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: 10282004
• 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/10282004
• 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; Structure; 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：摘要/抽象