Data Management and Biostatistical Analysis Core

数据管理和生物统计分析核心

• 批准号: 10454923
• 负责人: DAVID V. GLIDDEN
• 金额: $ 8.66万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-13 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10454923
• 关键词: AIDS related cancer; Africa; Africa South of the Sahara; African; Ally; Award; Biological; Biomedical Research; Biometry; Budgets; Caring; Clinical Research; Data; Data Analytics; Data Collection; Development; Discipline; Doctor of Philosophy; Early Diagnosis; Ensure; Epidemiology; Faculty; Foundations; Frequencies; Funding; Future; Goals; HIV Infections; Human Resources; Image; Individual; Instruction; Kenya; Malignant Neoplasms; Master of Public Health; Medical Records; Methodology; Methods; Participant; Periodicity; Pilot Projects; Prevention; Principal Investigator; Process; Questionnaires; Research; Research Personnel; Research Project Grants; Resource Sharing; Resource-limited setting; Resources; Rest; Scientist; Seasons; Solid; Specialist; Specimen; Statistical Data Interpretation; Tanzania; Techniques; Technology; Training; Training Programs; Translational Research; Uganda; United States; career; career development; cost; data management; design; epidemiology study; experience; field study; novel strategies; prospective; repaired; skills; success; systems research; virus related cancer

Data management and biostatistical analysis are critical to the success of clinical, epidemiologic and translational research. Despite this, these two disciplines are frequently overlooked and under-resourced during research planning and execution. Sub-optimal data collection and management approaches can generate missing and inaccurate data; researchers may be unaware of such issues in their data, which may be irreparable or require considerable resources to retrospectively repair. Deficiencies in biostatistical analysis are common and are also frequently unrecognized by non-experts. In resource-limited settings such as East Africa, research is even more susceptible to data management and biostatical analysis problems due to the absence of well-trained personnel and appropriate funding for high-level data management and biostatistical analysis. The result is a biomedical research system that often relies upon sub-optimal data management and analytic approaches performed by individuals with less than optimal training. Our goal is to transfer relevant technology and approaches to East Africa by making these available to our Research Projects and investigators within one efficient shared resource core. The Data Management and Biostatistical Analysis Core (DMBAC) of United States-East Africa HIV-Associated Malignancy Research Center (USEAHAMRC) will therefore have the following specific aims: Aim 1. Provide expertise and a common platform for primary data collection and data management as well as specialized instruction to our junior scientists. Aim 2. Offer project-specific biostatistical support both during the planning of new research and during the analysis of ongoing studies as well as specialized instruction to our junior scientists. We will leverage the expertise in data management and biostatistical analysis built at the Department of Epidemiology and Biostatistics at UCSF over the past two decades — and particularly during U54 CA190153 — and use it to support our two Research Projects and Pilot Projects. The core will also provide focused high-level didactic instruction in data management and biostatistical analysis through the Career Development Seminar organized by the Developmental Core. This will allow our emerging African principal investigators to incorporate best practices into their current and future studies, communicate effectively with data managers and biostatisticians, and keep abreast of developments in technology and methodology. By offering assistance from faculty-level biostatisticians who are familiar with the substantive aspects of HIV infection and HIV-related malignancies, we will ensure that the USEAHAMRC meets these objectives.

数据管理和生物统计分析对于临床、流行病学和 翻译研究尽管如此，这两个学科经常被忽视，资源不足 在研究计划和执行过程中。次优的数据收集和管理方法可能 产生缺失和不准确的数据;研究人员可能不知道他们的数据中存在这些问题， 无法修复或需要大量资源进行追溯性修复。生物统计学缺陷 分析是常见的，也经常被非专家所识别。在资源有限的情况下 例如东非，研究更容易受到数据管理和生物统计分析影响 由于缺乏训练有素的人员和高水平数据的适当资金而造成的问题 管理和生物统计分析。其结果是，生物医学研究系统往往依赖于 次优的数据管理和分析方法，由个人执行， 训练我们的目标是通过提供相关技术和方法， 我们的研究项目和调查人员在一个有效的共享资源核心。数据 美国-东非艾滋病毒相关的管理和生物统计分析核心（DMBAC） 因此，研究中心（USEAHAMRC）将有以下具体目标： 目标1。为主要数据收集和数据提供专业知识和通用平台 管理以及专门指导我们的年轻科学家。 目标2.在新研究的规划过程中提供特定项目的生物统计支持， 在分析正在进行的研究以及专门指导我们的初级科学家。 我们将利用在数据管理和生物统计分析建立在部门的专业知识， 流行病学和生物统计学在UCSF在过去的二十年-特别是在U 54 CA 190153-并使用它来支持我们的两个研究项目和试点项目。核心还将提供 通过职业生涯，专注于数据管理和生物统计分析的高水平教学指导 发展中心举办的发展研讨会。这将使我们新兴的非洲校长 研究人员将最佳做法纳入其当前和未来的研究， 数据管理员和生物统计员，并跟上技术和方法的发展。通过 由熟悉艾滋病毒实质性方面的学院级生物统计学家提供帮助 感染和艾滋病毒相关的恶性肿瘤，我们将确保USEAHAMRC满足这些目标。