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A K ‐nearest neighbors survival probability prediction method
Author(s) -
Lowsky D.J.,
Ding Y.,
Lee D.K.K.,
McCulloch C.E.,
Ross L.F.,
Thistlethwaite J.R.,
Zenios S.A.
Publication year - 2013
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5673
Subject(s) - covariate , proportional hazards model , survival analysis , statistics , estimator , nonparametric statistics , metric (unit) , data set , mathematics , similarity (geometry) , computer science , set (abstract data type) , artificial intelligence , operations management , economics , image (mathematics) , programming language
We introduce a nonparametric survival prediction method for right‐censored data. The method generates a survival curve prediction by constructing a (weighted) Kaplan–Meier estimator using the outcomes of the K most similar training observations. Each observation has an associated set of covariates, and a metric on the covariate space is used to measure similarity between observations. We apply our method to a kidney transplantation data set to generate patient‐specific distributions of graft survival and to a simulated data set in which the proportional hazards assumption is explicitly violated. We compare the performance of our method with the standard Cox model and the random survival forests method. Copyright © 2012 John Wiley & Sons, Ltd.