, but applications focusing on AAB Preferred topics were 3% less likely to be funded.

Appendix 1—table 1. Application review and funding outcomes according to whether topic was among those that accounted for half of all AAB applications.

Abbreviations as in Table 2.

Characteristic or outcome		AAB preferred	Other
Total N (%)		8193 (25.7)	23707 (74.3)
PI AAB	Yes	224 (2.7)	224 (0.9)
Discussed	Yes	6107 (74.5)	17300 (73.0)
Priority Score	Median (IQR)	31.0 (22.0 to 40.0)	31.0 (22.0 to 40.0)
Percentile Ranking	Median (IQR)	21.0 (9.8 to 36.0)	21.0 (10.0 to 35.0)
Awarded	Yes	2419 (29.5)	7186 (30.3)

Open in a new tab

Appendix 1—table 2 shows probit regressions on topic and funding outcomes. Without consideration of other variables, an AAB preferred topic was associated with a non-significantly lower probability of funding (Model 1, negative regression coefficient for AAB preferred topic). However, after adjusting for IC award rate alone (Model 2, regression coefficient for AAB preferred topic close to zero, p=NS) as well as other IC award rate and other characteristics, there was no association between AAB preferred topics and funding (Model 3, regression coefficient for AAB preferred topic close to zero, p=NS). The IC award rate was strongly associated with likelihood of funding (Models 2 and 3, regression coefficient positive, p<0.001)

Appendix 1—table 2. Probit Regression Models (regression coefficients and standard errors) with focus on topic type.

	Model 1	Model 2	Model 3
Intercept	−0.515***	−1.397***	−1.422***
	(0.009)	(0.065)	(0.068)
AAB Preferred Topic	−0.023	−0.001	−0.004
	(0.017)	(0.017)	(0.018)
IC Award Rate		0.029***	0.029***
		(0.002)	(0.002)
AAB Principal Investigator			−0.171**
			(0.065)
Early Stage Investigator			0.221***
			(0.019)
Multi-PI Application			0.035
			(0.020)
Human Subjects			−0.020
			(0.018)
Animal Subjects			−0.006
			(0.018)
AIC	39034.476	38849.763	38720.539
BIC	39051.217	38874.874	38787.502
Log Likelihood	−19515.238	−19421.881	−19352.270
Deviance	39030.476	38843.763	38704.539
Num. obs.	31900	31900	31900

Open in a new tab

${}^{*, * *}p < 0.001$ ; ${}^{* *}p < 0.01$ ; ${}^{*}p < 0.05$ .

Appendix 2

Single PI applications

In our primary analyses, we included all de novo applications, whether authored by one single PI or more than one PI ('multi-PI'). Here we focus on single-PI applications. Our findings are similar.

Of 76,976 single-PI applications received and reviewed, 6556 were funded, for an overall award rate of 9%. There were 1323 applications, or 2%, submitted by AAB PIs.

Appendix 2—figure 1 shows the association of cumulative percentage of AAB applications by the cumulative percentage of topics. There was a nonrandom distribution whereby 11% of topics (or 16 topics) accounted for 50% of AAB applications. We designted a topic as 'AAB Preferred' if it was among the 16 topics that accounted for 50% of AAB applications.

Appendix 2—figure 2 shows the topic-based peer review outcomes according to the proportion of total AAB applications linked to each topic. There was a non-significant trend whereby applications focusing on topics that accounted for a greater proportion of AAB applications (Equation 1) were more likely to make it to discussion (Equation 5)(Panel A, p=0.12). When we focused on applications that were discussed and therefore received a priority score, there was no association of mean score for each topic (Equation 6) with proportion of total AAB applications linked to each topic (Equation 1) (Panel B, p=0.84)

Appendix 2—table 1 shows peer review and funding outcomes according to whether applications were focused on the 16 topics that made up 50% of all resubmission applications with AAB PIs ('AAB Preferred' topics). Peer review outcomes were similar in the two groups, but applications focusing on AAB Preferred topics were 7% less likely to be funded.

Appendix 2—table 1. Application review and funding outcomes according to whether topic was among those that accounted for half of all AAB applications.

Abbreviations as in Table 2.

Characteristic or outcome		AAB preferred	Other
Total N (%)		19134 (24.9)	57842 (75.1)
PI AAB	Yes	660 (3.4)	663 (1.1)
Discussed	Yes	8993 (47.0)	26342 (45.5)
Priority Score	Median (IQR)	40.0 (31.0 to 48.0)	40.0 (30.0 to 48.0)
Percentile Ranking	Median (IQR)	35.0 (22.0 to 45.0)	34.0 (21.0 to 44.0)
Awarded	Yes	1541 (8.1)	5015 (8.7)

Open in a new tab

Appendix 2—table 2 shows probit regressions on topic and funding outcomes. Without consideration of other variables, an AAB preferred topic was associated with a lower probability of funding (Model 1, negative regression coefficient for AAB preferred topic, p<0.01). However, after adjusting for IC award rate alone (Model 2, regression coefficient for AAB preferred topic close to zero, p=NS) as well as other IC award rate and other characteristics, there was no association between AAB preferred topics and funding (Model 3, regression coefficient for AAB preferred topic close to zero, p=NS). The IC award rate was strongly associated with likelihood of funding (Models 2 and 3, regression coefficient positive, p<0.001)

Appendix 2—table 2. Probit Regression Models (regression coefficients and standard errors) with focus on topic type.

	Model 1	Model 2	Model 3
Intercept	−1.361***	−1.903***	−1.856***
	(0.007)	(0.023)	(0.030)
AAB Preferred Topic	−0.040**	0.002	0.008
	(0.015)	(0.015)	(0.017)
IC Award Rate		0.060***	0.056***
		(0.002)	(0.002)
AAB Principal Investigator			−0.206***
			(0.057)
Early Stage Investigator			0.160***
			(0.016)
Human Subjects			−0.070***
			(0.016)
Animal Subjects			−0.038*
			(0.016)
AIC	44830.312	44152.773	44026.919
BIC	44848.814	44180.526	44091.678
Log Likelihood	−22413.156	−22073.386	−22006.460
Deviance	44826.312	44146.773	44012.919
Num. obs.	76976	76976	76976
${}^{, }p < 0.001$ ; ${}^{ }p < 0.01$ ; ${}^{}p < 0.05$

Open in a new tab

Funding Statement

No external funding was received for this work.

