Core D: Biostatistics and Data Management Core

核心 D：生物统计学和数据管理核心

基本信息

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
项目类别：
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

项目摘要

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) 尤德尔中心生物统计和数据管理核心 (BDMC) 提供了重要的积极为 Udall 中心研究人员提供生物统计支持、数据管理和分析。参与并为国家 Udall 计划和更广泛的帕金森病 (PD) 研究做出贡献核心目标 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和分析方法进行统计分析目标 2 是提供先进的方法、结果解释和研究成果的有效传播。该中心的方法、分析能力和专业知识，特别是综合多模式分析我们为学生、研究员提供培训材料和教育机会。初级教师、研究调查员和社区 PD 倡导者。有助于分析和共享结果数据的词汇表，NINDS 帕金森病常见病使用数据元素 (CDE) 需要使用 NINDS 数据管理资源 (DMR) 存储库。全局唯一标识符 (GUID)，可促进数据聚合，而无需个人公开/传输识别信息标准化符合 NINDS 帕金森病生物标志物计划。用于存储和访问的 (PDBP) 协议将使用 U-M Udall Center CDE 数据输入 PDBP DMR。 NINDS ProForms 将促进数据聚合和与更广泛的社区共享。该中心网站将输入符合 21 CFR Part 11 的基于网络的关系数据库 OpenClinica。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协议。管理核心将增强密歇根大学尤德尔中心的研究、计算和分析能力，促进当地与帕金森病相关的研究，并为国家贡献独特的临床、生物标志物和影像数据供广泛社区使用的存储库尤其需要支持基于图像的项目。跨扫描仪（PET 和 MRI）和站点（Udall 与格罗宁根）的数据集成和协调，扫描（例如，[18F]FEOBV、[11C]DTBZ、[18F]Fdopa、T1 解剖、静息态 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、白质事项完整性）和时间（基线加上后续访问）。利用这个多方面数据集提供的多样性来识别与重要的有意义的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研究上。针对单个感兴趣的大脑区域，但可以扩展到涵盖已知的网络更广泛地涉及认知和步态相关障碍。