Kaplan-Meier estimator in competing risk contexts
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Survival analysis has become in a common procedure in biomedical researches. Conventionally, the well-known nonparametric Kaplan-Meier (KM) estimator is used in order to approximate the real survivor curve. However, in competing risk contexts where more than one failure cause compete to occur and only one of them is of interest, the direct use of the Kaplan-Meier statistic does not perform correctly and, in order to obtain a good estimation, it must be adapted. In this work, via Monte Carlo simulations, the author explores the behavior of the Kaplan-Meier estimator in a competing risk context. In addition, differences between KM and multiple decrement methods are pointed out. Finally, a real-data problem is used in order to illustrate the situation. © 2016, Hacettepe University. All rights reserved.
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