Contributor Information

Michael S Lauer, Email: Michael.Lauer@nih.gov.

Cliff J Rosen, Maine Medical Center Research Institute, United States.

Mone Zaidi, Icahn School of Medicine at Mount Sinai, United States.

Additional information

Competing interests

No competing interests declared.

Author contributions

Conceptualization, Formal analysis, Supervision, Methodology, Writing - original draft, Project administration.

Conceptualization, Formal analysis, Methodology, Writing - review and editing.

Formal analysis, Methodology, Writing - review and editing.

Additional files

Source data 1. csr_anon_id.RData.

elife-67173-data1.zip^{(4.1MB, zip)}

Source code 1. Main Manuscript.Rmd.

elife-67173-code1.zip^{(15.6KB, zip)}

Source code 2. Appendix 1.Rmd.

elife-67173-code2.zip^{(4.8KB, zip)}

Source code 3. Appendix 2.Rmd.

elife-67173-code3.zip^{(4.8KB, zip)}

Transparent reporting form

elife-67173-transrepform.docx^{(111.1KB, docx)}

Data availability

The authors have provided the de-identified data frame (in. RData format) along with three R markdown files that will make it possible for interested readers to reproduce the main paper and the two appendices (including all tables, figures, and numbers in the text).

References

CSR The assignment process. [April 25, 2021];2021 https://public.csr.nih.gov/ForApplicants/SubmissionAndAssignment/DRR/assignmentprocess
Ginther DK, Schaffer WT, Schnell J, Masimore B, Liu F, Haak LL, Kington R. Race, ethnicity, and NIH research awards. Science. 2011;333:1015–1019. doi: 10.1126/science.1196783. [DOI] [PMC free article] [PubMed] [Google Scholar]
Harrison E. Tom Drake, and ots Quickly Create Elegant Regression Results Tables and Plots when Modelling. [April 25, 2021];2020 https://finalfit.org/index.html
Hoppe TA, Litovitz A, Willis KA, Meseroll RA, Perkins MJ, Hutchins BI, Davis AF, Lauer MS, Valantine HA, Anderson JM, Santangelo GM. Topic choice contributes to the lower rate of NIH awards to African-American/black scientists. Science Advances. 2019;5:eaaw7238. doi: 10.1126/sciadv.aaw7238. [DOI] [PMC free article] [PubMed] [Google Scholar]
Lauer MS. Are attempts at renewal successful? [April 25, 2021];2016 https://nexus.od.nih.gov/all/2016/02/16/are-attempts-at-renewal-successful/
Lauer MS. Resubmissions revisited: funded resubmission applications and their initial peer review scores. [April 25, 2021];2017 https://nexus.od.nih.gov/all/2017/02/17/resubmissions-revisited-funded-resubmission-applications-and-their-initial-peer-review-scores/
Leifeld P. Texreg: conversion of statistical model output in R to LaTeX and HTML tables. Journal of Statistical Software. 2013;55:1–24. doi: 10.18637/jss.v055.i08. [DOI] [Google Scholar]
Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. arXiv. 2013 https://arxiv.org/abs/1301.3781
NIH ACD meetings. [April 25, 2021];2021a https://www.acd.od.nih.gov/meetings.html
NIH NIH — Office of Budget —Appropriations History by Institute/Center (1938 to Present) [April 25, 2021];2021b https://officeofbudget.od.nih.gov/approp_hist.html
NIH NIH strategic plans and visions | RePORT. [April 25, 2021];2021c https://report.nih.gov/reports/strategic-plans
R Foundation R: The R Project for Statistical Computing. 2021 https://www.r-project.org/
Taffe MA, Gilpin NW. Racial inequity in grant funding from the US National Institutes of Health. eLife. 2021;10:e65697. doi: 10.7554/eLife.65697. [DOI] [PMC free article] [PubMed] [Google Scholar]
Tromberg BJ, Schwetz TA, Pérez-Stable EJ, Hodes RJ, Woychik RP, Bright RA, Fleurence RL, Collins FS. Rapid scaling up of Covid-19 diagnostic testing in the united states — The NIH RADx Initiative. New England Journal of Medicine. 2020;383:1071–1077. doi: 10.1056/NEJMsr2022263. [DOI] [PMC free article] [PubMed] [Google Scholar]
Wickham H. GGplot2: Elegant Graphics for Data Analysis. New York: Springer-Verlag; 2016. [DOI] [Google Scholar]
Wickham H, Averick M, Bryan J, Chang W, McGowan L, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen T, Miller E, Bache S, Müller K, Ooms J, Robinson D, Seidel D, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H. Welcome to the tidyverse. Journal of Open Source Software. 2019;4:1686. doi: 10.21105/joss.01686. [DOI] [Google Scholar]

eLife. doi: 10.7554/eLife.67173.sa1

Decision letter

Editor: Cliff J Rosen¹

Reviewed by: Carlos Isales², Cliff J Rosen³

Our editorial process produces two outputs: i) public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Acceptance summary:

This is an important paper that nicely addresses a topical and timely issue relevant to extramural NIH funding. The knowledge gained from your analyses will undoubtedly add to the vigorous discussion about how to move forward. We believe you have revised the paper according to the comments of the reviewers. It was clear from your edits and explanation that some of the comments were outside of the scope of the paper, and we understand that. In the meantime, the other responses were noted to be thoughtful and thorough.

Decision letter after peer review:

Thank you for submitting your article "National Institutes of Health Institute and Center Award Rates and Funding Disparities" for consideration by eLife. Your article has been reviewed by 4 peer reviewers, including Cliff J Rosen as the Reviewing Editor and Reviewer #2, and the evaluation has been overseen by Mone Zaidi as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Carlos Isales (Reviewer #1).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

In general there was enthusiasm for the topical nature of the subject and the importance of defining inequities in NIH funding for African Americans. However there were issues raised about the analysis, the absence of significant data sets in 50% of the topics, and approach to the analysis. Notwithstanding these potentially resolvable issues, there was consensus that a revised paper will be critical to enhance discussion of this important area and move toward possible solutions.

