Onken James, Miklos Andrew C, Dorsey Travis F, Aragon Richard, Calcagno Anna Maria
Research Enterprise Analytics, LLC, 21 Hardwicke Place, Rockville, MD 20850, USA.
National Institute of General Medical Sciences, National Institutes of Health, MSC 6200, 45 Center Drive, Bethesda MD 20892-6200, USA.
Eval Program Plann. 2019 Dec;77:101710. doi: 10.1016/j.evalprogplan.2019.101710. Epub 2019 Sep 3.
Here, we report the results of an outcomes evaluation of the Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) Programs at the National Institute of General Medical Sciences (NIGMS). Since the programs' inception, assessments of the SBIR/STTR programs at several federal agencies have utilized surveys of former grantees as the primary source of data. Response rates have typically been low, making non-response bias a potential threat to the validity of some of these studies' results. Meanwhile, the availability of large publicly-available datasets continues to grow and methods of text mining and linking databases continue to improve. By linking NIGMS grant funding records, U.S. Patent and Trademark Office data, and business intelligence databases, we explored innovation, commercialization and survival for recipients of NIGMS SBIR/STTR funding. In doing so, we were able to more completely assess several key outcomes of the NIGMS SBIR/STTR program. Our evaluation demonstrated that the NIGMS program performed above baseline expectations along all dimensions, and comparably to other federal agency SBIR/STTR grant programs. In addition, we show that the use of extant data increasingly is a viable, less expensive, and more reliable approach to gathering data for evaluation studies.
在此,我们报告美国国立综合医学科学研究所(NIGMS)的小企业创新研究(SBIR)和小企业技术转让(STTR)计划的成果评估结果。自这些计划启动以来,多个联邦机构对SBIR/STTR计划的评估都将对前受资助者的调查作为主要数据来源。回应率通常较低,这使得无回应偏差成为这些研究部分结果有效性的潜在威胁。与此同时,大型公开可用数据集的数量持续增加,文本挖掘和数据库链接方法也在不断改进。通过将NIGMS资助记录、美国专利商标局数据和商业智能数据库相链接,我们探究了NIGMS SBIR/STTR资助接受者的创新、商业化和存续情况。通过这样做,我们能够更全面地评估NIGMS SBIR/STTR计划的几个关键成果。我们的评估表明,NIGMS计划在所有维度上的表现均高于基线预期,且与其他联邦机构的SBIR/STTR资助计划相当。此外,我们表明,使用现有数据越来越成为一种可行、成本更低且更可靠的评估研究数据收集方法。