Quantitative Methods for HIV/AIDS Research

艾滋病毒/艾滋病生物医学研究人员小组，他们自己进行数据分析并咨询

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.