Essential Revisions

Please refer to individual Recommendations for the Authors, and specifically focus on:

1. Further defending the overall approach to the analysis by providing new data and/or offering explanations to issues raised in the four reviews.

2. Further refine statistical analysis particularly as noted by Reviewer 3.

Reviewer #1 (Recommendations for the authors):

Please respond to the following questions by explaining further and/or providing data.

1. The authors don't discuss the details about how Institute assignments are made. Agreed that this is done by CSR, a number of variables go into this: (a) the π may attempt to direct the application to a specific Institute or Center by including keywords in abstract or title; (b) the π may request a specific Institute or Center.

2. Is part of the issue related to having an Institute named Minority Health, but then have that Institute be relatively underfunded? Would you suggest increased funding for specific Institutes?

3. Not clear if the numbers included are all the R01's received or just the ones received by CSR. Some R01's go directly to Institutes (e.g. RFA's); as do P01's and training grants. If the analysis is for total grants submitted, in sub analysis, were there any differences in AAB funding rates for those applications that went directly to a specific Institute or Center and were of a different topic that the 10% of 148 topics, i.e were there differences in outcomes in π assigned vs CSR assigned Institutes?

4. For resubmitted R01's do 100% go back to the same Institute-Center or what % are reassigned to a different Institute?

5. How was an "Early-Stage investigator Table 5 defined? Did seniority (# of publications, h-index) and funding success correlate with specific Institutes?

6. Do we know if there were differences between number of applications submitted before successful funding for AAB investigators? Or whether AAB investigators were less likely to submit an A1 application after initial triage?

Reviewer #2 (Recommendations for the authors):

Please address the following issues.

1. It is still problematic why topic choice of community engagement or population studies fare worse overall and particularly at an Institute such as AI compared to GM if both have the relatively same proportion of preferred topics, and both have relatively high budgets compared to other institutes. Please provide data if available.

2. Are there other factors imbedded within ICs or their leadership that determine priorities independent of funding scores?

3. Also, are there one or more ICs that drive the correlations between IC funding and preferred topics or PIs? Please provide any data.

4. In addition it should be noted that only 2% of all PIs are AABs so that represents a potential source of error.

5. The data are only up to 2015; it has now been five years and things have changed dramatically at NIH and in society. Do the authors have more recent data? There are now many more multiple π applications including AABs that may not be the contact π yet are likely to choose to be in a preferred topic area.

6. Please discuss potential resolutions to this issue; In other words we now know that the disparity in funding is such that AAB RO1s do worse than white PIs because they are selecting topics that end up at institutes with lower funding rates. Should the institutes devote a set aside for these topic choices to balance the portfolio of the IC and equal the playing field for AABs?

Reviewer #3 (Recommendations for the authors):

1. The limited introductory material, and the Discussion do not appropriately place this important, indeed critical, work in the context it deserves. In general, the expectation for a general science journal such as eLife demands greater attention to scholarship and contextualization for the anticipated broad audience. Indeed, the Cover Letter argues that this work is relevant to the "NIH extramural research community" and that it is correcting a "mistaken belief" related to their Hoppe et al. 2019 paper. Furthermore, the authors reference the Taffe and Gilpin eLife Feature Article which explains at some length why these matters are of such pressing importance. This is indeed a very important study to publish, although perhaps it should be understood, and written, more as an extension of the ongoing inquiry into NIH funding practices and less as a correction of "mistaken belief" related to the prior paper. It is suggested that at the very least the Introduction and Discussion should address the impact of the funding disparity on (1) AA/B researchers and (2) research on the health concerns of Black Americans, in a scholarly way, including reference to recent materials on this topic to guide the interested reader.

2. Introduction Ln26-41: This section, currently the bulk of the Introduction, fails to describe why applications in some topics would be assigned to one IC over another. The "for example" section is a misdirection and distracting. The key question, given the data in Figure 1 and Table 1, is why a given grant would be assigned to NIMHD or NINR instead of the disease-domain parent institute.

2. The Methods indicate that the 15 topics that accounted for 50% of the AA/B applications were designated as AA/B-preferred for analysis. There is no rationale or justification provided for this choice. Why not the topics that accounted for 66% or 33% or 75%? It would appear critical to provide a strong justification, and some indication of what impact other choices for what is an apparently arbitrary cutoff would have on the central analyses.

3. The Conclusion section presages what will be takeaway message for many readers, i.e. "likely primarily due to". Yet, from this analysis, this conclusion which is likely to be assumed to apply to all AA/B application leaves any disparity of funding for the topics associated with half of the applications with AA/B PIs ignored.

4. The Methods indicate that the definition of AA/B-preferred ICs was the top quartile (6/24 ICs), but there is no rationale or justification provided for this choice. In the absence of a strong justification, it would appear critical to provide some indication of what impact other essentially arbitrary cutoff choices would have on the central analyses. For example, of the five (Fogerty International Center appears to be omitted due to "privacy concerns") ICs with the highest percentage of AA/B applications shown in Table 1, the two lowest are 2.13% (AI) and 2.06% (ES) which are much closer to 12 remaining ICs in AA/B percentage than they are to the third lowest of the AA/B upper quartile. Three of these (DE, 1.99% AA/B; DA, 1.81% AA/B; MH, 1.69% AA/B) are more obviously grouped with AI and ES in terms of AA/B award rate than are the three most extreme AA/B-preferred ICs (HD, NR, MD). This differs from the choices made in Hoppe et al. 2019 where it is fairly obvious from Figure 3A why the 8 topic clusters receiving a high percentage of AA/B applications, and the 8 with zero, were selected for analysis. From Table 1 of this manuscript, the top quartile of ICs account for only 27.3% of AA/B applications. Inclusion of DE, DA and MH would increase this to 48.5% of the AA/B applications, more consistent with the 50% threshold used by the authors to designate AA/B-preferred topics. These choices are not explained or justified and it is critical to give some indication of whether the central conclusions depend on such arbitrary choices.

