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Semiparametric analysis of clustered survival data under nonparametric frailty
Author(s) -
Naskar Malay
Publication year - 2008
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/j.1467-9574.2007.00372.x
Subject(s) - nonparametric statistics , cluster analysis , proportional hazards model , computer science , expectation–maximization algorithm , survival analysis , maximization , dirichlet process , semiparametric model , monte carlo method , statistics , data mining , econometrics , mathematics , mathematical optimization , artificial intelligence , maximum likelihood , inference
In this article a novel approach to analyze clustered survival data that are subject to extravariation encountered through clustering of survival times is proposed. This is accomplished by extending the Cox proportional hazard model to a frailty model where the cluster‐specific shared frailty is modeled nonparametrically. We assume a nonparametric Dirichlet process for the distribution of frailty. In such a semiparametric setup, we propose a hybrid method to draw model‐based inferences. In the framework of the proposed hybrid method, the estimation of parameters is performed by implementing Monte Carlo expected conditional maximization algorithm. A simulation study is conducted to study the efficiency of our methodology. The proposed methodology is, thereafter, illustrated by a real‐life data on recurrence time to infections in kidney patient.