SCISIPBIO: Maximizing the Value of Sex-Inclusive Research Policies through Analytical Rigor

SCISIPBIO：通过严谨的分析最大限度地发挥性别包容性研究政策的价值

• 批准号: 2318478
• 负责人: Andrew Brown
• 金额: $ 26.44万
• 依托单位: Arkansas Childrens Hospital Research Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2318478&HistoricalAwards=false
• 关键词: SCISIPBIO; Maximizing; Value; Sex; Inclusive

Historically, preclinical research has focused disproportionately on males. This bias has limited the progress of science to advance national health and welfare. In 2016, to encourage inclusion of both males and females in research, the National Institutes of Health implemented a policy requiring the consideration of sex as a biological variable (SABV) in all funded studies. Although inclusion of females in research has improved, sex-based data are often not being analyzed using rigorous approaches. Our preliminary analysis of SABV-compliant studies showed that when authors report sex-specific effects, they tested statistically for such effects only 29% of the time. Instead, claims of sex-specific findings typically rest on assertion alone. Our previous findings indicate clearly that the implementation of SABV has not been rigorous and that the process of peer review of SABV-compliant research needs to improve. False claims of sex differences can lead to the wasting of resources on ‘differences’ that do not exist, and to inequities in access to effective treatments. Similarly, false claims of non-differences can lead to missed opportunities to provide effective health care for people of all sexes and genders. The intended goals of SABV, namely to enhance reproducibility and to facilitate sex-based precision medicine, are not being met. In this project we will determine the impact of inappropriate analytical approaches on the accurate reporting of sex-specific effects, identify factors that inform decisions about analytical approaches to sex-based data, and disseminate tools for designing and evaluating studies that consider SABV. We will use a mixed method approach that includes analyses of published journal articles, interviews with scientists, and outreach. We will conduct a large-scale analysis of the biomedical literature to show how inappropriate statistical approaches to sex-based data are impacting the rigorous implementation of SABV. Second, we will conduct semi-structured interviews with authors of some of those publications to identify the factors that facilitate best practices. Together, these methods will inform the development of a workflow for appropriate analysis of sex-based data. Third, we will make our optimized workflow publicly available and disseminate it widely. In addition, we will partner with journal editors to develop a simple tool that can be used by reviewers to evaluate the validity of sex-specific findings.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

从历史上看，临床前研究一直关注男性。这种偏见限制了科学进步，以促进国家卫生和福利。在2016年，为了鼓励将男性和女性纳入研究，美国国立卫生研究院实施了一项政策，在所有基本研究中都需要将性作为生物变量（SABV）。尽管在研究中纳入女性，但通常不会使用严格的方法来分析基于性别的数据。我们对符合SABV兼容研究的初步分析表明，当作者报告性别特异性效应时，他们仅在29％的时间进行了统计测试。取而代之的是，针对性别的发现的主张通常仅基于断言。我们以前的发现清楚地表明，SABV的实施并不是严格的，并且需要对符合SABV的研究的同行审查需要改进。性别差异的虚假主张可能导致资源浪费在不存在的“差异”上，以及获得有效治疗的不平等现象。同样，对非差异的虚假主张可能会导致错过的机会为所有性别和性别的人提供有效的医疗保健。 SABV的预期目标，即提高可重复性并促进基于性别的精确药物。在该项目中，我们将确定不适当的分析方法对性别特异性效应的准确报告的影响，确定因素为基于性别数据的分析方法提供信息的因素，并传播用于设计和评估考虑SABV的研究的工具。我们将使用一种混合方法，其中包括分析已发表的期刊文章，与科学家的访谈以及外展的分析。我们将对生物医学文献进行大规模分析，以展示基于性别数据的不适当统计方法如何影响SABV的严格实施。其次，我们将对其中一些出版物的作者进行半结构化访谈，以确定促进最佳实践的因素。这些方法将共同为工作流的开发提供信息，以适当分析基于性别的数据。第三，我们将公开提供优化的工作流并广泛传播。此外，我们将与期刊编辑合作开发一个简单的工具，审稿人可以使用该工具来评估性别特定发现的有效性。该奖项反映了NSF的法定任务，并通过评估该基金会的知识分子和更广泛的影响来评估NSF的法定任务，并被认为是宝贵的支持。