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The allofus R package is aimed at a wide range of researchers who wish to conduct complex analyses using best practices for reproducibility and transparency, and who have a range of experience using R. Because the All of Us data are transformed into the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM), researchers familiar with existing OMOP CDM tools or who wish to conduct network studies in conjunction with other OMOP CDM data will also find value in the package.</p><p><strong>Scope: </strong>We developed an initial set of functions that solve problems we experienced across survey and electronic health record data in our own research and in mentoring student projects. The package will continue to grow and develop with the All of Us Research Program. The allofus R package can help build community research capacity by increasing access to the All of Us Research Program data, the efficiency of its use, and the rigor and reproducibility of the resulting research.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7000,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"allofus: an R package to facilitate use of the All of Us Researcher Workbench.\",\"authors\":\"Louisa H Smith, Robert Cavanaugh\",\"doi\":\"10.1093/jamia/ocae198\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>Despite easy-to-use tools like the Cohort Builder, using All of Us Research Program data for complex research questions requires a relatively high level of technical expertise. 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引用次数: 0
摘要
目标:尽管有队列生成器等易于使用的工具,但使用 "我们所有人 "研究计划的数据来解决复杂的研究问题需要相对较高的专业技术水平。我们的目标是通过 R 软件包 allofus 提高研究和培训能力,减少 "我们所有人 "社区的准入门槛。在本文中,我们将介绍一些功能,这些功能可解决我们在使用我们所有人研究计划数据时遇到的常见难题,我们还将以通过综合电子健康记录和调查数据来创建我们所有人参与者队列的例子来演示这些功能:我们所有人研究计划的数据可供健康研究人员广泛使用。allofus R 软件包的目标受众是希望使用可重复性和透明度方面的最佳实践进行复杂分析,并具有一定 R 使用经验的广大研究人员。由于 All of Us 数据已转化为观察性医疗结果合作组织通用数据模型(OMOP CDM),因此熟悉现有 OMOP CDM 工具或希望结合其他 OMOP CDM 数据进行网络研究的研究人员也会发现该软件包的价值:我们开发了一套初步功能,以解决我们在自己的研究和指导学生项目中遇到的调查和电子健康记录数据问题。该软件包将随着 "我们所有人 "研究计划继续成长和发展。allofus R 软件包可以提高对 "我们所有人研究计划 "数据的访问、使用效率以及研究的严谨性和可重复性,从而帮助提高社区研究能力。
allofus: an R package to facilitate use of the All of Us Researcher Workbench.
Objectives: Despite easy-to-use tools like the Cohort Builder, using All of Us Research Program data for complex research questions requires a relatively high level of technical expertise. We aimed to increase research and training capacity and reduce barriers to entry for the All of Us community through an R package, allofus. In this article, we describe functions that address common challenges we encountered while working with All of Us Research Program data, and we demonstrate this functionality with an example of creating a cohort of All of Us participants by synthesizing electronic health record and survey data with time dependencies.
Target audience: All of Us Research Program data are widely available to health researchers. The allofus R package is aimed at a wide range of researchers who wish to conduct complex analyses using best practices for reproducibility and transparency, and who have a range of experience using R. Because the All of Us data are transformed into the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM), researchers familiar with existing OMOP CDM tools or who wish to conduct network studies in conjunction with other OMOP CDM data will also find value in the package.
Scope: We developed an initial set of functions that solve problems we experienced across survey and electronic health record data in our own research and in mentoring student projects. The package will continue to grow and develop with the All of Us Research Program. The allofus R package can help build community research capacity by increasing access to the All of Us Research Program data, the efficiency of its use, and the rigor and reproducibility of the resulting research.
期刊介绍:
JAMIA is AMIA''s premier peer-reviewed journal for biomedical and health informatics. Covering the full spectrum of activities in the field, JAMIA includes informatics articles in the areas of clinical care, clinical research, translational science, implementation science, imaging, education, consumer health, public health, and policy. JAMIA''s articles describe innovative informatics research and systems that help to advance biomedical science and to promote health. Case reports, perspectives and reviews also help readers stay connected with the most important informatics developments in implementation, policy and education.