Survival model induced by discrete frailty for modeling of lifetime data with long-term survivors and change-point
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1080/03610926.2019.1648826 http://hdl.handle.net/11449/184626 |
Resumo: | Frailty models are used for modeling heterogeneity in the data analysis of lifetimes. Analysis that ignore frailty when it is present leads to incorrect inferences. In survival analysis, the distribution of frailty is generally assumed to be continuous and, in some cases, it may be appropriate to consider a discrete frailty distribution. Survival models induced by frailty with a continuous distribution are not appropriate for situations in which survival data contain experimental units where the event of interest has not happened even after a long period of observation (survival data with cure fraction), that is, situations with units having zero frailty. In this paper, we propose a new survival model induced by discrete frailty for modeling survival data in the presence of a proportion of long-term survivors and a single change point. We use the maximum likelihood method to estimate the model parameters and evaluate their performance by a Monte Carlo simulation study. The proposed approach is illustrated by analyzing a kidney infection recurrence data set. |
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Repositório Institucional da UNESP |
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Survival model induced by discrete frailty for modeling of lifetime data with long-term survivors and change-pointChange-point hazard modelfrailty modelslong-term survivorsmaximum likelihoodFrailty models are used for modeling heterogeneity in the data analysis of lifetimes. Analysis that ignore frailty when it is present leads to incorrect inferences. In survival analysis, the distribution of frailty is generally assumed to be continuous and, in some cases, it may be appropriate to consider a discrete frailty distribution. Survival models induced by frailty with a continuous distribution are not appropriate for situations in which survival data contain experimental units where the event of interest has not happened even after a long period of observation (survival data with cure fraction), that is, situations with units having zero frailty. In this paper, we propose a new survival model induced by discrete frailty for modeling survival data in the presence of a proportion of long-term survivors and a single change point. We use the maximum likelihood method to estimate the model parameters and evaluate their performance by a Monte Carlo simulation study. The proposed approach is illustrated by analyzing a kidney infection recurrence data set.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)FAPEAM grants from the government of the State of Amazonas, BrazilUniv Sao Paulo, Dept Math & Stat, Sao Carlos, SP, BrazilUniv Estadual Paulista, Dept Producing Engn, Sao Paulo, BrazilUniv Fed Amazonas, Dept Stat, Manaus, Amazonas, BrazilUniv Brasilia, Dept Stat, Brasilia, DF, BrazilUniv Estadual Paulista, Dept Producing Engn, Sao Paulo, BrazilTaylor & Francis IncUniversidade de São Paulo (USP)Universidade Estadual Paulista (Unesp)Univ Fed AmazonasUniversidade de Brasília (UnB)Cancho, Vicente G.Barriga, Gladys [UNESP]Leao, JeremiasSaulo, Helton2019-10-04T12:15:18Z2019-10-04T12:15:18Z2019-07-30info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article12http://dx.doi.org/10.1080/03610926.2019.1648826Communications In Statistics-theory And Methods. Philadelphia: Taylor & Francis Inc, 12 p., 2019.0361-0926http://hdl.handle.net/11449/18462610.1080/03610926.2019.1648826WOS:000480006100001Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengCommunications In Statistics-theory And Methodsinfo:eu-repo/semantics/openAccess2021-10-23T14:48:10Zoai:repositorio.unesp.br:11449/184626Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:24:16.785311Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Survival model induced by discrete frailty for modeling of lifetime data with long-term survivors and change-point |
title |
Survival model induced by discrete frailty for modeling of lifetime data with long-term survivors and change-point |
spellingShingle |
Survival model induced by discrete frailty for modeling of lifetime data with long-term survivors and change-point Cancho, Vicente G. Change-point hazard model frailty models long-term survivors maximum likelihood |
title_short |
Survival model induced by discrete frailty for modeling of lifetime data with long-term survivors and change-point |
title_full |
Survival model induced by discrete frailty for modeling of lifetime data with long-term survivors and change-point |
title_fullStr |
Survival model induced by discrete frailty for modeling of lifetime data with long-term survivors and change-point |
title_full_unstemmed |
Survival model induced by discrete frailty for modeling of lifetime data with long-term survivors and change-point |
title_sort |
Survival model induced by discrete frailty for modeling of lifetime data with long-term survivors and change-point |
author |
Cancho, Vicente G. |
author_facet |
Cancho, Vicente G. Barriga, Gladys [UNESP] Leao, Jeremias Saulo, Helton |
author_role |
author |
author2 |
Barriga, Gladys [UNESP] Leao, Jeremias Saulo, Helton |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade de São Paulo (USP) Universidade Estadual Paulista (Unesp) Univ Fed Amazonas Universidade de Brasília (UnB) |
dc.contributor.author.fl_str_mv |
Cancho, Vicente G. Barriga, Gladys [UNESP] Leao, Jeremias Saulo, Helton |
dc.subject.por.fl_str_mv |
Change-point hazard model frailty models long-term survivors maximum likelihood |
topic |
Change-point hazard model frailty models long-term survivors maximum likelihood |
description |
Frailty models are used for modeling heterogeneity in the data analysis of lifetimes. Analysis that ignore frailty when it is present leads to incorrect inferences. In survival analysis, the distribution of frailty is generally assumed to be continuous and, in some cases, it may be appropriate to consider a discrete frailty distribution. Survival models induced by frailty with a continuous distribution are not appropriate for situations in which survival data contain experimental units where the event of interest has not happened even after a long period of observation (survival data with cure fraction), that is, situations with units having zero frailty. In this paper, we propose a new survival model induced by discrete frailty for modeling survival data in the presence of a proportion of long-term survivors and a single change point. We use the maximum likelihood method to estimate the model parameters and evaluate their performance by a Monte Carlo simulation study. The proposed approach is illustrated by analyzing a kidney infection recurrence data set. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-10-04T12:15:18Z 2019-10-04T12:15:18Z 2019-07-30 |
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://dx.doi.org/10.1080/03610926.2019.1648826 Communications In Statistics-theory And Methods. Philadelphia: Taylor & Francis Inc, 12 p., 2019. 0361-0926 http://hdl.handle.net/11449/184626 10.1080/03610926.2019.1648826 WOS:000480006100001 |
url |
http://dx.doi.org/10.1080/03610926.2019.1648826 http://hdl.handle.net/11449/184626 |
identifier_str_mv |
Communications In Statistics-theory And Methods. Philadelphia: Taylor & Francis Inc, 12 p., 2019. 0361-0926 10.1080/03610926.2019.1648826 WOS:000480006100001 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Communications In Statistics-theory And Methods |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
12 |
dc.publisher.none.fl_str_mv |
Taylor & Francis Inc |
publisher.none.fl_str_mv |
Taylor & Francis Inc |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128805053661184 |