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：总结/摘要 密歇根大学（U-M）Udall中心生物统计学和数据管理核心（BDMC）提供了重要的 生物统计支持，数据管理，并为尤德尔中心分析。核心研究人员积极 参与国家Udall计划和更广泛的帕金森病（PD）研究并做出贡献 社区核心的目标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，使用现代的统计分析，最先进的统计模型和分析 方法、结果解释和有效传播研究成果。目标2是提供先进的 方法，分析能力和中心的专业知识，特别是综合多模态分析， 异构Udall PD数据。我们为学生，研究员， 初级教师，研究调查员和社区PD倡导者。为确保格式统一， 有助于分析和共享结果数据的词汇表，NINDS帕金森病常见 使用数据元素（CDE）。NINDS数据管理资源（DMR）存储库需要使用 全局唯一标识符（GUID），可促进数据聚合，而无需公开/传输个人数据 识别信息。数据标准化符合NINDS帕金森病生物标志物计划 （PDBP）用于存储和访问的协议。U-M Udall中心CDE数据将使用以下方法输入PDBP DMR NINDS ProForms，以促进数据汇总和与更广泛的社区共享。非CDE数据将 输入符合21 CFR Part 11的基于网络的关系数据库OpenClinica。该中心网站将 提供直接链接以访问汇总统计数据（数据仪表板），管理社区对样本的请求 和数据，并传播研究成果和计算协议。生物统计学和数据 管理核心将提高U-M Udall中心的研究、计算和分析能力， 促进当地PD相关研究，并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， 整合和协调扫描仪（PET和MRI）和研究中心（Udall vs Groningen）的数据， 扫描（例如，[18 F]FEOBV、[11 C]DTBZ、[18 F]Fdopa、T1解剖学、静息状态fMRI、扩散张量成像）和 其随后的模态（例如，灰质密度、皮质厚度、功能和结构连接、白色- 事件完整性）和时间（基线加随访访视）。该中心将部署一些分析技术， 利用这一多方面数据集提供的多样性， 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 目标是感兴趣的单个大脑区域，但可以扩展到包括已知的网络， 更广泛地涉及认知和步态相关疾病。