Statistical Core/Data Management

统计/数据管理核心（SDMC）作为EAS的分析“中心”，并提供 整合核心、项目和试点研究收集的广泛数据。它有两个功能， 是爱因斯坦衰老研究（EAS）成功的关键。首先，SDMC通过以下方式最大限度地提高数据质量： 实施数据库系统，整合和管理从行政，临床， 神经病理学和神经影像学核心，以及三个项目中的每一个。此外，它还负责 确保研究中心之间以及参与者生态瞬时评估设备之间的数据传输安全。的 SDMC负责质量控制程序以及合并项目和核心数据。 第二，统计核心向项目调查人员提供有关事项的协作和咨询支持， 研究设计、数据分析和结果解释。统计核心负责开发， 实施和解释适合具体研究问题和假设的统计方法， 它定期与项目研究人员合作撰写科学手稿。 统计/数据管理核心的具体目标是： 目标1.实施和监督数据程序，以促进数据和想法的无缝交换 在核心和项目之间，并促进数据传输，以便与EAS以外的研究者合作。 目标2.为假设检验、模型构建和 跨测量构造的结果和分析（例如，风险、机制、结果），并 与研究人员合作，制定和测试假设，并提供专业知识， 设计和进行分析。 目标3.制定新的统计方法，并以创新的方式应用现有方法，以帮助 实现本核心和项目的其他目标，并进一步开展总体老龄化研究，重点是 联合收割机结合非卧床和传统标记物来识别早期认知障碍和疾病的方法。

The Statistical/Data Management Core (SDMC) serves as the analytic “hub” of the EAS, and provides integration of the wide range of data collected by Cores, Projects, and pilot studies. It serves two functions that are essential for the success of the Einstein Aging Study (EAS). First, the SDMC maximizes data quality by implementing database systems that integrate and manage the data collected from the Administrative, Clinical, Neuropathology and Neuroimaging Cores, and from each of the three projects. In addition, it is responsible for secure data transfer across study sites and to/from participant ecological momentary assessment devices. The SDMC assumes responsibility for quality control procedures and for merging data across Projects and Cores. Second, the Statistical Core provides collaborative and consultative support to Project investigators on matters of study design, data analyses and interpretation of results. The Statistical Core is responsible for developing, implementing and interpreting statistical methods appropriate to specific research questions and hypotheses, and it collaborates regularly with Project investigators on scientific manuscripts. Specific Aims of the Statistical/Data Management Core are: Aim 1. To implement and oversee data procedures to facilitate the seamless exchange of data and ideas among Cores and Projects, and to facilitate data transfer for collaborations with investigators outside the EAS. Aim 2. To provide a general analytic framework for hypothesis testing, model building, and integration of results and analyses across measurement constructs (e.g., exposures, mechanisms, outcomes), and to collaborate with investigators regarding the framing and testing of hypotheses and to provide expertise in the design and conduct of analyses. Aim 3. To develop new statistical methodology and to apply existing methodology in innovative ways to help to fulfill the other aims of this Core and the Projects and to further aging research in general, with the emphasis of methods to combine ambulatory and traditional markers to identify early cognitive impairment and disease.