A PRACTICAL GUIDE TO UNDERSTANDING KAPLAN-MEIER CURVES
By Jason T Rich, J. Neely, R. Paniello, C. Voelker, B. Nussenbaum, Eric W. Wang. Otolaryngology Head & Neck Surgery, 2010
913 citations recorded by the source corpus.
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 nonmedical disciplines. The purpose of this article is to explain how Kaplan-Meier curves are generated and analyzed. Throughout this article, we will discuss Kaplan-Meier 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. © 2010 American Academy of Otolaryngology-Head and Neck Surgery Foundation. All rights reserved.
Subfields: Computational Mechanics, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/256909163dfe93643ab540385f7859241b0973c7
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In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Feature Importance in Nonlinear Embeddings (FINE): Applications in Digital Pathology (2016, methodology): “In addition to assessing AUC values, Kaplan-Meier survival analysis [50] was also performed for this dataset (see Fig.”
- Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot): Development and Implementation (2021, neutral): “A comprehensive and practical review of the Kaplan-Meier curves has been published previously [17].”