期刊
STATISTICS IN MEDICINE
卷 32, 期 12, 页码 2062-2069出版社
WILEY
DOI: 10.1002/sim.5673
关键词
nonparametric survival analysis; survivor function; right-censored data; KaplanMeier; K-nearest neighbors; Mahalanobis distance; Cox regression; organ transplantation
We introduce a nonparametric survival prediction method for right-censored data. The method generates a survival curve prediction by constructing a (weighted) KaplanMeier 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 (c) 2012 John Wiley & Sons, Ltd.
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