5. Table 1 is missing a statistic which is key to the authors' conclusions. The award rate for applications with AA/B versus white PIs should be listed per IC, instead of only the aggregate award rate. The entire argument hinges on the presumption that there is no difference in the white/AA/B success rate ratio across the ICs, yet we have seen no analysis of this in prior papers nor does the information occur here.

6. Page 3, Line 102: The phrase "submitted [applications] to" in the context of IC assignment is an improper misrepresentation. This requires correcting throughout, including the Abstract and Line 163 of the Discussion. While applicants can request assignment to a given I or C, this is not always respected and thus this phrasing should reflect NIH assignment, not π choice. This aspect of application assignment (and any dual assignments and selection/refusal to pick up as a secondary IC) should be incorporated into the analysis. Are applications with AA/B PIs more or less likely to be assigned by the NIH's receipt and referral process to the lower-success institutes instead of the parent IC? Are applications with AA/B PIs more likely to request those low-success rate ICs? Are assignment requests equivalently respected? Are applications with AA/B PIs more or less likely to be dual-assigned and/or selected for funding by the secondary IC? What about the role of shared funding between ICs?

7. Page 5, Line 123: While the regression coefficients are useful for a sophisticated audience, it is essential to report the results in a more easily understandable manner for a more general audience. Most importantly the results should be expressed in a way that is comparable with the prior analyses provide by Ginther, Hoppe and their colleagues and indeed is mentioned in the first paragraph of the Introduction. The reader should be informed clearly what percentage of the disparity is accounted for here, similar to the ~20-25% of the racial bias effect accounted for by topic in Hoppe et al. 2019 and by CV metrics in Ginther et al. 2018. This will then help to make the Abstract and Conclusion statements more precise.

8. Figure 1 is confusing in juxtaposition with Table 1 and in context with the overall thrust of the paper. It is suggested that an additional panel be added in which the X axis is the AA/B percentage of applications to that IC, as in Table 1. That is what the reader is expecting at first, and it takes some close reading to figure out these are rankings of the ICs based on submissions by topic. In addition, the six AA/B-preferred ICs should be clearly indicated on-figure by means of colored text, bolded text, a circle or something similar.

9. Discussion, page 10: The second paragraph refers to the funding ecology and ICs with lower award rates, reflecting language used throughout the manuscript. There does not appear to be any description of this ecology and the general science reader will not understand the tremendous disparity in IC funding (e.g., NIMHD and NINR, the extreme outliers in % of AA/B π applications, received 0.8% and 0.4% of the FY2000 budget) without this information. It is essential to add columns in Table 1 that show the average amount of money assigned to each IC across the relevant FYs and to also represent this as a percentage to assist the reader. This context then needs to be mentioned in both Introduction and Discussion where points about IC award rates and funding "ecology" are being made. By way of analogy, the readership of eLife expects an extensive mechanistic explanation without key omissions to accompany empirical results and this is similar.

10. Discussion, page 10: The second paragraph is an outline of the illogic captured in this entire investigation. It cleverly draws indirect lines from the overall AA/B disparity of award to the success rate of ICs while seemingly ignoring the fact the analysis reflects only the topics preferred by half of the AA/B applications and reports on only 27.3% of AA/B applications. It is unclear why the primary analysis approach for this inquiry is not simply to examine white vs AA/B success rates across each of the ICs. This would be a much simpler and intuitive answer to what appear to be the authors originating question. The Hoppe report admitted, but tried to undersell, the critically important finding that even in the least-successful quintile of topics, the AA/B applications faired more poorly and it would be critical to understand if that is also true in this analysis of IC assignment.

11. Discussion, page 10: The third and fourth paragraphs are written in imprecise language that does a disservice to the reader understanding of the size of the effects that are being reported. The Hoppe sentence should just include the ~20% number. The comments about "just as well", "even better", "fare a bit worse" and "to a much lower degree" need quantitation.

12. Discussion, page 11, Line 179: The statement is utterly speculative and should be removed or backed with better evidence.

13. Conclusion, page 11: The use of "explain disparities to an even greater degree" needs to be accompanied by an estimate of the effect size, similar to the data in Hoppe underlying the "topic choice does partially explain" beginning of the sentence. In Hoppe, it was something on the order of accounting for only 21% of the white/AAB disparity of grants that were discussed. In this manuscript the authors need to be clearer about what amount of which racial disparity does "an even greater degree" reflect?

14. Conclusion, page 11: The third sentence again inappropriately draws conclusions from the indirect logic available from the design. The authors need to show the IC-specific award rates for white and AA/B applicants within each IC and contrast these by IC-specific award rate if they wish to make this statement.

15. Supplementary Materials: As mentioned above, there is not a rationale provided for excluding the competing continuation applications from the main analysis. This is peculiar given that these were included in Hoppe and the current manuscript is framed as an explanation of those prior results. If the outcome is the same, it would make more sense to make the full sample the subject of the main paper and the more selected sub-sample the supplement.

Reviewer #4 (Recommendations for the authors):

The paper is well written and uses appropriate methods in the described analysis. While this analysis does contribute to understanding the differential outcomes that have been previously described in the funding of AAB investigators, there are still unanswered questions about the success rate differentials. The topics examined only account for 50% of the applications. If there is little or no biases as is suggested by the authors, the other 50% should be able to be accounted for using the analysis performed in this paper. The authors should consider adding a bit of conditional conclusion of these findings.

eLife. 2021 Apr 13;10:e67173. doi: 10.7554/eLife.67173.sa2

Author response

Essential Revisions

Before going into the Editors’ and Reviewers’ recommendations in detail, I want to summarize a discussion we had regarding some of Reviewer 3’s comments:

– Reviewer 3 states: “The second paragraph is an outline of the illogic captured in this entire investigation. It cleverly draws indirect lines from the overall AA/B disparity of award to the success rate of ICs while seemingly ignoring the fact the analysis reflects only the topics preferred by half of the AA/B applications and reports on only 27.3% of AA/B applications. It is unclear why the primary analysis approach for this inquiry is not simply to examine white vs AA/B success rates across each of the ICs.”

