Quantitative Methods for HIV/AIDS Research

艾滋病毒/艾滋病生物医学研究人员小组进行自己的数据分析，并与 越来越多的生物统计学家发现标准咨询不够，因为需要 1)解决复杂的研究问题，2）理解大数据，3）确保分析是严格的。 迫切需要培训未来的艾滋病毒/艾滋病工作人员，以建立跨学科的大数据环境， 哪些具有专业方法学知识的定量专家正在合作项目， 适当研究人员需要接受培训，以确定他们自己的知识差距，并确定合作者 随着量化专业领域变得越来越复杂， 越来越专业化。为了解决这个问题，艾滋病毒/艾滋病社区需要制定培训计划， 研究人员和定量科学家-统计学家，计算机科学家，数学家，工程师等- 在团队科学中紧密合作。我们提出了一个交叉培训艾滋病毒/艾滋病研究人员的计划， 定量分析的基本概念，以及生物，临床和社会领域的定量科学家， 艾滋病研究的行为背景。该计划利用了资源的广度和深度， 艾滋病毒/艾滋病和定量科学在杜克提供真实世界的艾滋病毒/艾滋病研究经验与专家 不仅对杜克学员进行定量指导，而且对来自北卡罗来纳州地区机构的学员进行定量指导 他们可能无法获得这些资源。它旨在提供生物医学和定量 为跨学科艾滋病毒/艾滋病小组提供技能、机会、途径和资源（SOAR）的受训人员 科学在过去的项目期间，我们教授了数据科学，统计学和分析生物信息学，超过100 通过我们每年开发和实施的18个系列讲习班，为艾滋病毒/艾滋病初级研究人员提供培训。我们 我们还能够介绍50名统计学，数学和计算机科学的研究生， 通过我们的暑期实习计划进行多学科的艾滋病毒/艾滋病研究。在这项工作的基础上，我们 提出一个新的研究教育计划，旨在实现几个关键目标：首先，我们将提供 培训对艾滋病毒/艾滋病研究感兴趣的生物医学学员，以提高他们在数据科学方面的技能， 通过一系列以真实世界艾滋病病例为中心的研讨会， 问题研究二是实施量化研究生结构化实习培养计划 有一个增强的指导和团队科学经验，在现实世界的研究问题工作， 艾滋病毒/艾滋病调查的领导人。第三，我们将为生物医学培训生制定专门的指导计划 并提供跨学科的专业发展培训机会，包括长期专家 为学员提供数据驱动研究项目的定量指导。该计划将提供一个范例， 开发多学科团队科学培训计划，可用于发展劳动力 全国各地

Teams of HIV/AIDS biomedical researchers who perform their own data analysis and consult with biostatisticians as needed, are increasingly finding standard consultations inadequate because of the need to 1) tackle complex research questions, 2) make sense of big data, and 3) ensure that analysis is rigorous. There is a critical need to train the future HIV/AIDS workforce for an interdisciplinary big data environment in which quantitative experts with specialized methodological expertise are partnering on collaborative projects as appropriate. Researchers need to be trained to identify their own knowledge gaps and identify collaborators with the necessary expertise, which is getting more complex as quantitative expertise areas are becoming more and more specialized. To address this, the HIV/AIDS community needs to develop programs that train researchers and quantitative scientists — statisticians, computer scientists, mathematicians, engineers etc. — to work cohesively in team science. We propose a program to cross-train HIV/AIDS researchers in fundamental concepts of quantitative analysis, and quantitative scientists in the biological, clinical, and socio- behavioral context of HIV/AIDS research. This program capitalizes on the breadth and depth of resources in HIV/AIDS and in quantitative science at Duke to offer real-world HIV/AIDS research experiences with expert quantitative mentoring not only to Duke trainees, but also trainees from regional institutions in North Carolina who may not otherwise have access to these resources. It is designed to provide biomedical and quantitative trainees with the Skills, Opportunities, Access, and Resources (SOAR) for interdisciplinary HIV/AIDS team science. In the past project period, we taught data science, statistics, and assay bioinformatics to over 100 junior HIV/AIDS researchers through a series of 18 workshops we developed and implemented each year. We were also able to introduce 50 graduate students in statistics, mathematics, and computer science to multidisciplinary HIV/AIDS research through our summer internship program. To build upon this work, we are proposing a novel research education program designed to fulfill several key objectives: First, we will provide training for biomedical trainees interested in HIV/AIDS research to build up their skills in data science, predictive modeling, and assay analysis through a series of workshops centered on real-world HIV case studies. Second, we will implement a structured internship training program for quantitative graduate students to have an enhanced mentoring and team science experience working on real-world research problems with leading HIV/AIDS investigators. Third, we will develop a dedicated mentoring program for biomedical trainees and provide interdisciplinary professional development training opportunities, including long-term expert quantitative mentoring for trainees with data-driven research projects. This program will provide an exemplar for developing multidisciplinary team science training programs that can be used to develop the workforce across the country.