A review on statistical and machine learning competing risks methods
Karla Monterrubio‐Gómez,
Nathan Constantine‐Cooke,
Catalina A. Vallejos
Abstract:When modeling competing risks (CR) survival data, several techniques have been proposed in both the statistical and machine learning literature. State‐of‐the‐art methods have extended classical approaches with more flexible assumptions that can improve predictive performance, allow high‐dimensional data and missing values, among others. Despite this, modern approaches have not been widely employed in applied settings. This article aims to aid the uptake of such methods by providing a condensed compendium of CR… Show more
Set email alert for when this publication receives citations?
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.