Document Server@UHasselt >
Research >
Research publications >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/28072

Title: Correlated gamma frailty model for bivariate survival time data
Authors: Martins, Adelino
Aerts, Marc
Hens, Niel
Wienke, Andreas
Abrams, Steven
Issue Date: 2018
Citation: 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. adelioshazmo@gmail.com
URI: http://hdl.handle.net/1942/28072
DOI: 10.1177/0962280218803127
ISI #: 000486889000034
ISSN: 0962-2802
Category: A1
Type: Journal Contribution
Appears in Collections: Research publications

Files in This Item:

Description SizeFormat
peer-reviewed author version712.92 kBAdobe PDF

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.