Ten years of NIHR research training: who got an award? A retrospective cohort study

In 2017, the National Institute for Health Research (NIHR) academy produced a strategic review of training, which reported the variation in application characteristics associated with success rates. It was noted that variation in applicant characteristic was not independent of one another. Therefore, the aim of this secondary analysis was to investigate the inter-relationships in order to identify factors (or groups of factors) most associated with application numbers and success rates.

Retrospective data were gathered from 4388 applications to NIHR Academy between 2007 and 2016. Multinominal logistic regression models quantified the likelihood of success depending on changes in the explanatory factors; relative risk ratios with 95% CIs. A classification tree analysis was built using exhaustive χ² automatic interaction detection to better understand the effect of interactions between explanatory variables on application success rates.

936 (21.3%) applications were awarded. Applications from males and females were equally likely to be successful (p=0.71). There was an overall reduction in numbers of applications from females as award seniority increased from predoctoral to professorship. Applications from institutions with a medical school had a 2.6-fold increase in likelihood of success (p<0.001). Classification tree analysis revealed key predictors of application success: award level, type of programme, previous NIHR award experience and applying form a medical school.

Success rates did not differ according to gender, and doctors were not more likely to be successful than applications from other professions. Taken together, these findings suggest an essential fairness in how the quality of a submitted application is assessed, but they also raise questions about variation in the opportunity to submit a high-quality application. The companion qualitative study (Burkshaw et al. (2021) BMJ Open) provides valuable insight into potential candidate mechanisms and discusses how research capacity development initiatives might be targeted in the future.