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Title: Correlated gamma frailty model for bivariate survival time data
Authors: MARTINS, Adelino 
AERTS, Marc 
HENS, Niel 
Wienke, Andreas
ABRAMS, Steven 
Issue Date: 2018
Source: Statistical Methods in Medial Research, 28 (10-11), p. 3437-3450
Status: In Press
Abstract: Frailty models have been developed to quantify both heterogeneity as well as association in multivariate time-to-event data. In recent years, numerous shared and correlated frailty models have been proposed in the survival literature allowing for different association structures and frailty distributions. A bivariate correlated gamma frailty model with an additive decomposition of the frailty variables into a sum of independent gamma components was introduced before. Although this model has a very convenient closed-form representation for the bivariate survival function, the correlation among event- or subject-specific frailties is bounded above which becomes a severe limitation when the values of the two frailty variances differ substantially. In this article, we review existing correlated gamma frailty models and propose novel ones based on bivariate gamma frailty distributions. Such models are found to be useful for the analysis of bivariate survival time data regardless of the censoring type involved. The frailty methodology was applied to right-censored and left-truncated Danish twins mortality data and serological survey current status data on varicella zoster virus and parvovirus B19 infections in Belgium. From our analyses, it has been shown that fitting more flexible correlated gamma frailty models in terms of the imposed association and correlation structure outperforms existing frailty models including the one with an additive decomposition.
Notes: Martins, A (reprint author), Eduardo Mondlane Univ, Julius Nyerere 3453, Maputo, Mozambique.
Keywords: Individual heterogeneity; current status data; left-truncated data; right-censored data; serological survey data
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ISSN: 0962-2802
e-ISSN: 1477-0334
DOI: 10.1177/0962280218803127
ISI #: 000486889000034
Category: A1
Type: Journal Contribution
Validations: ecoom 2020
Appears in Collections:Research publications

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