– We did not focus on white vs AA/B because that is not the question we were attempting to answer.

– In the original Hoppe paper there is a sentence in the discussion which states, “Our analysis shows that all three of the factors that underlie the funding gap—preference for some topics over others, assignment of poorer scores, and decision to discuss an application—revolve around decisions made by reviewers.”

– The point of this analysis is that preference for some topics over others do not revolve around decisions made by reviewers. Instead, the funding preferences are due to IC award rates.

– We agreed that we needed to clarify the purpose of our analyses, and we feel have done that in the revised paper.

– You agreed that an analysis focusing on how peer review and IC’s approach topics (not PI’s) would be of interest to eLife. Therefore we decided to revise the paper and submit this revision to you.

Please refer to individual Recommendations for the Authors, and specifically focus on:

1. Further defending the overall approach to the analysis by providing new data and/or offering explanations to issues raised in the four reviews.

We have made a number of changes, including more detailed explanations and additional analyses.

2. Further refine statistical analysis particularly as noted by Reviewer 3.

Notwithstanding our discussion above regarding Reviewer 3’s comments, we have refined and extended our analyses in response to a number of comments with which we completely agree:

– New Figure 1 shows in quantitative terms the skewed nature of AAB π preferences. The Figure will help the reader understand why the 50% cut-off was reasonable.

– Reviewer 3 takes issue with our categorical approach (e.g. identifying 10% of topics that account for 50% of AAB π applications). Therefore, we calculated AAB preference for each topic as a continuous variable (equation 1) and show peer review and funding outcomes according to AAB preference in new Figures 2 and 5. These Figures reinforce the absence of an association of peer review outcomes with AAB preference for certain topics.

– We also present an extended descriptive analysis in new Table 4 showing that using a 70% cut-point, instead of 50%, yields similar findings.

– We took steps to better explain our terms and regression analyses. In the Methods section we present a series of equations to explain our terms, and we refer to those equations in other parts of the manuscript. In the legend to Table 5 we explain what the regression coefficients and p-values mean. In the text in the Results and Discussion sections we attempt to “walk the reader through” each of the models presented in the regression table.

Reviewer #1 (Recommendations for the authors):

Please respond to the following questions by explaining further and/or providing data.

1. The authors don't discuss the details about how Institute assignments are made. Agreed that this is done by CSR, a number of variables go into this: (a) the π may attempt to direct the application to a specific Institute or Center by including keywords in abstract or title; (b) the π may request a specific Institute or Center.

We provide information, as well as a citation, on Institute assignments.

2. Is part of the issue related to having an Institute named Minority Health, but then have that Institute be relatively underfunded? Would you suggest increased funding for specific Institutes?

3. Not clear if the numbers included are all the R01's received or just the ones received by CSR. Some R01's go directly to Institutes (e.g. RFA's); as do P01's and training grants. If the analysis is for total grants submitted, in subanalysis, were there any differences in AAB funding rates for those applications that went directly to a specific Institute or Center and were of a different topic that the 10% of 148 topics, i.e were there differences in outcomes in π assigned vs CSR assigned Institutes?

The vast majority (88%) of the applications were reviewed by CSR. Given that only 12% of applications were reviewed outside CSR, we did not conduct stratified analyses.

4. For resubmitted R01's do 100% go back to the same Institute-Center or what % are reassigned to a different Institute?

As a rule, resubmitted applications go back to the same IC.

5. How was an "Early-Stage investigator Table 5 defined? Did seniority (# of publications, h-index) and funding success correlate with specific Institutes?

We provide a definition for Early-Stage Investigator. The second question is about PI’s, not topics, and therefore beyond the scope of this paper.

6. Do we know if there were differences between number of applications submitted before successful funding for AAB investigators? Or whether AAB investigators were less likely to submit an A1 application after initial triage?

This question is about PI’s, not topics, and therefore beyond the scope of this paper. However, it was addressed in the original Hoppe paper.

Reviewer #2 (Recommendations for the authors):

Please address the following issues.

1. It is still problematic why topic choice of community engagement or population studies fare worse overall and particularly at an Institute such as AI compared to GM if both have the relatively same proportion of preferred topics, and both have relatively high budgets compared to other institutes. Please provide data if available.

Different IC’s do not have the same proportion of preferred topics. Tables 3 and 4 (which is a new table) shows the data. For example, in new Table 4, using a 70% cut-off, AAB preferred topics were disproportionately assigned to the 6 IC’s that received the highest proportion of applications with AAB PIs (25% vs. 14%).

2. Are there other factors imbedded within ICs or their leadership that determine priorities independent of funding scores?

We explain briefly how ICs make funding decisions and cite IC-specific strategic plans.

3. Also, are there one or more ICs that drive the correlations between IC funding and preferred topics or PIs? Please provide any data.

These data are shown in Figure 4, but we can see how this was not clear. We relabeled the X axis as “Proportion of Applications on AAB Preferred Topic (%).” In the Figure legend we added, “In other words, the X-axis value would be 33 percent if one-third of all IC applications focused on AAB Preferred topics, namely those 15 topics that together accounted for 50% of all applications with AAB PIs.” As suggested by Reviewer 3, we highlight the ICs that received the highest proportion of applications with AAB PIs; Table 2 shows that the award rates for these ICs were lower than for all other ICs.

4. In addition it should be noted that only 2% of all PIs are AABs so that represents a potential source of error.

We agree and added text in the Discussion.

5. The data are only up to 2015; it has now been five years and things have changed dramatically at NIH and in society. Do the authors have more recent data? There are now many more multiple π applications including AABs that may not be the contact π yet are likely to choose to be in a preferred topic area.

Since this is a re-analysis of the Hoppe project, we focused on those data. We agree that an analysis of more recent data would be of value, particularly to look for secular trends, but this is beyond the scope of this paper. We agree with the concerns about multi-PI applications; therefore we include a supplemental analysis on single-PI applications.

