Nonparametric Hypothesis Testing for Isotonic Survival Models with Clustering

Year: 

2016

Number: 

2

Volume: 

65

Author: 

John D. Eustaquio

Abstract: 

A nonparametric test for clustering in survival data based on the bootstrap method is proposed. The survival model used considers the isotonic property of the covariates in the estimation via the backfitting algorithm. Assuming a model that incorporates the clustering effect into the piecewise proportional hazards model, simulation studies indicate that the procedure is correctly-sized and powerful in a reasonably wide range of scenarios. The test procedure for the presence of clustering over time is also robust to model misspecification.

Keywords: 

Bootstrap confidence interval; Survival Analysis; Clustered Data; Backfitting Algorithm; Generalized Additive Models; Nonparametric bootstrap.

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