A practical guide to understanding Kaplan-Meier curves.
A practical guide to understanding Kaplan-Meier curves.
复制标题
理解Kaplan-Meier曲线的实用指南。
DOI：
10.1016/j.otohns.2010.05.007
复制
发表时间：
2010-09
影响因子：
3.4
通讯作者：
Wang, Eric W.
中科院分区：
文献类型：
--
作者：
Rich, Jason T.;Neely, J. Gail;Paniello, Randal C.;Voelker, Courtney C. J.;Nussenbaum, Brian;Wang, Eric W.
1958年，爱德华·L.卡普兰和保罗·迈耶合作发表了一篇关于如何处理不完整观测的开创性论文。随后，Kaplan-Meier曲线和生存数据的估计值已成为处理不同生存时间（至事件发生时间）的常见方法，尤其是当并非所有受试者都继续参与研究时。“存活”时间不需要与死亡是事件的实际存活相关;“事件”可以是任何感兴趣的事件。Kaplan-Meier分析也用于非医学学科。本文的目的是解释如何生成和分析Kaplan-Meier曲线。在这篇文章中，我们将讨论Kaplan-Meier（KM）估计在“生存”的背景下，在感兴趣的事件。为了让读者清楚地看到这个过程是如何工作的，我们使用了两小组假设的数据作为例子。这些例子还说明了至关重要的一点，即比较分析取决于整个曲线，而不是孤立的点。
In 1958, Edward L. Kaplan and Paul Meier collaborated to publish a seminal paper on how to deal with incomplete observations. Subsequently, the Kaplan-Meier curves and estimates of survival data have become a familiar way of dealing with differing survival times (times-to-event), especially when not all the subjects continue in the study. “Survival” times need not relate to actual survival with death being the event; the “event” may be any event of interest. Kaplan-Meier analyses are also used in non-medical disciplines. The purpose of this paper is to explain how Kaplan-Meier curves are generated and analyzed. Throughout this article we will discuss Kaplan-Meier (K-M) estimates in the context of “survival” before the event of interest. Two small groups of hypothetical data are used as examples in order for the reader to clearly see how the process works. These examples also illustrate the crucially important point that comparative analysis depends upon the whole curve and not upon isolated points.
影响因子：
3.7
作者：
KAPLAN, EL;MEIER, P
通讯作者：
MEIER, P
影响因子：
158.5
作者：
Henschke, Claudia I.;Yankelevitz, David F.;Miettinen, Olli S.
通讯作者：
Miettinen, Olli S.