Core D: Biostatistics and Data Management Core

核心 D：生物统计学和数据管理核心

• 批准号: 10493257
• 负责人: Catherine A Spino
• 金额: $ 16.73万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10493257
• 关键词: Adherence; Advocate; Albinism; Anatomy; Attention; Biological Markers; Biometry; Brain imaging; Brain region; Clinical; Clinical Research; Cognition; Cognitive; Collaborations; Common Data Element; Communities; Computer Analysis; Computer software; Computing Methodologies; Data; Data Aggregation; Data Analytics; Data Collection; Data Element; Data Files; Data Management Resources; Data Set; Data Storage and Retrieval; Databases; Development; Diffusion Magnetic Resonance Imaging; Disease; Disease Progression; Ensure; Evolution; Experimental Designs; Functional Magnetic Resonance Imaging; Functional disorder; Gait; Goals; Health; Human; Image; Image Analysis; Individual; Investigation; Knowledge; Link; Magnetic Resonance Imaging; Maintenance; Medical Imaging; Metadata; Methods; Michigan; Modernization; Motor; National Institute of Neurological Disorders and Stroke; Online Systems; Parkinson Disease; Patients; Phase; Planning Techniques; Positron-Emission Tomography; Process; Protocols documentation; Recommendation; Research; Research Personnel; Research Project Grants; Resources; Rest; Sampling; Scanning; Site; Standardization; Statistical Data Interpretation; Statistical Methods; Statistical Models; Students; System; Techniques; Thick; Time; Training; Translating; Universities; Visit; Visualization; Vocabulary; Work; analytical method; base; catalyst; cholinergic; clinical biomarkers; dashboard; data de-identification; data harmonization; data infrastructure; data integrity; data management; data sharing; data standards; data structure; data submission; density; design; electronic data; experience; experimental study; faculty research; falls; follow-up; gray matter; heterogenous data; high standard; image processing; improved; interest; multimodality; multisensory; neuroimaging; programs; relational database; repository; research and development; statistics; success; web site; white matter

CORE D: SUMMARY/ABSTRACT The University of Michigan (U-M) Udall Center Biostatistics and Data Management Core (BDMC) provides vital biostatistical support, data management, and analytics for the Udall Center. Core investigators actively participate and contribute to the national Udall program and the wider Parkinson’s disease (PD) research community. Aim 1 of the Core is to provide biostatistical and data management support of the Center projects, including design of experiments, statistical analyses using modern, state-of-the-art statistical models and analytic methods, result interpretation, and effective dissemination of research findings. Aim 2 is to provide advanced methods, analytical capabilities, and expertise in the Center, particularly for integrative multi-modal analyses for the heterogeneous Udall PD data. We offer training materials and educational opportunities for students, fellows, junior faculty, and research investigators, and community PD advocates. To ensure uniform formats and vocabularies that facilitate analyses and sharing of resulting data, the NINDS Parkinson’s Disease Common Data Elements (CDEs) are used. The NINDS Data Management Resource (DMR) repository requires use of Global Unique Identifiers (GUIDs) that facilitate data aggregation without exposing/transferring Personally Identifying Information. Data standardization complies with NINDS Parkinson's Disease Biomarkers Program (PDBP) protocols for storage and access. U-M Udall Center CDE data will be entered into the PDBP DMR using NINDS ProForms to facilitate data aggregation and sharing with the broader community. Non-CDE data will be entered into the 21 CFR Part 11-compliant web-based relational database OpenClinica. The Center website will provide direct links to access summary statistics (data dashboard), manage community requests for samples and data, and disseminate research findings and computational protocols. The Biostatistics and Data Management Core will enhance the research, computational and analytic capabilities of the U-M Udall Center, facilitate PD-related research locally, and contribute unique clinical, biomarker and imaging data to national repositories for broad community use. Support for the image-based projects, in particular, will require the integration and harmonization of data across scanners (PET and MRI) and sites (Udall versus Groningen), the scans (e.g., [18F]FEOBV, [11C]DTBZ, [18F]Fdopa, T1 anatomical, resting state fMRI, diffusion tensor imaging) and their ensuing modalites (e.g., gray matter density, cortical thickness, functional and structural connectivity, white- matter integrity), and time (baseline plus follow-up visits). The Center will deploy a number of analysis techniques to leverage the diversity provided by this multifaceted data set to identify meaningful associations with important clinical variables involving cognition and gait. Initial emphasis will be placed on hypothesis-driven investigations targeting individual brain regions of interest, but may be expanded to encompass networks known to be implicated more broadly in cognitive and gait-related disorders.

核心D：摘要/摘要 密歇根大学(U-M)尤德尔中心生物统计和数据管理核心(BDMC)提供至关重要的 为Udall中心提供生物统计支持、数据管理和分析。核心调查人员积极参与 参与国家Udall计划和更广泛的帕金森氏病(PD)研究并做出贡献 社区。中心的目标1是为中心项目提供生物统计和数据管理支持， 包括实验设计、使用现代、最先进的统计模型的统计分析和分析 研究方法、结果解释和研究成果的有效传播。目标2是提供先进的 该中心的方法、分析能力和专业知识，特别是在综合多模式分析方面 异类Udall PD数据。我们为学生、研究员提供培训材料和教育机会， 初级教员、研究调查人员和社区PD倡导者。确保统一的格式和 便于分析和共享结果数据的词汇，NINDS帕金森氏病常见 使用数据元素(CDE)。NINDS数据管理资源(DMR)存储库需要使用 全球唯一标识符(GUID)，无需亲自公开/传输即可促进数据聚合 识别信息。数据标准化符合NINDS帕金森氏病生物标志物计划 (PDBP)存储和访问协议。U-M Udall Center CDE数据将使用以下方式输入PDBP DMR NINDS ProForms，便于与更广泛的社区进行数据聚合和共享。非CDE数据将是 加入了符合21 CFR Part 11的基于Web的关系数据库OpenClinica。中心网站将 提供访问摘要统计数据(数据仪表板)的直接链接，管理社区对样本的请求 和数据，并传播研究成果和计算方案。生物统计学与数据 管理核心将增强U-M Udall中心的研究、计算和分析能力， 促进当地与帕金森病相关的研究，为国家提供独特的临床、生物标志物和成像数据 供社区广泛使用的存储库。特别是，对基于图像的项目的支持将需要 跨扫描仪(PET和MRI)和站点(Udall与Groningen)的数据集成和协调， 扫描(例如，[18F]FEOBV、[11C]DTBZ、[18F]FDOPA、T1解剖、静息状态fMRI、扩散张量成像)和 它们随后的模式(例如，灰质密度、皮质厚度、功能和结构连接性、白色- 物质完整性)和时间(基线加上后续访问)。该中心将部署一些分析技术 要利用此多方面数据集提供的多样性来确定与重要数据的有意义关联 涉及认知和步态的临床变量。最初的重点将放在假设驱动的调查上 以感兴趣的个别大脑区域为目标，但可能会扩展到包括已知的 更广泛地牵涉到认知和步态相关的障碍。