DISCOVERY - Statistics and Analysis Core

摘要(统计核心) 由Gottesman博士和Wruck博士领导的统计核心将提供关键的分析计划和关键的完成 分析、权力和样本量计算，以及认知评估和认知结果的设计 评估。此核心将支持Discovery Network应用程序和设计中的相关核心 这项研究以及作为整体研究设计的关键组成部分的详细认知测量， 以及对最终研究结果的最终评估和交流。这一核心将促进 研究目的，对于其认知评估和分类，它将使用以前在 社区动脉粥样硬化风险(ARIC)研究，其中核心PIs Gottesman博士和Wruck博士 以前合作过的，并且在长期认知评估中与差异人群相关的 轨迹和结果。除了标准的考虑方法外，痴呆症的时间和纵向 认知轨迹分析，这一核心将提出预测建模的新方法，包括使用 机器学习和深度学习技术。Discovery统计核心将：1)开发和实施 发现网络临床站点的可行神经认知电池，重复测量超过 至少两年的随访期；2)进行认知事件的判断，包括MCI和痴呆症，使用 目标1中描述的神经认知组，并使用 在同时接受更短和更全面电池的参与者中，电池更短；3)贡献 为探索项目的设计和运营提供统计专业知识。这将包括功率计算 和抽样计划，以确保对总体差异人群和卒中亚型的最佳表示 队列和子研究，研究数据的半年度质量控制检查，质量保证计划 神经认知电池，以及OSMB报告的统计输入；4)制定分析策略并执行 对发现项目的具体目标和子目标进行统计分析。统计核心调查员 将提供分析事件发生时间终点(痴呆症，MCI)的方法专业知识；认知变化 分析；特定领域的性能和变化分析；以及新的机器学习方法 PSCID的预测模型，结合了神经影像、生物标记物、组学和认知纵向 测量。通过这种核心、最先进的认知测量方法和统计分析 方法学将允许对这一拟议的中风患者队列中的认知结果进行公正的考虑。

ABSTRACT (STATISTICS CORE) The Statistical Core, led by Drs. Gottesman and Wruck, will provide key analytic plans and completion of key analyses, power and sample size calculations, and design of the cognitive evaluation and cognitive outcome assessment. This core will support the DISCOVERY Network application and associated cores in the design of the study and also for the detailed cognitive measurement that is a key component of the overall study design, as well as in the ultimate evaluation and communication of the final study results. This Core will facilitate the study aims, and for its cognitive assessments and classification, it will use methods previously used in the Atherosclerosis Risk in Communities (ARIC) study, where the Core PIs Drs. Gottesman and Wruck have previously collaborated, and which are relevant to disparity populations in the assessment of long-term cognitive trajectories and outcomes. In addition to standard methods to consider, time-to-dementia and longitudinal cognitive trajectory analyses, this Core will propose novel methods for predictive modeling, including the use of machine learning and deep learning techniques. The DISCOVERY Statistics Core will: 1) develop and implement a feasible neurocognitive battery at the DISCOVERY Network clinical sites, with repeated measurements over at least a 2-year follow-up period; 2) conduct adjudication of cognitive events, including MCI and dementia, using the neurocognitive battery described in Aim 1, and to perform validation of dementia classification using the shorter battery in those participants undergoing both the shorter and more comprehensive battery; 3) contribute statistical expertise to the design and operation of the DISCOVERY project. This will include power calculations and sampling plans to ensure optimal representation of disparity populations and stroke subtypes for the overall cohort and for sub-studies, semi-annual quality control checks of study data, a quality assurance plan for the neurocognitive battery, and statistical input on OSMB reports; 4) develop an analytic strategy and perform statistical analyses for the specific aims and sub-aims of the DISCOVERY project. Statistics core investigators will provide methodological expertise in analysis of time-to-event endpoints (dementia, MCI); cognitive change analyses; domain-specific performance and change analyses; and novel machine learning approaches to predictive modeling of PSCID, incorporating neuroimaging, biomarker, “omics” and cognitive longitudinal measurements. Through this Core, state-of-the-art cognitive measurement methods and statistical analysis methodologies will allow unbiased consideration of cognitive outcomes in this proposed cohort of stroke patients.