Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/12529
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dc.contributor.advisorBURZYKOWSKI, Tomasz-
dc.contributor.advisorARBYN, Marc-
dc.contributor.authorBlaha, Ondrej-
dc.date.accessioned2011-11-25T09:04:02Z-
dc.date.available2011-11-25T09:04:02Z-
dc.date.issued2010-
dc.identifier.urihttp://hdl.handle.net/1942/12529-
dc.description.abstractThe aim of this study is to model the natural history of human papillomavirus (HPV) in women which is responsible for several types of cancer and other less serious diseases. The analysis is restricted to infection with HPV16 and HPV18, which cause jointly 70% of cervical cancers. The point of interest is in modeling the cumulative incidence, clearance, persistence, and progression of infection to the lesions which are the precursors for cervical cancer. As a subquestion was established to model the history based on the age. For modeling are used survival analysis techniques such as non-parametric estimation of survival function by Kaplan-Meier method, semi-parametric proportional hazard model, and several methods and statistics intended for comparing the survival functions and proportions. The proposed methods are applied on the RIATOL laboratory data set which consists of information from the cytology and HPV genotyping investigations in the population based follow up ep-
dc.languageen-
dc.language.isoen-
dc.publishertUL Diepenbeek-
dc.titlePrevention of cancers caused by infection with human papillomavirus types: modelling of the natural history of HPV infection-
dc.typeTheses and Dissertations-
local.format.pages36-
local.bibliographicCitation.jcatT2-
dc.description.notesMaster of Statistics-Biostatistics-
local.type.specifiedMaster thesis-
dc.bibliographicCitation.oldjcatD2-
item.accessRightsClosed Access-
item.contributorBlaha, Ondrej-
item.fulltextNo Fulltext-
item.fullcitationBlaha, Ondrej (2010) Prevention of cancers caused by infection with human papillomavirus types: modelling of the natural history of HPV infection.-
Appears in Collections:Master theses
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