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Stat Med. 2013 May 30;32(12):2062-9. doi: 10.1002/sim.5673.

A K-nearest neighbors survival probability prediction method.

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RAND Corporation, Santa Monica, CA 90407, USA.


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.

[Indexed for MEDLINE]

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