Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/13407

TitleInference for non-markov multi-state models : an overview
Author(s)Machado, Luís Meira
KeywordsBivariate censoring
Markov property
Multi-state models
Kaplan–Meier
Presmoothing
Transition probabilities.
Issue dateMar-2011
PublisherInstituto Nacional de Estatística (INE)
JournalREVSTAT: Statistical Journal
Abstract(s)In longitudinal studies of disease, patients can experience several events across a follow-up period. Analysis of such studies can be successfully performed by multistate models. This paper considers nonparametric and semiparametric estimation of important targets in multi-state modeling, such as the transition probabilities and bivariate distribution function (for sequentially ordered events). These estimators are shown to be consistent even for data which is non-Markov. We illustrate the methods on two data sets.
TypeArticle
URIhttp://hdl.handle.net/1822/13407
ISSN1645-6726
Publisher versionhttp://www.ine.pt/revstat/inicio.html
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CMAT - Artigos em revistas com arbitragem / Papers in peer review journals
DMA - Artigos (Papers)

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