Nonparametric estimation of conditional transition probabilities in a non-Markov illness-death model

Bibliographic Details
Main Author: Machado, Luís Meira
Publication Date: 2015
Other Authors: Uña-Álvarez, Jacobo de, Datta, Somnath
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/1822/31544
Summary: 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.
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spelling Nonparametric estimation of conditional transition probabilities in a non-Markov illness-death modelConditional survivalDependent censoringKaplan-MeierMulti-state modelNonparametric regressionCiências Naturais::MatemáticasCiências Sociais::Economia e GestãoScience & TechnologyOne 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.This research was nanced by FEDER Funds through Programa Operacional Factores de Competitividade COMPETE and by Portuguese Funds through FCT - Funda ção para a Cência e a Tecnologia, within Projects Est-C/MAT/UI0013/2011 and PTDC/MAT/104879/2008. We also acknowledge nancial support from the project Grants MTM2008-03129 and MTM2011-23204 (FEDER support included) of the Spanish Ministerio de Ciencia e Innovaci on and 10PXIB300068PR of the Xunta de Galicia. Partial support from a grant from the US National Security Agency (H98230-11-1-0168) is greatly appreciated.Springer VerlagUniversidade do MinhoMachado, Luís MeiraUña-Álvarez, Jacobo deDatta, Somnath20152015-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/31544engMeira-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/s00180-014-0538-60943-406210.1007/s00180-014-0538-6http://link.springer.com/article/10.1007%2Fs00180-014-0538-6info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-11T05:37:23Zoai:repositorium.sdum.uminho.pt:1822/31544Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T15:24:37.281289Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Nonparametric estimation of conditional transition probabilities in a non-Markov illness-death model
title Nonparametric estimation of conditional transition probabilities in a non-Markov illness-death model
spellingShingle Nonparametric estimation of conditional transition probabilities in a non-Markov illness-death model
Machado, Luís Meira
Conditional survival
Dependent censoring
Kaplan-Meier
Multi-state model
Nonparametric regression
Ciências Naturais::Matemáticas
Ciências Sociais::Economia e Gestão
Science & Technology
title_short Nonparametric estimation of conditional transition probabilities in a non-Markov illness-death model
title_full Nonparametric estimation of conditional transition probabilities in a non-Markov illness-death model
title_fullStr Nonparametric estimation of conditional transition probabilities in a non-Markov illness-death model
title_full_unstemmed Nonparametric estimation of conditional transition probabilities in a non-Markov illness-death model
title_sort Nonparametric estimation of conditional transition probabilities in a non-Markov illness-death model
author Machado, Luís Meira
author_facet Machado, Luís Meira
Uña-Álvarez, Jacobo de
Datta, Somnath
author_role author
author2 Uña-Álvarez, Jacobo de
Datta, Somnath
author2_role author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Machado, Luís Meira
Uña-Álvarez, Jacobo de
Datta, Somnath
dc.subject.por.fl_str_mv Conditional survival
Dependent censoring
Kaplan-Meier
Multi-state model
Nonparametric regression
Ciências Naturais::Matemáticas
Ciências Sociais::Economia e Gestão
Science & Technology
topic Conditional survival
Dependent censoring
Kaplan-Meier
Multi-state model
Nonparametric regression
Ciências Naturais::Matemáticas
Ciências Sociais::Economia e Gestão
Science & Technology
description 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.
publishDate 2015
dc.date.none.fl_str_mv 2015
2015-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/1822/31544
url http://hdl.handle.net/1822/31544
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Meira-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
0943-4062
10.1007/s00180-014-0538-6
http://link.springer.com/article/10.1007%2Fs00180-014-0538-6
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer Verlag
publisher.none.fl_str_mv Springer Verlag
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833595296337100800

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