Improving Applied Biostatistics Skills for AI/AN Researchers

提高人工智能/AN 研究人员的应用生物统计学技能

• 批准号: 10487411
• 负责人: JODI A LAPIDUS
• 金额: $ 15.27万
• 依托单位: NORTHWEST PORTLAND AREA INDIAN HLTH BD
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-10 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10487411
• 关键词: Address; Behavioral; Benchmarking; Biological; Biometry; Biostatistical Methods; Clinical; Communities; Companions; Competence; Complex; Data; Data Analyses; Data Science; Data Scientist; Data Sources; Development; Distance Learning; Educational Curriculum; Educational workshop; Employee; Epidemiology; Ethics; Etiology; Evaluation; Fostering; Funding; Glean; Goals; Grant; Health; Health Priorities; Health Professional; Health Sciences; Home; Individual; Institutes; Institution; Investigation; Knowledge; Lead; Mentors; Mentorship; Methods; Modernization; Native American Research Center for Health; Participant; Persons; Play; Population; Process; Publishing; Research; Research Personnel; Role; Schedule; Science; Scientist; Statistical Computing; Strategic Planning; Stress; Students; Time; Training; Training Programs; Tribes; Trust; United States National Institutes of Health; Work; career; career development; cohort; complex data; computer program; data handling; data visualization; data wrangling; design; health disparity; health science research; improved; insight; interest; meetings; member; minority scientist; novel; population based; practical application; programs; skills; social; sound; tribal community; tribal health

Project Summary: Improving Applied Biostatistics Skills for AI/AN Researchers The NIH Strategic Plan for Tribal Health Research 2019-2023 outlines priority health research needs for AI/AN communities, and emphasizes the importance of developing a pipeline of well-trained AI/AN researchers to address these needs. Individuals skilled in biostatistics and/or data science are essential members of health research teams, especially given the proliferation of health-related data in recent times. However, there are few AI/AN with this type of training who are currently contributing to health sciences research. The goal of this capacity-building grant is to increase the number of tribal researchers and tribal health professionals who can apply sound biostatistical reasoning, efficiently work with data, and conduct appropriately rigorous analyses, so that they can further the research efforts in tribal health. To meet this goal, we have designed and will implement a modern quantitative training curriculum that fosters career development in AI/AN trainees who aspire to analytically-oriented roles in health sciences research. We will accomplish this via in-person intensive institutes (1 per year for 2 years), monthly distance learning sessions (monthly for 2 years), and a mentored capstone project. Throughout the curriculum, we will emphasize practical applications of biostatistics methods, as well as data handling, data visualization and contemporary data science methods. We will stress strategies for designing ethical, yet rigorous studies, developing appropriate analysis plans, and clearly communicating findings to diverse audiences. Graduates of this program will be poised to further their research-related careers which will contribute towards building a strong, coordinated community of AI/AN health researchers and expanding scientific knowledge to improve the health of AI/AN communities.

项目摘要：提高人工智能/人工智能研究人员的应用生物统计学技能 《2019-2023年美国国立卫生研究院部落卫生研究战略计划》概述了人工智能/人工智能的优先卫生研究需求 社区，并强调开发训练有素的人工智能/人工智能研究人员渠道的重要性 满足这些需求。精通生物统计学和/或数据科学的个人是健康的重要成员 研究团队，特别是考虑到最近与健康有关的数据的激增。然而，有一些 很少有接受过这种培训的人工智能/人工智能目前正在为健康科学研究做出贡献。这样做的目的是 能力建设赠款是为了增加部落研究人员和部落卫生专业人员的数量，这些人可以 应用合理的生物统计学推理，高效地处理数据，并进行适当严格的分析，因此 他们可以进一步在部落健康方面进行研究。为了实现这一目标，我们已经设计并将 实施现代量化培训课程，促进AI/AN学员的职业发展 渴望在健康科学研究中发挥以分析为导向的作用。我们将通过面对面的密集培训来实现这一点 学院(两年内每年一次)、每月远程学习课程(两年内每月一次)和一名导师 顶石项目。在整个课程中，我们将强调生物统计学方法的实际应用， 以及数据处理、数据可视化和当代数据科学方法。我们将强调战略 用于设计合乎道德但严格的研究，制定适当的分析计划，并清晰地进行沟通 将调查结果提供给不同的受众。该项目的毕业生将准备进一步发展他们与研究相关的知识 有助于建立一个强大、协调的AI/AN健康研究人员社区的职业 以及扩大科学知识，以改善人工智能/人工智能社区的健康。