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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 | Source: | JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 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 | Keywords: | age-dependent frailties; basic reproduction number; non-immunizing infections; serology; shared and correlated frailty models | Document URI: | http://hdl.handle.net/1942/24173 | ISSN: | 0035-9254 | e-ISSN: | 1467-9876 | DOI: | 10.1111/rssc.12236 | ISI #: | 000425638000008 | Rights: | © 2017 The Authors Journal of the Royal Statistical Society: Series C (Applied Statistics) Published by John Wiley & Sons Ltd on behalf of the Royal Statistical Society. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2019 |
Appears in Collections: | Research publications |
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