Improving Cognitive Outcome Precision & Responsiveness with Modern Psychometrics

描述（由申请人提供）：阿尔茨海默病神经影像学计划（ADNI）是一项耗资6000万美元、多地点、纵向、前瞻性、自然的研究，研究对象是正常认知衰老、轻度认知障碍（MCI）和早期阿尔茨海默病（AD）患者，旨在促进对MCI和AD发病和进展的神经影像学和其他标志物的科学评估。认知功能测试为ADNI的比较提供了基准。ADNI项目的一个主要限制是没有应用现代心理测量分析。我们的研究小组来自华盛顿大学、加州大学戴维斯分校、圣路易斯华盛顿大学和波士顿希伯来老年人康复中心，他们打算解决这一限制，以确定那些最能检测和测量重要变化的方法。我们将结合ADNI数据分析和皮层下缺血性血管疾病项目资助（Helena Chui， PI）的数据分析，以实现以下具体目标：采用现代心理测量方法，将ADNI和SIVD PPG的神经心理学数据应用于正常衰老、MCI和AD受试者，制定测量随时间变化的策略。目标2。评估用于测量随时间变化的现代心理测量方法，并与使用模拟和真实数据的传统分析进行比较。目标3。制定和评估将神经心理学、神经影像学和生物标志物数据结合成预防和治疗MCI和AD的干预措施的随机对照试验（rct）的复合结果的策略。目标4。为统一数据集（UDS）开发临床有用的评分报告，以总结一般的认知，记忆和执行功能，考虑DIP和测量误差。我们将测试几个假设，并希望阐明统计技术，以优化心理测量数据的稳健性，这些数据将在未来的临床试验中收集，无论是单独还是与成像结合。

DESCRIPTION (provided by applicant): The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a $60 million, multi-site, longitudinal, prospective, naturalistic study of individuals with normal cognitive aging, mild cognitive impairment (MCI), and early Alzheimer's disease (AD) to facilitate the scientific evaluation of neuroimaging and other markers for the onset and progression of MCI and AD. Tests of cognitive functioning provide the benchmark for comparison in ADNI. A major limitation of the ADNI project is that modern psychometric analyses are not applied. Our group of investigators from the University of Washington, the University of California Davis, Washington University in St. Louis, and the Hebrew Rehabilitation Center for the Aged in Boston intend to address this limitation to determine those methods best able to detect and measure important changes over time. We will combine analyses of ADNI data with analyses of data from the Subcortical Ischemic Vascular Disease Program Project Grant (Helena Chui, PI) to pursue the following specific aims: Aim 1. Develop strategies for measuring change over time in subjects with normal aging, MCI, and AD using modern psychometric methods applied to neuropsychological data from ADNI and SIVD PPG. Aim 2. Evaluate modern psychometric methods for measuring change over time, and compare with conventional analyses using simulated and real data. Aim 3. Develop and evaluate strategies for combining neuropsychological, neuroimaging, and biomarker data into composite outcomes for randomized controlled trials (RCTs) of interventions to prevent and treat MCI and AD. Aim 4. Develop clinically useful score reports for the Uniform Data Set (UDS) to summarize general cognition, memory, and executive functioning, accounting for DIP and measurement error. We will test several hypotheses and hope to illustrate statistical techniques for optimizing the robustness of the psychometric data that will be collected in future clinical trials, whether alone or in conjunction with imaging.