发表机构
University of Minnesota; Columbia University; University of Texas; University of Texas Southwestern Medical Center; University of Mississippi Medical Center; University of North Carolina; Wake Forest University School of Medicine（明尼苏达大学; 哥伦比亚大学; 德克萨斯大学; 德克萨斯大学西南医学中心; 密西西比大学医学中心; 北卡罗来纳大学; 维克森林大学医学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对左截断、分量特异性删失（区间删失非致命事件与右删失死亡）下的无事件生存估计，提出核平滑非参数方法，支持受限均值检验及补充数据利用，并经模拟和ARIC数据验证。
AI 中文摘要
在某些临床环境中，被诊断患有某种感兴趣疾病的患者面临死亡以及严重非致命事件的风险，研究兴趣在于无事件生存（EFS），这是一个复合终点，定义为从疾病发作到非致命事件和死亡中较早发生的时间。当每个组成部分受到不同的删失机制影响时，EFS会出现分量特异性删失。例如，非致命事件可能在评估之间是区间删失的，而死亡是右删失的。此外，在个体在疾病发作后入组的研究中（包括现患队列研究），EFS是左截断的。文献中已有估计左截断和右删失数据下EFS概率的方法，但这些方法无法处理分量特异性删失。我们提出了一种核平滑方法，用于在此设置下非参数估计EFS。我们的方法还可以估计和检验受限平均无事件生存时间的差异，并能利用可能可用的两种补充数据：仅随访死亡（不随访非致命事件）的参与者数据，以及当疾病发作与研究入组之间没有延迟时产生的发病队列数据。我们通过模拟评估了所提出的方法，并使用社区动脉粥样硬化风险（ARIC）研究的数据演示了该方法，以估计心肌梗死后的无痴呆生存概率。
英文摘要
In certain clinical settings, patients diagnosed with a disease of interest are at risk for death as well as a serious nonfatal event, and interest lies in event-free survival (EFS), a composite endpoint defined as the time from disease onset until the earlier of the nonfatal event and death. Component-wise censoring of EFS arises when each component is subject to a different censoring mechanism. For example, the nonfatal event may be interval censored between assessments, and death is right-censored. Further, in studies where individuals enroll after disease onset (including prevalent cohort studies), EFS is left truncated. Methods to estimate EFS probability with left-truncated and right-censored data are available in the literature, but they cannot handle component-wise censoring. We propose a kernel smoothing method to non-parametrically estimate EFS in this setting. Our method can also estimate and test for differences in the restricted mean event-free survival time, and can leverage two types of supplemental data that may be available: data from participants followed for death only (not followed for the nonfatal event), and incident cohort data, which arises when there is no delay between disease onset and study enrollment. We assess the proposed method using simulations and demonstrate the method using data from the Atherosclerosis Risk in Communities (ARIC) Study to estimate dementia-free survival probability following a myocardial infarction.
