Core C: Data Management and Statistics Core

核心C：数据管理和统计核心

• 批准号: 10647906
• 负责人: Katherine P Rankin
• 金额: $ 45.48万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10647906
• 关键词: Acceleration; Address; Aging; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Architecture; Back; Basic Science; Biological Markers; Biometry; Brain; Clinical; Clinical Research; Collection; Complex; Consultations; Creativeness; Custom; Data; Data Analyses; Data Analytics; Data Collection; Data Management Resources; Data Set; Dementia; Diagnosis; Differential Diagnosis; Disease; Early Diagnosis; Education; Ensure; Epidemiology; Etiology; Faculty; Foundations; Genetic; Genotype; Goals; Heterogeneity; Human Resources; Image; Infrastructure; Institution; Investigation; Leadership; Link; Mediating; Memory; Modeling; Molecular; Natural History; Nature; Neuroanatomy; Neurologic; Neurology; Patients; Pattern; Phenotype; Positioning Attribute; Process; Quality Control; Recommendation; Recording of previous events; Research; Research Design; Research Methodology; Research Personnel; Resource Sharing; Resources; Signs and Symptoms; Solid; Source; Statistical Data Interpretation; Statistical Methods; Statistical Models; Structure; Support System; Syndrome; System; Techniques; Technology; Translational Research; United States National Institutes of Health; Visualization; analytical method; complex data; data integration; data management; data resource; data submission; design; education research; experience; genetic signature; healthy aging; heterogenous data; improved; innovation; insight; longitudinal analysis; mild cognitive impairment; multidisciplinary; neurobehavioral; neuropathology; nonalzheimer dementia; novel strategies; open source; predictive modeling; programs; proteomic signature; quality assurance; statistics; support tools; tool

ABSTRACT This ADRC has comprehensive and demanding translational research objectives, for which the Data Management and Statistical (DMS) Core must be able to efficiently collect, validate, integrate, and derive meaning from a complex array of data. Our key questions about diagnosis, clinical features, natural history, interactions between genotype and phenotype, biomarkers, clinico-pathological relationships, and connections between brain changes and neurobehavioral disturbances can only be addressed with sophisticated and comprehensive data structures, and with the support of a cohesive team of expert statistical faculty. To facilitate this process, the DMS Core will incorporate a number of innovative tools that will enable our researchers to better identify and integrate high-quality multilevel data collected through this ADRC, while linking data with other resources at UCSF and beyond. Historically, the UCSF Memory and Aging Center (MAC) has shown national leadership in developing neurology-specific data management and analytic systems that integrate a wealth of information from a variety of sources in order to better understand the etiology and treatment of dementia syndromes and promote translational research. This DMS Core would be uniquely positioned to make a national impact on key NIH goals by making tools available to the ADRC network that are designed to facilitate and accelerate the research process for investigators. Finally, we have assembled a superb team of faculty from the Epidemiology and Biostatistics Department who will provide high quality analytic consultation and education for our investigators. Overall, this DMS Core will combine our center’s solid two-decade foundation of excellent data management and UDS submission practices, with tools enhancing representation and analysis of multiple levels of neurological information, as well as experienced statistical guidance that allows investigators to effectively model complex patterns and associations in our patients. We have structured this core ensure that these exemplary data management and research methods are effectively coordinated among the multidisciplinary cores of this ADRC, freeing investigators to pursue the answers that will most powerfully impact dementia research and treatment.

摘要 该ADRC具有全面和苛刻的转化研究目标，数据 管理和统计（DMS）核心必须能够有效地收集、验证、集成和导出 这意味着从一个复杂的数据数组。我们的关键问题是诊断，临床特征，自然史， 基因型和表型之间的相互作用，生物标志物，临床病理关系，以及连接 大脑变化和神经行为紊乱之间的联系， 全面的数据结构，并在一个有凝聚力的专家统计学院团队的支持下。到 为了促进这一进程，DMS核心将包括一些创新工具，使我们的 研究人员可以更好地识别和整合通过ADRC收集的高质量多级数据， 将数据与加州大学旧金山分校及其他地方的其他资源联系起来。加州大学旧金山分校记忆与衰老中心 (MAC)在开发神经病学特定数据管理和分析系统方面表现出国家领导力 它整合了来自各种来源的丰富信息，以便更好地了解病因， 治疗痴呆综合征和促进转化研究。这个DMS核心将是独一无二的 通过向ADRC网络提供工具，能够对NIH的关键目标产生全国性的影响， 旨在促进和加快研究人员的研究过程。最后，我们组装了一个 来自流行病学和生物统计学系的优秀教师团队将提供高质量的 为我们的研究人员提供分析咨询和教育。总的来说，这个DMS核心将联合收割机结合我们中心的固体 卓越的数据管理和UDS提交实践的二十年基础，工具增强 神经学信息的多层次表示和分析，以及经验丰富的统计 指导，使研究人员能够有效地模拟我们患者的复杂模式和关联。我们 我构建了这个核心，确保这些示范性的数据管理和研究方法有效地 在这个ADRC的多学科核心之间进行协调，使调查人员能够寻求答案， 将对痴呆症的研究和治疗产生最大的影响。