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

总结 超过650万美国人患有阿尔茨海默病（AD），预计到2050年， 双然而，开发有效的治疗方法仍然是一个迫切的未满足的需求。在一个高度 复杂的AD病理生理学和昂贵而漫长的药物开发过程，批准用于治疗的药物的再利用 其他迹象是一个有吸引力的补充办法。重新利用药物的基本原理最初依赖于 观察性研究表明，累积药物暴露与以下两种情况相关： 在发生AD痴呆的风险中，或基于连续认知功能减退的认知功能减退速率减缓， 在调整协变量后，尸检时AD神经病理学变化较轻 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饰的 危险因素然而，绝大多数此类已发表的纵向研究都忽略了截断或 与感兴趣的协变量相关联的区间删失（例如，累积药物暴露），这是由于 在纵向研究中，因死亡或非连续观察访视而终止观察。建筑 基于我们之前在截断和审查以及AD领域的广泛工作，我们建议开发 更适当地处理这些抽样和测量问题的方法，以避免偏差。我们将应用 他们在两个病例研究的药物与AD风险相反的协会-他汀类药物（保护）和 质子泵抑制剂（PPI，有害）-但纵向研究的结果不一。为此我们将 利用两个高质量的公开纵向数据集的优势：国家阿尔茨海默氏症 协调中心（NACC）队列研究和哈佛衰老大脑研究（HABS）。在目标1中，我们建议 消除由于AD研究中截断的协变量测量而产生的偏倚的分析方法， 例如累积他汀类药物暴露，包括逆概率加权、伪观测和逆 回归方法。在目标2中，我们开发了时间-事件回归的伪观测方法， 区间删失协变量。在目标3中，我们使用以下方法对NACC和HABS数据集进行分析并报告 提出的方法，并开发公开可用的R包实现我们提出的方法。 成功地完成这些具体目标将产生新的统计方法，消除偏见 这可能会出现截断和区间删失协变量，这是纵向队列研究所固有的 在AD和相关痴呆症中。我们建议的研究是响应NIA特别关注通知（NOSI）： 最大化现有队列和数据集次要分析的科学价值，以解决 研究差距和促进老龄化研究的额外机会（NOT-AG-21 - 020）。

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).