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Application and comparison of methods for analysing correlated interval-censored data from sexual partnerships

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dc.date.accessioned 2006-01-24 en
dc.date.accessioned 2020-02-20T09:16:58Z
dc.date.available 2020-02-20T09:16:58Z
dc.date.issued 2015-08-25 en
dc.identifier.uri http://hdl.handle.net/20.500.11910/7038
dc.description.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. en
dc.format.medium Print en
dc.subject HIV/AIDS en
dc.subject SEXUAL BEHAVIOUR en
dc.subject GIBBS SAMPLER en
dc.subject EM ALGORITHM en
dc.subject EPIDEMIOLOGY en
dc.title Application and comparison of methods for analysing correlated interval-censored data from sexual partnerships en
dc.type Journal Article en
dc.description.version N en
dc.Volume 3 en
dc.BudgetYear 2005/06 en
dc.ResearchGroup Social Aspects of HIV/AIDS and Health en
dc.SourceTitle Journal of Data Science en
dc.ArchiveNumber 3567 en
dc.PageNumber 241-256 en
dc.outputnumber 2120 en
dc.bibliographictitle Zuma, K. & Lurie, M.N. (2005) Application and comparison of methods for analysing correlated interval-censored data from sexual partnerships. <i>Journal of Data Science</i>. 3:241-256. en
dc.publicationyear 2005 en
dc.contributor.author1 Zuma, K. en
dc.contributor.author2 Lurie, M.N. en


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