Statistics Core

统计核心

• 批准号: 10333616
• 负责人: Cuiling Wang
• 金额: $ 66.03万
• 依托单位: ALBERT EINSTEIN COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 1982
• 资助国家: 美国
• 起止时间: 1982-09-29 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10333616
• 关键词: Address; Aging; Air; Air Pollution; Alzheimer' s Disease; Alzheimer' s disease related dementia; Behavior; Behavioral; Biological Aging; Biological Markers; Biology; Blood Vessels; Clinical assessments; Cognition; Cognitive; Communities; Complex; Consultations; Data; Data Analyses; Development; Ecological momentary assessment; Elderly; Ensure; Environmental Risk Factor; Evaluation; Generations; Goals; Growth; Health; Health behavior; Impaired cognition; Individual; Inflammation; Interview; Joints; Leadership; Link; Longitudinal Studies; Manuscripts; Measurement; Measures; Mediation; Mental Depression; Methodology; Methods; Modeling; Monitor; Natural History; Outcome; Pathway interactions; Patient Self-Report; Persons; Physical activity; Play; Polysomnography; Positioning Attribute; Process; Recording of previous events; Research; Research Design; Research Personnel; Resources; Role; Services; Sleep; Statistical Methods; Surveys; Technology; Testing; Time; Variant; Work; actigraphy; biopsychosocial; biopsychosocial factor; blood glucose regulation; cognitive performance; cognitive testing; data management; data quality; data streams; experience; glucose monitor; handheld mobile device; innovation; member; method development; model building; multilevel analysis; power analysis; programs; psychosocial; racial diversity; sedentary lifestyle; statistics; success; vascular factor

ABSTRACT The Statistics Core (SC) serves as the statistical support hub of the Einstein Aging Study (EAS). A primary scientific goal of this Core is to advance our understanding of how individual in-depth characterization of health behaviors, biomarkers, physical and psychosocial health, and environmental factors impact cognitive performance and decline prior to onset of Alzheimer’s disease and related dementias and assessing the role of multiple exposures that influence cognition on a short-term and long-term basis. To achieve this goal, the EAS collects data across multiple timescales ranging from real-time to annual assessments, combining conventional in-person interviews, self-reports and clinical assessments with data streams from wearables (e.g., actigraphy for measurement of sleep and physical activity, air quality monitors, continuous glucose monitors – CGM) and collected via mobile devices (e.g., ecological momentary assessment (EMA) surveys and ambulatory cognitive assessments). The SC will continue in its long history of developing and applying cutting-edge analytic approaches to address the research questions described in the four scientific Projects and to assist in service to the other Cores. It serves two functions that are essential for the success of EAS. First, the SC will provide collaborative and consultative support to Project investigators for data analyses and interpretation of results. Second, the SC will engage in methodological development to meet the challenges posed from complications encountered in analyses and study design. The SC team plays a key role in assisting EAS investigators with all stages of data analysis, such as tailoring analysis plans to specific scientific hypotheses, conducting data analysis, interpreting results, and addressing challenges and potential threats to validity. In particular, the SC will work closely with the Technology and Data Management Core to insure data quality. In short, the SC will continue its long history of contributions to aging research through developing new statistical methodology and through innovative application of existing methodologies.

摘要 统计核心（SC）是爱因斯坦老龄化研究（EAS）的统计支持中心。主 本核心的科学目标是促进我们对个人健康的深入表征的理解 行为，生物标志物，身体和心理健康，以及环境因素影响认知 阿尔茨海默病和相关痴呆症发作前的表现和下降，并评估 短期和长期影响认知的多重暴露。为了实现这一目标， 收集从实时到年度评估的多个时间尺度的数据， 使用可穿戴设备的数据流进行面对面访谈、自我报告和临床评估（例如，活动记录 用于测量睡眠和身体活动、空气质量监测器、连续葡萄糖监测器（CGM）和 经由移动的设备收集（例如，生态瞬时评估（EMA）调查和动态认知 评估）。SC将继续其开发和应用尖端分析技术的悠久历史， 解决四个科学项目中描述的研究问题的方法，并协助服务 其他核心它有两个对EAS的成功至关重要的功能。首先，SC将提供 为项目调查人员提供数据分析和结果解释方面的协作和咨询支持。 第二，常设委员会将参与方法的发展，以应对复杂情况带来的挑战 在分析和研究设计中遇到的问题。SC团队在协助EAS调查人员进行所有 数据分析的各个阶段，例如根据具体的科学假设制定分析计划， 分析，解释结果，并解决挑战和潜在的威胁有效性。特别是，SC 将与技术和数据管理核心密切合作，以确保数据质量。简而言之，SC将 通过开发新的统计方法，继续其对老龄化研究的长期贡献， 通过创新应用现有方法。