6. Please discuss potential resolutions to this issue; In other words we now know that the disparity in funding is such that AAB RO1s do worse than white PIs because they are selecting topics that end up at institutes with lower funding rates. Should the institutes devote a set aside for these topic choices to balance the portfolio of the IC and equal the playing field for AABs?

This is an important question, but it is related to policy and legislative priorities. Therefore, we feel this is not the right venue for NIH staff to publish such a discussion. However, we did add text in the Discussion on how NIH is prioritizing funding for specific topics. Furthermore, we hope that the paper communicates the message that the problem is not purported peer review biases against certain topics (Figures 2 and 3, Table 6 models 2 and 3).

This paper is not stating that the entire disparity in NIH R01 funding between AAB and whites is due to the topics they choose. IC funding ecology is one of many factors that contribute to the funding disparity. This has been clarified in the Discussion section.

Reviewer #3 (Recommendations for the authors):

1. The limited introductory material, and the Discussion do not appropriately place this important, indeed critical, work in the context it deserves. In general, the expectation for a general science journal such as eLife demands greater attention to scholarship and contextualization for the anticipated broad audience. Indeed, the Cover Letter argues that this work is relevant to the "NIH extramural research community" and that it is correcting a "mistaken belief" related to their Hoppe et al. 2019 paper. Furthermore, the authors reference the Taffe and Gilpin eLife Feature Article which explains at some length why these matters are of such pressing importance. This is indeed a very important study to publish, although perhaps it should be understood, and written, more as an extension of the ongoing inquiry into NIH funding practices and less as a correction of "mistaken belief" related to the prior paper. It is suggested that at the very least the Introduction and Discussion should address the impact of the funding disparity on (1) AA/B researchers and (2) research on the health concerns of Black Americans, in a scholarly way, including reference to recent materials on this topic to guide the interested reader.

As noted previously, we have clarified that this analysis focuses on topics, not PIs.

2. Introduction Ln26-41: This section, currently the bulk of the Introduction, fails to describe why applications in some topics would be assigned to one IC over another. The "for example" section is a misdirection and distracting. The key question, given the data in Figure 1 and Table 1, is why a given grant would be assigned to NIMHD or NINR instead of the disease-domain parent institute.

We provide information, as well as a citation, on Institute assignments.

2. The Methods indicate that the 15 topics that accounted for 50% of the AA/B applications were designated as AA/B-preferred for analysis. There is no rationale or justification provided for this choice. Why not the topics that accounted for 66% or 33% or 75%? It would appear critical to provide a strong justification, and some indication of what impact other choices for what is an apparently arbitrary cutoff would have on the central analyses.

This is an excellent point. We added analyses on AAB Preference as a continuous variable (equation 1, Figures 2 and 5), present a cumulative distribution plot (Figure 1), and test a different cut-point (Figure 1 and Table 4).

3. The Conclusion section presages what will be takeaway message for many readers, i.e. "likely primarily due to". Yet, from this analysis, this conclusion which is likely to be assumed to apply to all AA/B application leaves any disparity of funding for the topics associated with half of the applications with AA/B PIs ignored.

This is an analysis of topics, not AAB PIs. As noted, we considered topic preferences as continuous and categorical variables (equation 1; Figures 1, 2, and 5; new Table 4). We clarify in the re-written conclusion where our interpretations are based (mainly from the probit regressions shown in Tables 5 and 6).

4. The Methods indicate that the definition of AA/B-preferred ICs was the top quartile (6/24 ICs), but there is no rationale or justification provided for this choice. In the absence of a strong justification, it would appear critical to provide some indication of what impact other essentially arbitrary cutoff choices would have on the central analyses. For example, of the five (Fogerty International Center appears to be omitted due to "privacy concerns") ICs with the highest percentage of AA/B applications shown in Table 1, the two lowest are 2.13% (AI) and 2.06% (ES) which are much closer to 12 remaining ICs in AA/B percentage than they are to the third lowest of the AA/B upper quartile. Three of these (DE, 1.99% AA/B; DA, 1.81% AA/B; MH, 1.69% AA/B) are more obviously grouped with AI and ES in terms of AA/B award rate than are the three most extreme AA/B-preferred ICs (HD, NR, MD). This differs from the choices made in Hoppe et al. 2019 where it is fairly obvious from Figure 3A why the 8 topic clusters receiving a high percentage of AA/B applications, and the 8 with zero, were selected for analysis. From Table 1 of this manuscript, the top quartile of ICs account for only 27.3% of AA/B applications. Inclusion of DE, DA and MH would increase this to 48.5% of the AA/B applications, more consistent with the 50% threshold used by the authors to designate AA/B-preferred topics. These choices are not explained or justified and it is critical to give some indication of whether the central conclusions depend on such arbitrary choices.

We agree with the reviewer that we should beyond one categorical cut-off. See comments to Editor; Figures 1, 2, and 5; Table 4.

5. Table 1 is missing a statistic which is key to the authors' conclusions. The award rate for applications with AA/B versus white PIs should be listed per IC, instead of only the aggregate award rate. The entire argument hinges on the presumption that there is no difference in the white/AA/B success rate ratio across the ICs, yet we have seen no analysis of this in prior papers nor does the information occur here.

This paper is an analysis of topics, not PIs.

6. Page 3, Line 102: The phrase "submitted [applications] to" in the context of IC assignment is an improper misrepresentation. This requires correcting throughout, including the Abstract and Line 163 of the Discussion. While applicants can request assignment to a given I or C, this is not always respected and thus this phrasing should reflect NIH assignment, not π choice. This aspect of application assignment (and any dual assignments and selection/refusal to pick up as a secondary IC) should be incorporated into the analysis. Are applications with AA/B PIs more or less likely to be assigned by the NIH's receipt and referral process to the lower-success institutes instead of the parent IC? Are applications with AA/B PIs more likely to request those low-success rate ICs? Are assignment requests equivalently respected? Are applications with AA/B PIs more or less likely to be dual-assigned and/or selected for funding by the secondary IC? What about the role of shared funding between ICs?

