Biostatistics Core

艾滋病毒和酒精的现代研究在很大程度上依赖于研究设计、统计建模和推理以及有效和稳定的数据管理等领域的专门知识。纳入生物统计学核心的动机是需要向CARC调查员提供在该领域开展工作所需的各种工具和专业知识。拥有一个由统计和数据系统管理领域的教职员工组成的核心，将最大限度地减少或消除项目负责人自己寻找这些资源的需要。

Modern research in HIV and alcohol depends heavily on expertise in the areas of study design, statistical nnodeling and inference, and efficient and stable management of data. The inclusion of a Biostatistics Core is motivated by the need to provide access to CARC investigators to the diverse set of tools and expertise required to conduct work in this area. Having a core with faculty and staff from the fields of statistics and data systems management will minimize or eliminate the need for project leaders to seek these resources on their own. The mission ofthe Core is to promote the highest standards for study design, data analysis and data integrity for the individual CARC research projects. The Core will facilitate collaboration between biostatistics faculty and CARC investigators on issues related to design, analysis, and computing. We will work with investigators on standard aspects of design and analysis, and provide access to technical staff needed for formatting variables, constructing databases, implementing data analyses on various software platforms, and archiving project data on secure file servers. We anticipate that several projects will demand development of new methodology, and the faculty configuration of the core is designed to respond to those demands and indeed to generate synergy in methods development. Core operations and resources will be overseen by Dr Joseph Hogan, Professor of Biostatistics at Brown University. Dr Hogan currently co-directs the Outcomes & Biostatistics Core for the Lifespan/Tufts/Brown CFAR, and is an expert on statistical methods for HIV and behavioral science. Core faculty include Dr Hernando Ombao, Associate Professor of Biostatistics at Brown and Tao Liu, Assistant Professor of Biostatistics at Brown. Dr Ombao is an authority on the analysis of brain imaging data, and Dr Liu is experienced in analysis of longitudinal data, causal inference, and statistical methods for HIV. The core will