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氏病（AD），到2050年，这一数字有望 双倍的。然而，有效疗法的发展仍然是一个紧迫的需求。在高度的情况下 复杂的AD病理生理学以及昂贵且较长的药物开发过程，重新批准的药物 其他迹象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 在观察性研究中，表明累积药物暴露与减少相关 出于患AD痴呆症的风险，或者基于串行认知的认知下降速度放缓 调整协变量后，评估或进行尸检检查时米勒AD神经病理学变化 包括年龄，性别，教育，痴呆症和ApoE基因型的家族史以及血管和其他可修改 风险因素。但是，绝大多数此类发表的纵向研究都忽略了截断或 与关注的协变量相关的间隔检查（例如，累积药物暴露），这是由于 在纵向研究中，通过死亡观察或非连续观察访问终止。建筑 在我们在截断和审查领域以及广告领域的大量工作中，我们在这里提议开发 更适当地处理这些抽样和测量问题以避免偏见的方法。我们将申请 他们在两个案例研究中对与AD风险相反的相反关联的药物研究 - 他汀类药物（保护性）和 质子 - 泵抑制剂（PPIS，缺失） - 但纵向研究的结果混合了。为此，我们将 利用两个高质量公开可用纵向数据集的优势：国家阿尔茨海默氏症 协调中心（NACC）队列研究和哈佛衰老脑研究（HABS）。在AIM 1中，我们建议 消除由于AD研究中被截断的协变量测量引起的偏见的分析方法， 例如累积他汀类药物的暴露，包括逆概率加权，伪观察和反向 回归方法。在AIM 2中，我们开发了伪观察方法，用于通过 间隔审查的协变量。在AIM 3中，我们使用使用NACC和HABS数据集的分析 建议的方法，并开发公开可用的R软件包，以实施我们建议的方法。 这些特定目标的成功完成将产生新的统计方法，以消除偏见 这可能是通过截短和间隔经过的协变量引起的，这些协变量继承了纵向队列研究 在AD和相关痴呆症中。我们拟议的研究对NIA特殊利益通知（NOSI）响应： 最大化现有队列和数据集的二级分析的科学价值，以解决 研究差距和培养衰老研究的额外机会（NOT-AG-21-020）。