G. Samsa is associate professor, Department of Biostatistics and Bioinformatics, and director, Research Integrity Office, Duke University School of Medicine, Durham, North Carolina. L. Samsa is postdoctoral teaching scholar, BIT Biotechnology Program, North Carolina State University, Raleigh, North Carolina.
Acad Med. 2019 Jan;94(1):47-52. doi: 10.1097/ACM.0000000000002351.
Many have raised concerns about the reproducibility of biomedical research. In this Perspective, the authors address this "reproducibility crisis" by distilling discussions around reproducibility into a simple guide to facilitate understanding of the topic.Reproducibility applies both within and across studies. The following questions address reproducibility within studies: "Within a study, if the investigator repeats the data management and analysis, will she get an identical answer?" and "Within a study, if someone else starts with the same raw data, will she draw a similar conclusion?" Contrastingly, the following questions address reproducibility across studies: "If someone else tries to repeat an experiment as exactly as possible, will she draw a similar conclusion?" and "If someone else tries to perform a similar study, will she draw a similar conclusion?"Many elements of reproducibility from clinical trials can be applied to preclinical research (e.g., changing the culture of preclinical research to focus more on transparency and rigor). For investigators, steps toward improving reproducibility include specifying data analysis plans ahead of time to decrease selective reporting; more explicit data management and analysis protocols; and increasingly detailed experimental protocols, which allow others to repeat experiments. Additionally, senior investigators should take greater ownership of the details of their research (e.g., implementing active laboratory management practices, such as random audits of raw data [or at least reduced reliance on data summaries], more hands-on time overseeing experiments, and encouraging a healthy skepticism from all contributors). These actions will support a culture where rigor + transparency = reproducibility.
许多人对生物医学研究的可重复性提出了担忧。在本文观点中,作者通过将可重复性的讨论提炼为一个简单的指南,来解决这个“可重复性危机”,以促进对该主题的理解。可重复性既适用于单个研究内,也适用于多个研究间。以下问题针对单个研究内的可重复性:“在一项研究中,如果研究人员重复数据管理和分析,她是否会得到相同的答案?”和“在一项研究中,如果另一个人从相同的原始数据开始,她是否会得出类似的结论?”相比之下,以下问题针对多个研究间的可重复性:“如果其他人试图尽可能准确地重复实验,她是否会得出类似的结论?”和“如果其他人试图进行类似的研究,她是否会得出类似的结论?”许多临床试验中的可重复性元素可以应用于临床前研究(例如,改变临床前研究的文化,更加注重透明度和严谨性)。对于研究人员来说,提高可重复性的步骤包括提前指定数据分析计划,以减少选择性报告;更明确的数据管理和分析方案;以及越来越详细的实验方案,这可以使其他人重复实验。此外,资深研究人员应该更多地参与研究的细节(例如,实施积极的实验室管理实践,例如对原始数据进行随机审计[或至少减少对数据摘要的依赖],更多地亲自监督实验,并鼓励所有贡献者持健康的怀疑态度)。这些行动将支持一种严谨+透明度=可重复性的文化。