The Identifiability of Dependent Competing Risks Models Induced by Bivariate Frailty Models

Document Type

Article

Publication Date

6-1-2015

Abstract

In this paper, we propose to use a special class of bivariate frailty models to study dependent censored data. The proposed models are closely linked to Archimedean copula models. We give sufficient conditions for the identifiability of this type of competing risks models. The proposed conditions are derived based on a property shared by Archimedean copula models and satisfied by several well-known bivariate frailty models. Compared with the models studied by Heckman and Honoré and Abbring and van den Berg, our models are more restrictive but can be identified with a discrete (even finite) covariate. Under our identifiability conditions, expectation-maximization (EM) algorithm provides us with consistent estimates of the unknown parameters. Simulation studies have shown that our estimation procedure works quite well. We fit a dependent censored leukaemia data set using the Clayton copula model and end our paper with some discussions.

Identifier

84928275735 (Scopus)

Publication Title

Scandinavian Journal of Statistics

External Full Text Location

https://doi.org/10.1111/sjos.12114

e-ISSN

14679469

ISSN

03036898

First Page

427

Last Page

437

Issue

2

Volume

42

Grant

DMS1106608

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