We agree, and changed the language to “assigned to”. We address dual assignments.

7. Page 5, Line 123: While the regression coefficients are useful for a sophisticated audience, it is essential to report the results in a more easily understandable manner for a more general audience. Most importantly the results should be expressed in a way that is comparable with the prior analyses provide by Ginther, Hoppe and their colleagues and indeed is mentioned in the first paragraph of the Introduction. The reader should be informed clearly what percentage of the disparity is accounted for here, similar to the ~20-25% of the racial bias effect accounted for by topic in Hoppe et al. 2019 and by CV metrics in Ginther et al. 2018. This will then help to make the Abstract and Conclusion statements more precise.

In the table legends for Tables 5 and 6, equation 7, Results text, and Discussion / Conclusion text, we take steps to be precise in our explanations, including walking the reader through the tables.

8. Figure 1 is confusing in juxtaposition with Table 1 and in context with the overall thrust of the paper. It is suggested that an additional panel be added in which the X axis is the AA/B percentage of applications to that IC, as in Table 1. That is what the reader is expecting at first, and it takes some close reading to figure out these are rankings of the ICs based on submissions by topic. In addition, the six AA/B-preferred ICs should be clearly indicated on-figure by means of colored text, bolded text, a circle or something similar.

The ranking of ICs is by award rate; we added language to the Table legend. We added a color-legend to specific ICs in Figure 4.

9. Discussion, page 10: The second paragraph refers to the funding ecology and ICs with lower award rates, reflecting language used throughout the manuscript. There does not appear to be any description of this ecology and the general science reader will not understand the tremendous disparity in IC funding (e.g., NIMHD and NINR, the extreme outliers in % of AA/B π applications, received 0.8% and 0.4% of the FY2000 budget) without this information. It is essential to add columns in Table 1 that show the average amount of money assigned to each IC across the relevant FYs and to also represent this as a percentage to assist the reader. This context then needs to be mentioned in both Introduction and Discussion where points about IC award rates and funding "ecology" are being made. By way of analogy, the readership of eLife expects an extensive mechanistic explanation without key omissions to accompany empirical results and this is similar.

We added a column in Table 1 on the 2015 appropriations to each IC.

10. Discussion, page 10: The second paragraph is an outline of the illogic captured in this entire investigation. It cleverly draws indirect lines from the overall AA/B disparity of award to the success rate of ICs while seemingly ignoring the fact the analysis reflects only the topics preferred by half of the AA/B applications and reports on only 27.3% of AA/B applications. It is unclear why the primary analysis approach for this inquiry is not simply to examine white vs AA/B success rates across each of the ICs. This would be a much simpler and intuitive answer to what appear to be the authors originating question. The Hoppe report admitted, but tried to undersell, the critically important finding that even in the least-successful quintile of topics, the AA/B applications faired more poorly and it would be critical to understand if that is also true in this analysis of IC assignment.

See above at the beginning of this letter.

11. Discussion, page 10: The third and fourth paragraphs are written in imprecise language that does a disservice to the reader understanding of the size of the effects that are being reported. The Hoppe sentence should just include the ~20% number. The comments about "just as well", "even better", "fare a bit worse" and "to a much lower degree" need quantitation.

In the text descriptions of the probit regression analyses, we provided quantitative terms.

12. Discussion, page 11, Line 179: The statement is utterly speculative and should be removed or backed with better evidence.

We agree; new Figure 2 provides that evidence.

13. Conclusion, page 11: The use of "explain disparities to an even greater degree" needs to be accompanied by an estimate of the effect size, similar to the data in Hoppe underlying the "topic choice does partially explain" beginning of the sentence. In Hoppe, it was something on the order of accounting for only 21% of the white/AAB disparity of grants that were discussed. In this manuscript the authors need to be clearer about what amount of which racial disparity does "an even greater degree" reflect?

We have rewritten the conclusion to be more quantitative and to indicate where in the probit regression models we base our interpretations.

14. Conclusion, page 11: The third sentence again inappropriately draws conclusions from the indirect logic available from the design. The authors need to show the IC-specific award rates for white and AA/B applicants within each IC and contrast these by IC-specific award rate if they wish to make this statement.

See above at the beginning of this letter.

15. Supplementary Materials: As mentioned above, there is not a rationale provided for excluding the competing continuation applications from the main analysis. This is peculiar given that these were included in Hoppe and the current manuscript is framed as an explanation of those prior results. If the outcome is the same, it would make more sense to make the full sample the subject of the main paper and the more selected sub-sample the supplement.

We try to explain better why it is inappropriate to combine first submissions with resubmissions and why to exclude competing renewals. Both resubmissions and competing renewals receive systematically different peer review outcomes.

Reviewer #4 (Recommendations for the authors):

The paper is well written and uses appropriate methods in the described analysis. While this analysis does contribute to understanding the differential outcomes that have been previously described in the funding of AAB investigators, there are still unanswered questions about the success rate differentials. The topics examined only account for 50% of the applications. If there is little or no biases as is suggested by the authors, the other 50% should be able to be accounted for using the analysis performed in this paper. The authors should consider adding a bit of conditional conclusion of these findings.

As noted, we added AAB Preference as a continuous variable (equation 1, Figure 2 and 5) and tested a different cut-point (Figure 1, Table 4).

