Studies performed in the field of oxidative medicine and cellular longevity frequently focus on the association between biomarkers of cellular and molecular mechanisms of oxidative stress as well as of aging, immune function, and vascular biology with specific time to event data, such as mortality and organ failure. Indeed, time-to-event analysis is one of the most important methodologies used in clinical and epidemiological research to address etiological and prognostic hypotheses. Survival data require adequate methods of analyses. Among these, the Kaplan-Meier analysis is the most used one in both observational and interventional studies. In this paper, we describe the mathematical background of this technique and the concept of censoring (right censoring, interval censoring, and left censoring) and report some examples demonstrating how to construct a Kaplan-Meier survival curve and how to apply this method to provide an answer to specific research questions.

中文翻译：

---

分析事件时间数据的方法：Kaplan-Meier 生存曲线

在氧化医学和细胞寿命领域进行的研究经常关注氧化应激的细胞和分子机制以及衰老、免疫功能和血管生物学的生物标志物与特定事件发生时间数据（例如死亡率和器官）之间的关联。失败。事实上，事件发生时间分析是临床和流行病学研究中用于解决病因和预后假设的最重要的方法之一。生存数据需要足够的分析方法。其中，Kaplan-Meier 分析是观察性和介入性研究中最常用的一种。在本文中，我们描述了这种技术的数学背景和删失的概念（右删失、区间删失、