SCISIPBIO: Maximizing rigor and reproducibility when considering Sex as a Biological Variable in research

SCISIPBIO：在研究中将性别视为生物变量时最大限度地提高严谨性和可重复性

• 批准号: 10786440
• 负责人: DONNA L MANEY
• 金额: $ 20.08万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-03 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10786440
• 关键词: Biological; Data; Development; Effectiveness; Female; Funding; Gender; Goals; Health; Health Policy; Healthcare; Interview; Journals; Literature; Methods; Peer Review; Persons; Policies; Process; Publications; Publishing; Reporting; Reproducibility; Research; Research Personnel; Resources; Rest; Science; Scientist; Sex Differences; Structure; Testing; Time; United States National Institutes of Health; access disparities; design; effective therapy; improved; journal article; male; outreach; pre-clinical research; precision medicine; sex; tool; wasting; welfare

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 (NIH) implemented a policy requiring the consideration of sex as a biological variable (SABV) in all funded studies. Although inclusion of females 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 past findings indicate clearly that the implementation of SABV by researchers has not been as rigorous as it could be 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. In Aim 1, 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. In Aim 2, we will conduct semi-structured interviews with authors of some of those publications to identify the factors that facilitate best practices. Together, the first two aims will inform the development of a workflow for appropriate analysis of sex-based data. In Aim 3, we will make our optimized workflow publicly available and disseminate it widely. A large part of this effort will involve journal editors and other likely reviewers to apply the tools directly in the context of publishing, thus increasing rigor in the reporting of sex differences and, in turn, the effectiveness of SABV policies.

从历史上看，临床前研究主要集中在男性身上。这种偏见限制了促进国民健康和福利的科学进步。2016年，为了鼓励男性和女性参与研究，美国国立卫生研究院（NIH）实施了一项政策，要求在所有资助的研究中考虑性别作为生物变量（SABV）。虽然纳入女性的情况有所改善，但基于性别的数据往往没有使用严格的方法进行分析。我们对符合sabv的研究的初步分析表明，当作者报告性别特异性效应时，他们只有29%的时间进行了统计测试。相反，性别特异性发现的主张通常只依赖于断言。我们过去的研究结果清楚地表明，研究人员对SABV的实施并没有像它应该的那样严格，并且符合SABV的研究的同行评审过程需要改进。性别差异的错误主张可能导致资源浪费在不存在的“差异”上，并导致获得有效治疗的不平等。同样，不存在差异的错误主张可能导致错失为所有性别和性别的人提供有效保健的机会。SABV的预期目标，即提高可重复性和促进基于性别的精准医学，尚未实现。在这个项目中，我们将确定不适当的分析方法对准确报告性别特异性效应的影响，确定影响基于性别的数据分析方法决策的因素，并传播用于设计和评估考虑SABV的研究的工具。我们将采用混合方法，包括对发表的期刊文章的分析、对科学家的采访和推广。在目标1中，我们将对生物医学文献进行大规模分析，以显示对基于性别的数据的不适当统计方法如何影响SABV的严格实施。在目标2中，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。前两个目标将共同为制定适当分析基于性别的数据的工作流程提供信息。在Aim 3中，我们将使优化后的工作流公开可用并广泛传播。这项工作的很大一部分将涉及期刊编辑和其他可能的审稿人直接在出版背景下应用这些工具，从而提高报告性别差异的严谨性，进而提高SABV政策的有效性。