From the Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA.
Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA.
Epidemiology. 2023 May 1;34(3):389-395. doi: 10.1097/EDE.0000000000001599. Epub 2023 Jan 31.
To increase research reproducibility, sharing of study data, analysis code, and use of standardized reporting are increasingly advocated. However, beyond reproducibility, few initiatives have addressed the integrity of how research is conducted before manuscripts are submitted. We describe a decades-long experience with a comprehensive approach based in an academic research community around prospective cohort studies that is aimed at promoting a culture of integrity in observational research. The approach includes prespecifying hypotheses and analysis plans, which are discussed in the research community and posted; presentation and discussion of analysis results; mandatory analysis code review by a programmer; review of concordance between analysis output and manuscripts by a technical reviewer; and checks of adherence to the process, including compliance with institutional review board requirements and reporting stipulations by the National Institutes of Health. The technical core is based in shared computing and analytic environments with long-term archiving. More than simply a list of rules, our approach promotes research integrity through integrated educational elements, making it part of the "hidden curriculum," by fostering a sense of belonging, and by providing efficiency gains to the research community. Unlike reproducibility checklists, such long-term investments into research integrity require substantial and sustained funding for research personnel and computing infrastructure. Our experiences suggest avenues for how institutions, research communities, and funders involved in observational research can strengthen integrity within the research process.
为了提高研究的可重复性,越来越提倡分享研究数据、分析代码,并使用标准化报告。然而,除了可重复性之外,很少有举措涉及到提交手稿之前研究开展的完整性。我们描述了一个长达数十年的经验,即围绕前瞻性队列研究的学术研究社区中采取了一种全面的方法,旨在促进观察性研究中的诚信文化。该方法包括预先指定假设和分析计划,这些计划在研究社区中进行讨论并发布;呈现和讨论分析结果;由程序员对分析代码进行强制性审查;由技术审查员审查分析结果与手稿之间的一致性;以及检查是否遵守该过程,包括遵守机构审查委员会的要求和美国国立卫生研究院的报告规定。技术核心基于具有长期存档功能的共享计算和分析环境。我们的方法不仅仅是一系列规则,它通过综合教育元素来促进研究诚信,通过培养归属感,并为研究社区提供效率提升,使其成为“隐性课程”的一部分。与可重复性检查表不同,这种对研究诚信的长期投资需要为研究人员和计算基础设施提供大量且持续的资金。我们的经验为参与观察性研究的机构、研究社区和资助者提供了如何在研究过程中加强诚信的途径。