Statistical Methods for Alzheimer's Research

阿尔茨海默病研究的统计方法

• 批准号: 10522647
• 负责人: Daniel L Gillen
• 金额: $ 112.55万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-02 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10522647
• 关键词: Aged, 80 and over; Aging; Alzheimer' s Disease; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Biomedical Research; Black Populations; Black race; Cardiovascular Diseases; Caring; Catchment Area; Cations; Cessation of life; Clinic; Cohort Studies; Collaborations; Communities; Computer software; Data; Data Set; Data Sources; Dementia; Diabetes Mellitus; Disease; Electronic Health Record; Epidemiology; Etiology; Event; Goals; Health Policy; Health and Retirement Study; Incidence; Individual; Institution; Latinx; Latinx population; Lead; Life; Longevity; Longitudinal cohort; Longitudinal cohort study; Medicare/Medicaid; Memory; Methodology; Methods; Minority; Modeling; Native Americans; Onset of illness; Patients; Pattern; Policy Making; Population; Prevalence; Prevention; Probability; Procedures; Process; ROC Curve; Recurrence; Regression Analysis; Research; Research Personnel; Risk; Risk Factors; Sampling; Source; Statistical Methods; System; Time; United States; United States Centers for Medicare and Medicaid Services; United States Indian Health Service; Ursidae Family; Work; aging demographic; aging population; care seeking; cohort; discrete time; ethnoracial minority; experience; factor A; improved; indexing; interest; life history; lifetime risk; migration; novel; patient health information; predictive modeling; semiparametric; stem; time use

The main theme of the research is to develop new methodologies for resolving statistical issues emerging from our team's collaborations in cohort studies of underrepresented and overall US aging populations with a particular interest in Alzheimer's disease (AD). We focus on making appropriate statistical inference for censored survival data with partially known risk sets, developing methods for assessing prediction precision for recurrent events survival data including time-dependent ROC and extension of the C-index, and developing new conditional modeling strategies that best model life history processes during the lifespan. We also plan to develop publicly available statistical software with the goal of dissemination and generalization. The proposed methods of estimating time-dependent ROC and C-index for recurrent events extend those for a single survival time by taking into account different models for recurrent events, producing novel predictive models for recurrent events when there are possible missing events. Such predictive models are useful in estimating the partially observed risk sets which would lead to appropriate parameter estimation in regression analysis for censored survival data, in particular, time of AD onset, using the proposed weighted estimating approaches. In AD research, death as a terminal event occurs frequently in aging cohorts. The proposed conditional modeling strategy allows us to investigate AD events during the entire lifespan, which provides a clearer picture of the relationships among AD onset, death, other life evens, and risk factors, thus a better understanding of AD etiology and prevention. The method also provides a straightforward thus more precise estimation of AD prevalence that is potentially impactful to health care policy making. These methods are motivated from and will be applied to a wide range of datasets, including the Indian Health Service – a unique source for studying the disproportionate burden of AD among Native American populations, the Aging, Demographics and Memory Study – a subset of the Health and Retirement Study representing an ideal observation cohort study for studying the epidemiology of AD in the Black and Latinx communities, the National Alzheimer's Coordinating Center uniform data sets collected by more than ADRCs in the US from their longitudinal cohorts, the 90+ study that focuses on aging and dementia of the oldest old population, and the CMS Limited Data Set that is a random sample of Medicare/Medicaid claims data accessible for research purposes – the most representative data of the US aging population. The methods will be widely applicable to problems in many other fields of biomedical research.

研究的主题是制定新的方法来解决 我们的团队在代表性不足和整体美国老龄化人口的队列研究中的合作， 对阿尔茨海默病（AD）特别感兴趣。我们专注于对删失数据进行适当的统计推断， 部分已知风险集的生存数据，开发用于评估复发风险预测精度的方法。 事件生存数据，包括时间依赖性ROC和C指数的扩展，并开发新的 有条件的建模策略，最好的模型生活史过程中的生命周期。我们还计划 开发可公开获得的统计软件，以便传播和推广。 所提出的估计复发事件的时间依赖性ROC和C指数的方法扩展了 通过考虑复发事件的不同模型， 当存在可能的缺失事件时，为复发事件建立模型。这种预测模型在以下方面是有用的： 估计部分可观察的风险集，这将导致回归中适当的参数估计 使用建议的加权估计分析删失生存数据，特别是AD发作时间 接近。在AD研究中，死亡作为终末事件经常发生在衰老队列中。拟议 条件建模策略允许我们在整个生命周期内调查AD事件，这提供了一个 更清楚地了解AD发病、死亡、其他生活事件和危险因素之间的关系，从而更好地 了解AD的病因和预防。该方法还提供了一个简单的，因此更精确的 估计AD患病率，这可能对医疗保健政策制定产生影响。这些方法 其动机来自并将应用于广泛的数据集，包括印度卫生服务-一个独特的 研究美国土著人口中AD负担不成比例的来源，老龄化， 人口统计学和记忆研究-健康和退休研究的一个子集，代表了一个理想的 一项研究黑人和拉丁裔社区AD流行病学的观察队列研究， 阿尔茨海默氏症协调中心统一的数据集收集了超过ADRC在美国从他们的 纵向队列，90岁以上的研究，重点是老龄化和老年痴呆症的最古老的老年人口，和CMS 有限数据集，是可用于研究目的的Medicare/Medicaid索赔数据的随机样本- 美国老龄化人口最具代表性的数据。该方法将广泛适用于 许多其他生物医学研究领域。