A Bayesian analysis of correlated interval-censored data
SOURCE: Communications in Statistics - Theory and Methods
OUTPUT TYPE: Journal Article
PUBLICATION YEAR: 2007
TITLE AUTHOR(S): K.Zuma
KEYWORDS: EPIDEMIOLOGY, STATISTICS
DEPARTMENT: Public Health, Societies and Belonging (HSC)
Print: HSRC Library: shelf number 4498
HANDLE: 20.500.11910/6163
URI: http://hdl.handle.net/20.500.11910/6163
OUTPUT TYPE: Journal Article
PUBLICATION YEAR: 2007
TITLE AUTHOR(S): K.Zuma
KEYWORDS: EPIDEMIOLOGY, STATISTICS
DEPARTMENT: Public Health, Societies and Belonging (HSC)
Print: HSRC Library: shelf number 4498
HANDLE: 20.500.11910/6163
URI: http://hdl.handle.net/20.500.11910/6163
If you would like to obtain a copy of this Research Output, please contact Hanlie Baudin at researchoutputs@hsrc.ac.za.
Abstract
In epidemiological studies where subjects are seen periodically on follow-up visits, interval-censored data occur naturally. The exact time the change of state (such as HIV seroconversion) occurs is not known exactly, only that it occurred within some time interval. In multi-stage sampling or partner tracing studies, individuals are grouped into smaller subgroups. Individuals within a subgroup share an unobservable specific frailty which induces correlation within the subgroup. In this paper, we consider a Bayesian model for analysing correlated interval-censored data. Parameters are estimated using the Markov chain Monte Carlo methods, specifically the Gibbs sampler.-
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