Statistical Methods for Alzheimer's Research

研究的主要主题是开发新的方法来解决从 我们团队在对代表性不足和总体老龄化人口进行的队列研究中的合作 对阿尔茨海默病(AD)特别感兴趣。我们专注于对删失的数据进行适当的统计推断 具有部分已知风险集的生存数据，正在开发评估复发预测精度的方法 事件生存数据，包括随时间变化的ROC和C指数的扩展，以及开发新的 对生命周期内的生命历史过程进行最佳建模的条件建模策略。我们还计划 开发公开可用的统计软件，以传播和推广为目标。 所提出的估计复发事件与时间相关的ROC和C指数的方法扩展了 通过考虑复发事件的不同模型来计算单个生存时间，产生新的预测 当存在可能丢失的事件时，用于重复事件的模型。这种预测模型在以下方面很有用 估计部分观察到的风险集，这将导致回归中适当的参数估计 使用建议的加权估计对经审查的生存数据，特别是AD发病时间进行分析 接近了。在AD研究中，死亡作为一种终末事件在老年队列中频繁发生。建议数 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 了解阿尔茨海默病的病因和预防。该方法还提供了一种直接的、因此更精确的 对AD患病率的估计可能会对卫生保健政策的制定产生影响。这些方法是 其动机来自并将应用于广泛的数据集，包括印度卫生服务--一种独特的 研究美国原住民人口中AD不成比例负担的来源，老龄化， 人口统计学和记忆研究--代表理想的健康和退休研究的子集 全国黑人和拉丁裔社区阿尔茨海默病流行病学观察队列研究 阿尔茨海默氏症协调中心统一数据集由美国多个ADRC从其 纵向队列，这是一项90+的研究，重点是年龄最大的老年人口和痴呆症，以及CMS 有限的数据集，是可用于研究目的的Medicare/Medicaid索赔数据的随机样本- 最具代表性的美国人口老龄化数据。这些方法将广泛适用于解决以下问题 生物医学研究的许多其他领域。

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.