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

基本信息

项目摘要

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。 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(NIH)实施了一项政策,在所有基本研究中都需要将性作为生物学变量(SABV)。尽管包括女性的包含有所改善,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。我们过去的发现清楚地表明,研究人员对SABV的实施并没有像它那样严格,并且需要改进对符合SABV的研究的同行审查的过程。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。在这个项目中,我们将确定AIM 1的影响,我们将对生物医学文献进行大规模分析,以显示如何不适当。基于性别数据的统计方法正在影响SABV的严格实施。在AIM 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。总之,前两个目标将为工作流程的开发提供信息,以适当分析基于性别的数据。在AIM 3中,我们将公开提供优化的工作流程并广泛传播。这项工作的很大一部分将涉及期刊编辑和其他可能的审阅者直接在出版的背景下应用工具,从而增加了性别差异的报告,进而增加了SABV政策的有效性。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

暂无数据

数据更新时间:2024-06-01

DONNA L MANEY的其他基金

A New Animal Model of Social Reward
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  • 批准号:
    9252522
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A New Animal Model of Social Reward
社会奖励的新动物模型
  • 批准号:
    9036078
    9036078
  • 财政年份:
    2016
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    $ 20.08万
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Resource development for a new model of social threat response
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  • 批准号:
    8771140
    8771140
  • 财政年份:
    2014
  • 资助金额:
    $ 20.08万
    $ 20.08万
  • 项目类别:
Resource development for a new model of social threat response
社会威胁应对新模式的资源开发
  • 批准号:
    9117844
    9117844
  • 财政年份:
    2014
  • 资助金额:
    $ 20.08万
    $ 20.08万
  • 项目类别:
A Unique Natural Model for Studying the Mechanisms Underlying Social Behavior
研究社会行为背后机制的独特自然模型
  • 批准号:
    8213453
    8213453
  • 财政年份:
    2010
  • 资助金额:
    $ 20.08万
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A Unique Natural Model for Studying the Mechanisms Underlying Social Behavior
研究社会行为背后机制的独特自然模型
  • 批准号:
    7889278
    7889278
  • 财政年份:
    2010
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A Unique Natural Model for Studying the Mechanisms Underlying Social Behavior
研究社会行为背后机制的独特自然模型
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    8411272
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  • 财政年份:
    2010
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A Unique Natural Model for Studying the Mechanisms Underlying Social Behavior
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    8063210
    8063210
  • 财政年份:
    2010
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    $ 20.08万
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