Latent variable analysis in cognitive aging executive

认知老化执行者的潜变量分析

• 批准号: 7142300
• 负责人: ROCHELLE E TRACTENBERG
• 金额: $ 12.61万
• 依托单位: GEORGETOWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7142300
• 关键词: Alzheimer' s disease; aging; behavioral /social science research tag; brain disorder diagnosis; clinical research; cognition disorders; data collection methodology /evaluation; data quality /integrity; dementia; developmental psychology; executive function; functional ability; human data; human old age (65+); human subject; mathematical model; model design /development; neuropsychological tests; psychological aspect of aging; psychometrics; short term memory

DESCRIPTION (provided by applicant): The symptom domains of greatest interest in Alzheimer's disease (AD), cognition, function, and behavior, are also associated with executive function (EF). But EF cannot be directly observed; these symptom domains and EF have unknown measurement error in their assessment, and unknown interrelationships. Precise assessment of each area is important for efficient clinical studies of AD interventions and for public health purposes like screening for AD and dementia. Unlike regression analysis, latent variable analysis (LVA) methods can increase precision by explicitly modeling measurement error, and can model constructs (like EF) that are not directly observable. Additional precision will be derived from determining whether EF contributes significant explanatory power to models of cognitive aging and AD. This K01 therefore has two specific aims. Aim 1: Model measurement error in instruments that are common in clinical trials for AD using a Bayesian Network to differentiate measurement error from true change for non-demented elderly, persons with AD, and persons with incipient AD. Aim 2: Construct a statistical model of EF for persons with AD and cognitively normal elderly controls in order to: a. evaluate the latent factor structure in EF; and b. compare fits of latent variable models of EF. The best fitting model will address the hypothesis that EF contributes significant explanatory power for variance in cognitive, functional and behavioral symptoms and will suggest whether this is true for both normal elderly and persons with AD. These aims will contribute to precision and modeling of assessment in normal aging and AD; and to a better understanding of EF as an outcome in aging research and clinical studies of AD. The proposed K01 will develop deep and broad sets of skills in assessment and analysis, to be achieved with training in design and conduct of clinical trials, measurement theory, neuropsychology, and advanced statistical/LVA modeling. Mentoring from an experienced clinical trialist, Paul Aisen, MD, will be supplemented with expert consultation in these domains throughout the five year grant. The PI is a junior investigator in who wishes to extend her expertise in statistics to include LVA methods, and her background in cognitive science to include neuropsychological assessment and measurement. This K01 award will achieve these and so will move the PI closer to her long-term goal of becoming an independent researcher in cognitive aging and AD.

描述（由申请人提供）：对阿尔茨海默氏病（AD），认知，功能和行为最感兴趣的症状领域也与执行功能（EF）有关。但是不能直接观察到EF；这些症状域和EF在评估中具有未知的测量误差和未知的相互关系。 对每个领域的精确评估对于对AD干预措施的有效临床研究和公共卫生目的（例如筛查AD和痴呆症）很重要。与回归分析不同，潜在变量分析（LVA）方法可以通过明确建模测量误差来提高精度，并且可以对无法直接观察到的构造（例如EF）进行建模。通过确定EF是否为认知衰老和AD模型贡献重要的解释能力，将获得其他精度。因此，该K01具有两个具体的目标。 AIM 1：在临床试验中使用贝叶斯网络的AD临床试验中常见的仪器模型测量误差，以区分测量误差与非痴呆的老年人的真实变化，人 与AD和具有初期广告的人。目标2：为具有AD和认知正常的老年人对照人的EF构建统计模型，以：评估EF中的潜在因子结构；和b。比较EF的潜在变量模型的拟合。最佳拟合模型将解决以下假设：EF在认知，功能和行为症状方面贡献了重要的解释能力，并且会暗示这对于正常的老年人和AD患者是否如此。这些目标将有助于正常衰老和AD中评估的精确和建模；并更好地理解EF作为AD衰老研究和临床研究的结果。拟议的K01将在评估和分析方面发展深厚和广泛的技能，并在临床试验，测量理论，神经心理学以及高级统计/LVA建模方面的设计和进行培训中实现。在整个五年的赠款中，来自经验丰富的临床试验人员保罗·艾森（Paul Aisen）医学博士保罗·艾森（Paul Aisen）的指导将在这些领域中获得专家咨询。 PI是一名初级研究者，希望将其统计学专业知识扩展到包括LVA方法，以及她在认知科学方面的背景，包括神经心理学评估和测量。该K01奖将实现这些奖项，因此将使PI更接近她成为认知衰老和广告中独立研究人员的长期目标。