Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/31544

TitleNonparametric estimation of conditional transition probabilities in a non-Markov illness-death model
Author(s)Machado, Luís Meira
Uña-Álvarez, Jacobo de
Datta, Somnath
KeywordsConditional survival
Dependent censoring
Kaplan-Meier
Multi-state model
Nonparametric regression
Issue date2015
PublisherSpringer Verlag
JournalComputational Statistics
CitationMeira-Machado, L., de Una-Alvarez, J., & Datta, S. (2015). Nonparametric estimation of conditional transition probabilities in a non-Markov illness-death model. Computational Statistics, 30(2), 377-397. doi: 10.1007/s00180-014-0538-6
Abstract(s)One important goal in multi-state modeling is the estimation of transition probabilities. In longitudinal medical studies these quantities are particularly of interest since they allow for long-term predictions of the process. In recent years signi ficant contributions have been made regarding this topic. However, most of the approaches assume independent censoring and do not account for the influence of covariates. The goal of the paper is to introduce feasible estimation methods for the transition probabilities in an illness-death model conditionally on current or past covariate measures. All approaches are evaluated through a simulation study, leading to a comparison of two di erent estimators. The proposed methods are illustrated using real a colon cancer data set.
TypeArticle
URIhttp://hdl.handle.net/1822/31544
DOI10.1007/s00180-014-0538-6
ISSN0943-4062
1613-9658
Publisher versionhttp://link.springer.com/article/10.1007%2Fs00180-014-0538-6
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CMAT - Artigos em revistas com arbitragem / Papers in peer review journals

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