Department of Pathology and Immunology, Washington University in St. Louis School of Medicine, 660 S. Euclid, Box 8118, St. Louis, MO, 63110, USA.
University Libraries, Washington University in St. Louis, 1 Brookings Drive, Campus Box 1061, St. Louis, MO, 63130, USA.
BMC Med Res Methodol. 2017 Sep 18;17(1):143. doi: 10.1186/s12874-017-0377-6.
The reproducibility of research is essential to rigorous science, yet significant concerns of the reliability and verifiability of biomedical research have been recently highlighted. Ongoing efforts across several domains of science and policy are working to clarify the fundamental characteristics of reproducibility and to enhance the transparency and accessibility of research.
The aim of the proceeding work is to develop an assessment tool operationalizing key concepts of research transparency in the biomedical domain, specifically for secondary biomedical data research using electronic health record data. The tool (RepeAT) was developed through a multi-phase process that involved coding and extracting recommendations and practices for improving reproducibility from publications and reports across the biomedical and statistical sciences, field testing the instrument, and refining variables.
RepeAT includes 119 unique variables grouped into five categories (research design and aim, database and data collection methods, data mining and data cleaning, data analysis, data sharing and documentation). Preliminary results in manually processing 40 scientific manuscripts indicate components of the proposed framework with strong inter-rater reliability, as well as directions for further research and refinement of RepeAT.
The use of RepeAT may allow the biomedical community to have a better understanding of the current practices of research transparency and accessibility among principal investigators. Common adoption of RepeAT may improve reporting of research practices and the availability of research outputs. Additionally, use of RepeAT will facilitate comparisons of research transparency and accessibility across domains and institutions.
研究的可重复性对于严谨的科学至关重要，但最近人们对生物医学研究的可靠性和可验证性提出了重大担忧。目前，科学和政策的多个领域正在共同努力，以阐明可重复性的基本特征，并提高研究的透明度和可及性。
本研究旨在开发一种评估工具，用于在生物医学领域实施研究透明度的关键概念，特别是针对使用电子健康记录数据的二级生物医学数据研究。该工具（RepeAT）是通过多阶段的过程开发的，包括对生物医学和统计学领域的出版物和报告中的建议和实践进行编码和提取，以提高可重复性，对仪器进行现场测试，并改进变量。
RepeAT 包括 119 个独特的变量，分为五个类别（研究设计和目的、数据库和数据收集方法、数据挖掘和数据清理、数据分析、数据共享和文档）。对 40 篇科学手稿进行手动处理的初步结果表明，该框架的组成部分具有很强的评分者间可靠性，以及进一步研究和改进 RepeAT 的方向。
使用 RepeAT 可能使生物医学社区更好地了解主要研究者当前的研究透明度和可及性实践。共同采用 RepeAT 可能会提高研究实践的报告水平，并增加研究成果的可用性。此外，使用 RepeAT 将有助于跨领域和机构比较研究的透明度和可及性。
