A multivariate survival model induced by discrete frailty
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1080/03610918.2020.1806323 http://hdl.handle.net/11449/195590 |
Resumo: | Frailty models are generally used to model heterogeneity and dependence between individuals. The distribution of the frailty variable is often assumed to be continuous. However, there are situations where a discretely-distributed frailty may be appropriate. On the other hand, when a cure rate is present in survival data, these continuous distributions may not be appropriate since individuals with long-term survival times encompass zero frailty. Having zero frailty can be interpreted as being immune or cured (long-term survivors). In this paper, we propose a new multivariate survival model induced by frailty for multivariate lifetime data in the presence of surviving fractions and examine some of its properties. The inferential approach exploits the Bayesian methods. We provide some simulation results to assess the performance of the proposed model. Furthermore, we illustrate the performance of the proposed model by means of a real data set. |
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Repositório Institucional da UNESP |
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A multivariate survival model induced by discrete frailtyCured fractionFrailty modelsGibbs samplingMultivariate survival modelsFrailty models are generally used to model heterogeneity and dependence between individuals. The distribution of the frailty variable is often assumed to be continuous. However, there are situations where a discretely-distributed frailty may be appropriate. On the other hand, when a cure rate is present in survival data, these continuous distributions may not be appropriate since individuals with long-term survival times encompass zero frailty. Having zero frailty can be interpreted as being immune or cured (long-term survivors). In this paper, we propose a new multivariate survival model induced by frailty for multivariate lifetime data in the presence of surviving fractions and examine some of its properties. The inferential approach exploits the Bayesian methods. We provide some simulation results to assess the performance of the proposed model. Furthermore, we illustrate the performance of the proposed model by means of a real data set.Univ Sao Paulo, Dept Appl Math & Stat, Ave Trabalhador Saocarlense 400, BR-13566590 Sao Paulo, BrazilSao Paulo State Univ, Fac Engn Bauru, Sao Paulo, BrazilSao Paulo State Univ, Fac Engn Bauru, Sao Paulo, BrazilTaylor & Francis IncUniversidade de São Paulo (USP)Universidade Estadual Paulista (Unesp)Cancho, Vicente G.Suzuki, Adriano K.Barriga, Gladys D. C. [UNESP]Espirito Santo, Ana P. J. do2020-12-10T17:39:43Z2020-12-10T17:39:43Z2020-08-11info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article19http://dx.doi.org/10.1080/03610918.2020.1806323Communications In Statistics-simulation And Computation. Philadelphia: Taylor & Francis Inc, 19 p., 2020.0361-0918http://hdl.handle.net/11449/19559010.1080/03610918.2020.1806323WOS:000558961300001Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengCommunications In Statistics-simulation And Computationinfo:eu-repo/semantics/openAccess2021-10-23T09:55:35Zoai:repositorio.unesp.br:11449/195590Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:13:36.015135Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A multivariate survival model induced by discrete frailty |
title |
A multivariate survival model induced by discrete frailty |
spellingShingle |
A multivariate survival model induced by discrete frailty Cancho, Vicente G. Cured fraction Frailty models Gibbs sampling Multivariate survival models |
title_short |
A multivariate survival model induced by discrete frailty |
title_full |
A multivariate survival model induced by discrete frailty |
title_fullStr |
A multivariate survival model induced by discrete frailty |
title_full_unstemmed |
A multivariate survival model induced by discrete frailty |
title_sort |
A multivariate survival model induced by discrete frailty |
author |
Cancho, Vicente G. |
author_facet |
Cancho, Vicente G. Suzuki, Adriano K. Barriga, Gladys D. C. [UNESP] Espirito Santo, Ana P. J. do |
author_role |
author |
author2 |
Suzuki, Adriano K. Barriga, Gladys D. C. [UNESP] Espirito Santo, Ana P. J. do |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade de São Paulo (USP) Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Cancho, Vicente G. Suzuki, Adriano K. Barriga, Gladys D. C. [UNESP] Espirito Santo, Ana P. J. do |
dc.subject.por.fl_str_mv |
Cured fraction Frailty models Gibbs sampling Multivariate survival models |
topic |
Cured fraction Frailty models Gibbs sampling Multivariate survival models |
description |
Frailty models are generally used to model heterogeneity and dependence between individuals. The distribution of the frailty variable is often assumed to be continuous. However, there are situations where a discretely-distributed frailty may be appropriate. On the other hand, when a cure rate is present in survival data, these continuous distributions may not be appropriate since individuals with long-term survival times encompass zero frailty. Having zero frailty can be interpreted as being immune or cured (long-term survivors). In this paper, we propose a new multivariate survival model induced by frailty for multivariate lifetime data in the presence of surviving fractions and examine some of its properties. The inferential approach exploits the Bayesian methods. We provide some simulation results to assess the performance of the proposed model. Furthermore, we illustrate the performance of the proposed model by means of a real data set. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-10T17:39:43Z 2020-12-10T17:39:43Z 2020-08-11 |
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/10.1080/03610918.2020.1806323 Communications In Statistics-simulation And Computation. Philadelphia: Taylor & Francis Inc, 19 p., 2020. 0361-0918 http://hdl.handle.net/11449/195590 10.1080/03610918.2020.1806323 WOS:000558961300001 |
url |
http://dx.doi.org/10.1080/03610918.2020.1806323 http://hdl.handle.net/11449/195590 |
identifier_str_mv |
Communications In Statistics-simulation And Computation. Philadelphia: Taylor & Francis Inc, 19 p., 2020. 0361-0918 10.1080/03610918.2020.1806323 WOS:000558961300001 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Communications In Statistics-simulation And Computation |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
19 |
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_ |
1808128620755943424 |