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Source data 1. csr_anon_id.RData.

elife-67173-data1.zip^{(4.1MB, zip)}

Source code 1. Main Manuscript.Rmd.

elife-67173-code1.zip^{(15.6KB, zip)}

Source code 2. Appendix 1.Rmd.

elife-67173-code2.zip^{(4.8KB, zip)}

Source code 3. Appendix 2.Rmd.

elife-67173-code3.zip^{(4.8KB, zip)}

Transparent reporting form

elife-67173-transrepform.docx^{(111.1KB, docx)}

Data Availability Statement

[bib1] CSR The assignment process. [April 25, 2021];2021 https://public.csr.nih.gov/ForApplicants/SubmissionAndAssignment/DRR/assignmentprocess

[bib2] Ginther DK, Schaffer WT, Schnell J, Masimore B, Liu F, Haak LL, Kington R. Race, ethnicity, and NIH research awards. Science. 2011;333:1015–1019. doi: 10.1126/science.1196783. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib3] Harrison E. Tom Drake, and ots Quickly Create Elegant Regression Results Tables and Plots when Modelling. [April 25, 2021];2020 https://finalfit.org/index.html

[bib4] Hoppe TA, Litovitz A, Willis KA, Meseroll RA, Perkins MJ, Hutchins BI, Davis AF, Lauer MS, Valantine HA, Anderson JM, Santangelo GM. Topic choice contributes to the lower rate of NIH awards to African-American/black scientists. Science Advances. 2019;5:eaaw7238. doi: 10.1126/sciadv.aaw7238. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib5] Lauer MS. Are attempts at renewal successful? [April 25, 2021];2016 https://nexus.od.nih.gov/all/2016/02/16/are-attempts-at-renewal-successful/

[bib6] Lauer MS. Resubmissions revisited: funded resubmission applications and their initial peer review scores. [April 25, 2021];2017 https://nexus.od.nih.gov/all/2017/02/17/resubmissions-revisited-funded-resubmission-applications-and-their-initial-peer-review-scores/

[bib7] Leifeld P. Texreg: conversion of statistical model output in R to LaTeX and HTML tables. Journal of Statistical Software. 2013;55:1–24. doi: 10.18637/jss.v055.i08. [DOI] [Google Scholar]

[bib8] Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. arXiv. 2013 https://arxiv.org/abs/1301.3781

[bib9] NIH ACD meetings. [April 25, 2021];2021a https://www.acd.od.nih.gov/meetings.html

[bib10] NIH NIH — Office of Budget —Appropriations History by Institute/Center (1938 to Present) [April 25, 2021];2021b https://officeofbudget.od.nih.gov/approp_hist.html

[bib11] NIH NIH strategic plans and visions | RePORT. [April 25, 2021];2021c https://report.nih.gov/reports/strategic-plans

[bib12] R Foundation R: The R Project for Statistical Computing. 2021 https://www.r-project.org/

[bib13] Taffe MA, Gilpin NW. Racial inequity in grant funding from the US National Institutes of Health. eLife. 2021;10:e65697. doi: 10.7554/eLife.65697. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib14] Tromberg BJ, Schwetz TA, Pérez-Stable EJ, Hodes RJ, Woychik RP, Bright RA, Fleurence RL, Collins FS. Rapid scaling up of Covid-19 diagnostic testing in the united states — The NIH RADx Initiative. New England Journal of Medicine. 2020;383:1071–1077. doi: 10.1056/NEJMsr2022263. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib15] Wickham H. GGplot2: Elegant Graphics for Data Analysis. New York: Springer-Verlag; 2016. [DOI] [Google Scholar]

[bib16] Wickham H, Averick M, Bryan J, Chang W, McGowan L, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen T, Miller E, Bache S, Müller K, Ooms J, Robinson D, Seidel D, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H. Welcome to the tidyverse. Journal of Open Source Software. 2019;4:1686. doi: 10.21105/joss.01686. [DOI] [Google Scholar]

PERMALINK

Associations of topic-specific peer review outcomes and institute and center award rates with funding disparities at the National Institutes of Health

Michael S Lauer

Jamie Doyle

Joy Wang

Deepshikha Roychowdhury

Roles

Abstract

Introduction

Materials and methods

Data and descriptive measures

Regression analyses

Figure 1. Cumulative percentage plot showing the association of cumulative percentage of applications submitted by AAB Principal Investigators and cumulative percentage of topics.

Results

Table 1. Application characteristics according to Institute or Center (IC), as ranked by award rate (Equation 2, highest to lowest).

Table 2. Application review and funding outcomes according to whether Institute or Center received a higher or lower proportion of applications from AAB principal investigators.

Review outcomes according to topic

Figure 2. Topic peer review outcomes according to proportion of AAB applications linked to specific topics.

Figure 3. Topic peer review scores according to number of applications received ('Topic Size') and topic type (AAB Preferred or Other).

Funding outcomes according to topic: descriptive findings

Table 3. Application review and funding outcomes according to whether topic was among those that accounted for half of all AAB applications.

Table 4. Application review and funding outcomes according to whether topic was among those that accounted for 70% of all AAB applications.

Figure 4. Scatter plot of IC-specific award rates according to proportion of IC applications that focus on AAB Preferred topics.

Figure 5. Descriptive analyses of actual (Equation 3) and predicted (Equation 4) topic-specific award rates.

Funding outcomes according to topic: probit regression models

Table 5. Probit Regression Models (regression coefficients and standard errors) with focus on the PI.

Table 6. Probit Regression Models (regression coefficients and standard errors) with focus on topic type.

Resubmission and single-PI applications

Discussion

Conclusion

Appendix 1

Resubmission applications

Appendix 1—figure 1. Cumulative percentage plot showing the association of cumulative percentage of applications submitted by AAB Principal Investigators and cumulative percentage of topics.

Appendix 1—figure 2. Topic peer review outcomes according to proportion of AAB applications linked to specific topics.

Appendix 1—table 1. Application review and funding outcomes according to whether topic was among those that accounted for half of all AAB applications.

Appendix 1—table 2. Probit Regression Models (regression coefficients and standard errors) with focus on topic type.

Appendix 2

Single PI applications

Appendix 2—figure 1. Cumulative percentage plot showing the association of cumulative percentage of applications submitted by AAB Principal Investigators and cumulative percentage of topics.

Appendix 2—figure 2. Topic peer review outcomes according to proportion of AAB applications linked to specific topics.

Appendix 2—table 1. Application review and funding outcomes according to whether topic was among those that accounted for half of all AAB applications.

Appendix 2—table 2. Probit Regression Models (regression coefficients and standard errors) with focus on topic type.

Funding Statement

Contributor Information

Additional information

Competing interests

Author contributions

Additional files

Data availability

References

Decision letter

Roles

Author response

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases