Application and comparison of methods for analysing correlated interval-censored data from sexual partnerships
SOURCE: Journal of Data Science
OUTPUT TYPE: Journal Article
PUBLICATION YEAR: 2005
TITLE AUTHOR(S): K.Zuma, M.N.Lurie
KEYWORDS: EM ALGORITHM, EPIDEMIOLOGY, GIBBS SAMPLER, HIV/AIDS, SEXUAL BEHAVIOUR
DEPARTMENT: Public Health, Societies and Belonging (HSC)
Print: HSRC Library: shelf number 3567
HANDLE: 20.500.11910/7038
URI: http://hdl.handle.net/20.500.11910/7038
OUTPUT TYPE: Journal Article
PUBLICATION YEAR: 2005
TITLE AUTHOR(S): K.Zuma, M.N.Lurie
KEYWORDS: EM ALGORITHM, EPIDEMIOLOGY, GIBBS SAMPLER, HIV/AIDS, SEXUAL BEHAVIOUR
DEPARTMENT: Public Health, Societies and Belonging (HSC)
Print: HSRC Library: shelf number 3567
HANDLE: 20.500.11910/7038
URI: http://hdl.handle.net/20.500.11910/7038
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 sometime within a specific time interval. This paper considers estimation of parameters when HIV infection times are intervalcensored and correlated. It is assumed that each sexual partnership has a specific unobservable random effect that induces association between infection times. Parameters are estimated using the expectation-maximization algorithm and the Gibbs sampler. The results from the two methods are compared. Both methods yield fixed effects and baseline hazard estimates that are comparable. However, standard errors and frailty variance estimates are underestimated in the expectation-maximization algorithm compared to those from the Gibbs sampler. The Gibbs sampler is considered a plausible alternative to the expectation-maximization algorithm.-
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