Competing Risksand Multistate Models in Clinical Nephrology Research

• Type of Material: Article
• Authors: Katie Ross-Driscoll, Emory University; Rachel Patzer, Emory University
• Language: English
• Date: 2022-11-03
• Publisher: ELSEVIER SCIENCE INC
• Publication Version: Final Published Version
• Copyright Statement: © 2022 International Society of Nephrology. Published by Elsevier Inc.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.ekir.2022.08.011
• Title of Journal or Parent Work: KIDNEY INTERNATIONAL REPORTS
• Volume: 7
• Issue: 11
• Start Page: 2325
• End Page: 2326
• Grant/Funding Information: KRD is supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR002378 and KL2TR002381. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
• Keywords: Urology & Nephrology; Life Sciences & Biomedicine; Science & Technology; SURVIVAL ANALYSIS
• Research Categories: Health Sciences, Health Care Management; Health Sciences, Medicine and Surgery

Abstract

In cohort studies and clinical trials, outcomes are often expressed in terms of time to an event such as time to death, time to treatment, and time to disease incidence. Regression methods that handle time-to-event data are commonly called “survival analysis,” even if death is not the event of interest. Survival analyses allow us to examine not just whether an event happened, but how quickly that event happened.1 Survival analysis can also handle patients who are censored (i.e. those who drop out of the study or who do not experience the event of interest by the end of the follow-up period). Instead of treating these patients as having missing event data, survival analysis makes use of all of the follow-up information that is available by including those who experienced a certain amount of time without an event.