South Texas Alzheimer's Disease Center Data and Statistical Management Core

数据管理和统计核心(DMSc)将支持南得克萨斯州阿尔茨海默病中心 (堆叠)一个综合分析信息系统。在这次重新提交中，它有两个核心领导，一个主席 具有多站点协作经验的临床信息学，Meredith Zozus博士和生物统计学家Chen 王平，博士。它包括临床信息学家、生物统计学家、遗传统计学家、 生物信息学家，机器学习专家。DMSc将提供全面的专业知识、基础设施和 数据收集、使用、分析和共享服务。它提供了STAC与STAC之间的信息高速公路 核心通过先进的基础设施进行日常数据获取和数据的实时交换和使用。 这是通过分散的数据收集、集中的数据集成和存储以及 跨站点和STAC核心分散使用数据。这条信息高速公路建立在信息学数据的基础上 交流和获取系统(IDEAS)平台，支持多样化的多中心研究已有16年 并且与STAC核心使用的系统集成，例如由所有核心使用的RedCap，尤其是临床， 推广和人口神经科学核心(CC、OREC和PNC)、Freezerworks-由Biomarker和 神经病理核心(BC和NPC)和可扩展神经成像档案工具包(XNAT)-由管理 成像核心(IC)。Ideas是专门为整合来自多个来源的不同数据而设计的， 收集点，包括与多组学平台(GMC)保持联系。尖端信息学 支持与电子健康记录(EHR)进行数据交换，例如提取数据和 协助向消委会和全国人大转介。因此，在一个核心中收集的信息在以下情况下可供另一个核心使用 需要并将在整个STAC中使用，以触发控制数据质量和流程保真度的自动化流程 穿过STAC。统计学方法将利用广泛的教师在异质性表征方面的专业知识， 结果预测、生物信息学和多组学方法。DMSC将支持PNC协调一致 来自高价值拉美裔人口研究的数据，并将通过精选数据、样本跟踪、 维护和更新数据字典，以支持向NACC、NCRAD提交数据、图像或样本 和NIGADS。DMSc的目标是：目标1：利用数据优化关键的STAC过程：提供 高级系统连接、自动化和决策支持，以优化和调整对招聘的关注， 数据和样本收集的保留性和完整性。目标2：收集和共享质量数据：应用 跨核心和项目的严格收集和管理方法，以确保数据满足NAccess 要求和支持研究结论和重用。目标3：提供最先进的统计方法 支持：支持STAC核心和项目，在研究设计、统计监测、数据方面拥有深厚的专业知识 分析和结果报告。目标4：培养下一代精通数据的ADRD研究人员： 为STAC学员和调查人员提供ADRD相关信息学和数据科学方面的培训和指导。

The Data Management and Statistics Core (DMSC) will support the South Texas Alzheimer disease Center (STAC) with an integrated analytic information system. It has two core leads in this resubmission, a Chair of Clinical Informatics with experience in multisite collaborations, Meredith Zozus, PhD and a biostatistician, Chen Pin Wang, PhD. It includes faculty and staff who are clinical informaticians, biostatisticians, genetic statisticians, bioinformaticians, machine learning experts. The DMSC will provide comprehensive expertise, infrastructure and services for data collection, use, analysis and sharing. It provides an information highway between the STAC Cores through advanced infrastructure for daily acquisition of data and real-time exchange and use of that data. This is accomplished through decentralized data collection, centralized data integration and storage, and decentralized data use across sites and STAC cores. This information highway is built on the Informatics Data Exchange and Acquisition System (IDEAS) platform that has supported diverse multicenter studies for 16 years and is integrated with systems in use by STAC cores such as REDCap - used by all cores, especially the clinical, outreach and population neuroscience cores (CC, OREC and PNC), Freezerworks - used by the Biomarker and Neuropathology Cores (BC and NPC) and the Extensible Neuroimaging Archive Toolkit (XNAT) - managed by the imaging core (IC). IDEAS was specifically designed to integrate disparate data from multiple sources, collection points, including maintaining associations with multi-omics platforms (GMC). Cutting-edge informatics support extends to data exchange with Electronic Health Records (EHRs) such as extraction of data and facilitating referrals to the CC and NPC. Thus, information collected in one core is available to another when needed and will be used across STAC to trigger automated processes controlling data quality and process fidelity across STAC. Statistical methods will leverage extensive faculty expertise in heterogeneity characterization, outcome prediction, bioinformatics and multi-omics approaches. DMSC will support the PNC in harmonization of data from high-value Hispanic population studies, and will support STAC with curated data, sample tracking, maintaining and updating data dictionaries to support submission of data, images or samples to NACC, NCRAD and NIGADS. The aims of the DMSC are: AIM 1: Leverage Data to Optimize Key STAC Processes: Provide advanced system connectivity, automation and decision support to optimize and align focus on recruitment, retention and completeness of data and sample collection. AIM 2: Collect and Share Quality Data: Apply rigorous collection and management methods across cores and projects to ensure that data meet NACC requirements and support study conclusions and reuse. AIM 3: Provide State-of-the-Art Statistical Methods and Support: Support STAC Cores and projects with deep expertise in study design, statistical monitoring, data analyses, and result reporting. AIM 4: Educate the Next Generation of Data Savvy ADRD Researchers: Provide training and mentoring in ADRD relevant informatics and data science for STAC trainees, investigators.