Nonparametric hypothesis testing in clustered survival model

John de Guzman Eustaquio; Erniel B. Barrios

doi:10.1080/03610918.2016.1241407

Nonparametric hypothesis testing in clustered survival model

John de Guzman Eustaquio, Erniel B. Barrios

Research output: Contribution to journal › Article › Research › peer-review

1 Citation (Scopus)

Abstract

A nonparametric test for the presence of clustering in survival data is proposed. Assuming a model that incorporates the clustering effect into the Cox Proportional Hazards model, simulation studies indicate that the procedure is correctly sized and powerful in a reasonably wide range of scenarios. The test for the presence of clustering over time is also robust to model misspecification. With large number of clusters, the test is powerful even if the data is highly heterogeneous.

Original language	English
Pages (from-to)	7485-7500
Number of pages	16
Journal	Communications in Statistics - Simulation and Computation
Volume	46
Issue number	9
DOIs	https://doi.org/10.1080/03610918.2016.1241407
Publication status	Published - 26 Apr 2017
Externally published	Yes

Keywords

Backfitting algorithm
Bootstrap confidence interval
Clustered data
Generalized additive models
Survival analysis

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10.1080/03610918.2016.1241407

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abstract = "A nonparametric test for the presence of clustering in survival data is proposed. Assuming a model that incorporates the clustering effect into the Cox Proportional Hazards model, simulation studies indicate that the procedure is correctly sized and powerful in a reasonably wide range of scenarios. The test for the presence of clustering over time is also robust to model misspecification. With large number of clusters, the test is powerful even if the data is highly heterogeneous.",

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KW - Bootstrap confidence interval

KW - Clustered data

KW - Generalized additive models

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Nonparametric hypothesis testing in clustered survival model

Abstract

Keywords

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