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Core G: Biostatistics and Data Management

Core G: Biostatistics and Data Management
核心 G:生物统计学和数据管理
批准号:
10454258
负责人:
Dana L Tudorascu
金额:
$194.74万
依托单位国家:
美国
项目类别:
财政年份:
2020
资助国家:
美国
项目状态:
未结题
起止时间:
2020-09-30 至 2025-08-31

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中文摘要
翻译
生物统计学和数据管理核心摘要 生物统计和数据管理(BDM)核心促进了与ABC-DS的研究和合作 研究者通过提供数据管理、流行病学、分析设计和统计支持, 建立与研究界共享数据的基础设施,以进一步了解 老年痴呆症和唐氏综合症。BDM核心人员,由Tudorascu博士(统计学)领导, 安德鲁斯(数据管理)将支持ABC-DS核心领导人和研究者履行具体的 研究项目1、2和3的目标是建立一个安全、集中的在线数据管理系统, 实施一项协议,以确保临床、神经影像学、组学、遗传学 和神经病理学数据,并提供高水平的统计支持,与专业知识,需要测试每个陈述 这个项目的假设。这些职能将通过生物统计学的多学科小组执行。 方法学小组(BMG):统计学家、流行病学家和数据管理专家组成 团队,每个团队都有特定的专业知识和独特的技能,可以在多个项目和核心中使用。的 利用这一组织结构将实现的具体目标如下:目标1:完成 通过协调和整合,过渡到统一的关系数据库和项目管理系统 所有性能站点的评估数据,纳入新的统一评估协议,并提供 用于安排和记录所有研究程序并整合来自 临床、神经病理学、组学、ADDORE和神经成像核心;目标2:创建一组分析数据文件 包含所需统计分析所需的所有来源的所有原始和派生数据元素 测试本提案中每个研究项目的假设,并创建符合以下要求的数据集: 本研究实施期间计划的任何次要和特别分析的质量标准;目标3:铅 统计数据分析,合作编写所有核心和研究项目的报告,并提供 ABC-DS研究项目统计分析的设计和方法学方面的专业知识;目标4: 开发和实施适当的统计模型,用于对所有来源的生物标志物进行纵向分析。 模式,以便在我们的AD发展的每个阶段进行影响推断 DS成人队列;目标5:通过神经影像学实验室促进ABC-DS数据的共享 (LONI),作为ABC-DS内外有兴趣使用 用于分析的ABC-DS数据库,并提供文件,确保已发表的ABC- DS研究。
英文摘要
Biostatistics and Data Management Core Abstract The Biostatistics and Data Management (BDM) Core facilitates research and collaboration with ABC-DS investigators by providing data management, epidemiological, analytic design and statistical support, and by creating an infrastructure for data sharing with the research community to further the understanding of Alzheimer’s Disease and Down syndrome. BDM Core personnel, lead by Drs. Tudorascu (statistics) and Andrews (data management) will support the ABC-DS Cores leaders and investigators in fulfilling the specific aims of Research Projects 1, 2 and 3 by establishing a secure, centralized online data management system, implementing a protocol to ensure the integration and documentation of clinical, neuroimaging, omics, genetic and neuropathology data and providing high-level statistical support, with expertise required to test each stated hypothesis in this project. These functions will be carried out through a multidisciplinary team of Biostatistics Methodology Groups (BMGs): statisticians, epidemiologists, and data management experts organized into groups, each with specific expertise and unique skills that can be used across several Projects and Cores. The Specific Aims that will be implemented using this organizational structure are as follows: Aim 1: Complete the transition to a unified relational database and project management system by harmonizing and integrating evaluation data across all Performance sites, incorporating the new, unified evaluation protocol, and providing functionality for scheduling and documenting all study procedures and integrating key data elements from the Clinical, Neuropathology, Omics, ADDORE and Neuroimaging Core; Aim 2: Create a set of analytic data files containing all original and derived data elements from all sources necessary for the statistical analysis required to test the hypotheses of each Research Project in this proposal, as well as create data sets meeting specifications for any secondary and ad hoc analyses planned during the conduct of this research; Aim 3: Lead the statistical data analyses, collaborate in report preparation for all Cores and Research Projects, and provide expertise on design and methodological aspects of statistical analyses for ABC-DS Research Projects; Aim 4: Develop and implement appropriate statistical models for longitudinal analyses of derived biomarkers from all modalities in order to make inferences with respect to impact at each stage of the development of AD in our cohort of adults with DS; Aim 5: Facilitate sharing of ABC-DS data through the Laboratory of Neuroimaging (LONI), serve as a methodological resource for researchers within and outside of ABC-DS interested in using the ABC-DS database for analyses, and provide documentation and ensure reproducibility of published ABC- DS research.
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Core G: Biostatistics and Data Management
Core G: Biostatistics and Data Management
Core G: Biostatistics and Data Management
Statistical methods to improve reproducibility and reduce technical variability in heterogeneous multimodal neuroimaging studies of Alzheimer’s Disease
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