Analysis of Alzheimer's disease studies that feature truncated or interval-censored covariates

对具有截断或区间删失协变量的阿尔茨海默病研究的分析

• 批准号: 10725225
• 负责人: Jing Qian
• 金额: $ 22.53万
• 依托单位: UNIVERSITY OF MASSACHUSETTS AMHERST
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10725225
• 关键词: Acceleration; Address; Age; Aging; Alzheimer' s Disease; Alzheimer' s disease related dementia; Alzheimer' s disease risk; American; Amyloid; Area; Autopsy; Blood Vessels; Calibration; Case Study; Cessation of life; Clinical; Cognitive; Cohort Studies; Complex; Computer software; Cox Models; Data; Data Analyses; Data Set; Dementia; Development; Disease Outcome; Drug Exposure; Education; Evaluation; Event; Failure; Family history of; Fostering; Functional disorder; Genotype; Impaired cognition; Longitudinal Studies; Longitudinal cohort study; Measurement; Methodology; Methods; Modeling; Observational Study; Outcome; Parental Ages; Pharmaceutical Preparations; Probability; Process; Property; Proton Pump Inhibitors; Publishing; Regression Analysis; Reporting; Research; Research Project Grants; Risk; Risk Factors; Sampling; Source; Statistical Methods; Testing; Time; Visit; Work; aging brain; analytical method; cohort; cost; discrete time; drug development; drug repurposing; effective therapy; follow-up; interest; longitudinal dataset; modifiable risk; neuropathology; secondary analysis; sex; simulation; time use

SUMMARY More than 6.5 million Americans suffer from Alzheimer’s Disease (AD), and by 2050 this number is expected to double. Yet the development of effective therapies remains an urgent unmet need. In a scenario of highly complex AD pathophysiology and costly and long drug development process, repurposing of drugs approved for other indications is an attractive complementary approach. The rationale for repurposing a drug initially relies on observational studies demonstrating that the cumulative drug exposure is correlated with either a reduction in the risk of developing AD dementia, or with a slowing of the rate of cognitive decline based on serial cognitive evaluations, or with milder AD neuropathological changes at autopsy examination, after adjusting for covariates including age, sex, education, family history of dementia and APOE genotype, and vascular and other modifiable risk factors. However, the vast majority of such published longitudinal studies have ignored the truncation or interval censoring associated with the covariate of interest (e.g., cumulative drug exposure), which is due to either termination of observation by death or non-continuous observation visits in longitudinal studies. Building upon our extensive prior work in the areas of truncation and censoring as well as AD, here we propose to develop methods to more appropriately treat these sampling and measurement problems to avoid bias. We will apply them in two case studies of drugs with opposite purported associations with AD risk -- statins (protective) and proton-pump inhibitors (PPIs, deleterious) -- but mixed findings from longitudinal studies. To this end, we will leverage the strengths of two high-quality publicly available longitudinal datasets: the National Alzheimer’s Coordinating Center (NACC) cohort study and the Harvard Aging Brain Study (HABS). In Aim 1, we propose analytic methods that remove biases arising due to covariate measurements that are truncated in AD studies, such as cumulative statin exposure, including inverse probability weighting, pseudo-observations and reverse regression approaches. In Aim 2, we develop pseudo-observation methods for time-to-event regression with interval-censored covariates. In Aim 3, we conduct and report analyses of NACC and HABS datasets using proposed methods, and develop publicly available R packages for implementation of our proposed methods. Successful completion of these specific aims will produce new statistical methodology that will eliminate the bias that may arise with truncated and interval-censored covariates, which are inherent to longitudinal cohort studies in AD and related dementias. Our proposed research is responsive to the NIA Notice of Special Interest (NOSI): Maximizing the Scientific Value of Secondary Analyses of Existing Cohorts and Datasets in Order to Address Research Gaps and Foster Additional Opportunities in Aging Research (NOT-AG-21-020).

概括 超过 650 万美国人患有阿尔茨海默病 (AD)，到 2050 年，这一数字预计将 然而，在这种情况下，开发有效的治疗方法仍然是一个迫切的未满足的需求。 复杂的 AD 病理生理学和昂贵且漫长的药物开发过程，重新利用已批准的药物 其他适应症是一种有吸引力的补充方法，重新利用药物的理由最初依赖于。 观察性研究表明，累积药物暴露与减少相关 患 AD 痴呆的风险，或基于连续认知的认知能力下降速度减慢 调整协变量后，进行评估，或尸检时出现较轻的 AD 神经病理学变化 包括年龄、性别、教育程度、痴呆家族史和APOE基因型，以及血管和其他可修改的信息 然而，绝大多数已发表的纵向研究都忽略了截断或风险因素。 与感兴趣的协变量（例如累积药物暴露）相关的区间审查，这是由于 因死亡而终止观察或纵向研究中的非连续观察访问。 基于我们之前在截断和审查以及 AD 领域的广泛工作，我们在此建议开发 我们将应用更适当地处理这些抽样和测量问题以避免偏差的方法。 在两个案例研究中，药物与 AD 风险具有相反的关联——他汀类药物（保护性）和 质子泵抑制剂（PPI，有害）——但纵向研究的结果好坏参半。为此，我们将。 利用两个高质量公开纵向数据集的优势：国家阿尔茨海默病 在目标 1 中，我们建议协调中心 (NACC) 队列研究和哈佛大脑老化研究 (HABS)。 消除因 AD 研究中被截断的协变量测量而产生的偏差的分析方法， 例如累积他汀类药物暴露，包括逆概率加权、伪观察和逆向 在目标 2 中，我们开发了事件时间回归的伪观察方法。 在目标 3 中，我们使用 NACC 和 HABS 数据集进行并报告分析。 提出的方法，并开发公开可用的 R 包来实施我们提出的方法。 成功完成这些具体目标将产生新的统计方法，消除偏见 可能因截断和区间删失协变量而出现，这是纵向队列研究所固有的 我们提议的研究是对 NIA 特别关注通知 (NOSI) 的响应： 最大限度地提高现有队列和数据集二次分析的科学价值，以解决 衰老研究中的研究差距和促进额外机会（NOT-AG-21-020）。