A K-nearest neighbors survival probability prediction method

作者：Lowsky D J^*; Ding Y; Lee D K K; McCulloch C E; Ross L F; Thistlethwaite J R; Zenios S A

来源：Statistics in Medicine, 2013, 32(12): 2062-2069.

DOI：10.1002/sim.5673

• 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.

• 出版日期2013-5-30