To assist new scientists in the transition to independent research careers, the National Institutes of Health (NIH) implemented an Early Stage Investigator (ESI) policy beginning with applications submitted in 2009. During the review process, the ESI designation segregates applications submitted by investigators who are within 10 years of completing their terminal degree or medical residency from applications submitted by more experienced investigators. Institutes/centers can then give special consideration to ESI applications when making funding decisions. One goal of this policy is to increase the probability of newly emergent investigators receiving research support. Using optimal matching to generate comparable groups pre- and post-policy implementation, generalized linear models were used to evaluate the ESI policy. Due to a lack of control group, existing data from 2004 to 2008 were leveraged to infer causality of the ESI policy effects on the probability of funding applications from 2011 to 2015. This article addresses the statistical necessities of public policy evaluation, finding administrative data can serve as a control group when proper steps are taken to match the samples. Not only did the ESI policy stabilize the proportion of NIH funded newly emergent investigators but also, in the absence of the ESI policy, 54% of newly emergent investigators would not have received funding. This manuscript is important to Research Evaluation as a demonstration of ways in which existing data can be modeled to evaluate new policy, in the absence of a control group, forming a quasi-experimental design to infer causality when evaluating federal policy.

中文翻译：

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对美国国立卫生研究院早期研究者政策的评估：使用现有数据评估联邦政策。


为了帮助新科学家过渡到独立研究职业，美国国立卫生研究院 (NIH) 从 2009 年提交的申请开始实施早期研究者 (ESI) 政策。在审查过程中，ESI 指定将由以下研究者提交的申请分开：根据更有经验的研究人员提交的申请，在完成最终学位或住院医师实习后的 10 年内。机构/中心在做出资助决定时可以特别考虑 ESI 申请。这项政策的目标之一是增加新出现的研究人员获得研究支持的可能性。使用最佳匹配来生成政策实施前后的可比较组，并使用广义线性模型来评估 ESI 政策。由于缺乏对照组，利用2004年至2008年的现有数据来推断ESI政策效应对2011年至2015年资助申请概率的因果关系。本文讨论了公共政策评估的统计必要性，发现行政数据可以当采取适当的步骤来匹配样本时，作为对照组。 ESI政策不仅稳定了NIH资助新晋研究者的比例，而且如果没有ESI政策，54%的新晋研究者将无法获得资助。这份手稿对于研究评估很重要，因为它展示了在没有对照组的情况下对现有数据进行建模以评估新政策的方法，形成准实验设计以在评估联邦政策时推断因果关系。