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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/24173

Title: Modelling time varying heterogeneity in recurrent infection processes: an application to serological data
Authors: Abrams, Steven
Wienke, Andreas
Hens, Niel
Issue Date: 2017
Citation: Journal of the Royal Statistical Society, Series C, 67(3), p. 687-704
Abstract: Frailty models are often used in survival analysis to model multivariate time-to-event data. In infectious disease epidemiology, frailty models have been proposed to model heterogeneity in the acquisition of infection and to accommodate association in the occurrence of multiple types of infection. Although traditional frailty models rely on the assumption of lifelong immunity after recovery, refinements have been made to account for reinfections with the same pathogen. Recently, Abrams and Hens quantified the effect of misspecifying the underlying infection process on the basic and effective reproduction number in the context of bivariate current status data on parvovirus B19 and varicella zoster virus. Furthermore, Farrington, Unkel and their co-workers introduced and applied time varying shared frailty models to paired bivariate serological data. In this paper, we consider an extension of the proposed frailty methodology by Abrams and Hens to account for age-dependence in individual heterogeneity through the use of age-dependent shared and correlated gamma frailty models. The methodology is illustrated by using two data applications.
Notes: Abrams, S (reprint author), Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, Agoralaan Bldg D, B-3590 Diepenbeek, Belgium, steven.abrams@uhasselt.be
URI: http://hdl.handle.net/1942/24173
DOI: 10.1111/rssc.12236
ISI #: 000425638000008
ISSN: 0035-9254
Category: A1
Type: Journal Contribution
Appears in Collections: Research publications

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