Indirect inference for survival data

In this paper we describe the so-called “indirect” method of inference, originally developed from the econometric literature, and apply it to survival analyses of two data sets with repeated events. This method is often more convenient computationally than maximum likelihood estimation when handling...

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Detalhes bibliográficos
Autores: Turnbull, Bruce W., Jiang, Wenxin
Tipo de documento: artigo
Data de publicação:2003
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositório:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglês
OAI Identifier:oai:upcommons.upc.edu:2099/3730
Acesso em linha:https://hdl.handle.net/2099/3730
Access Level:Acceso aberto
Palavra-chave:Survival Analysis
Statistics
Estadística
Aplicacions (Matemàtica)
Classificació AMS::62 Statistics::62N Survival analysis and censored data
Classificació AMS::62 Statistics::62P Applications
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spelling Indirect inference for survival dataTurnbull, Bruce W.Jiang, WenxinSurvival AnalysisStatisticsEstadísticaAplicacions (Matemàtica)Classificació AMS::62 Statistics::62N Survival analysis and censored dataClassificació AMS::62 Statistics::62P ApplicationsIn this paper we describe the so-called “indirect” method of inference, originally developed from the econometric literature, and apply it to survival analyses of two data sets with repeated events. This method is often more convenient computationally than maximum likelihood estimation when handling such model complexities as random effects and measurement error, for example; and it can also serve as a basis for robust inference with less stringent assumptions on the data generating mechanism. The first data set concerns recurrence times of mammary tumors in rats and is modeled using a Poisson process model with covariates and frailties. The second data set involves times of recurrences of skin tumors in individual patients in a clinical trial. The methodology is applied in both parametric and semi-parametric regression analyses to accommodate random effects and covariate measurement error.Peer ReviewedInstitut d'Estadística de Catalunya20032003-01-0120072007-11-12journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2099/3730reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivs 2.5 Spainhttp://creativecommons.org/licenses/by-nc-nd/2.5/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2099/37302026-05-27T15:37:01Z
dc.title.none.fl_str_mv Indirect inference for survival data
title Indirect inference for survival data
spellingShingle Indirect inference for survival data
Turnbull, Bruce W.
Survival Analysis
Statistics
Estadística
Aplicacions (Matemàtica)
Classificació AMS::62 Statistics::62N Survival analysis and censored data
Classificació AMS::62 Statistics::62P Applications
title_short Indirect inference for survival data
title_full Indirect inference for survival data
title_fullStr Indirect inference for survival data
title_full_unstemmed Indirect inference for survival data
title_sort Indirect inference for survival data
dc.creator.none.fl_str_mv Turnbull, Bruce W.
Jiang, Wenxin
author Turnbull, Bruce W.
author_facet Turnbull, Bruce W.
Jiang, Wenxin
author_role author
author2 Jiang, Wenxin
author2_role author
dc.subject.none.fl_str_mv Survival Analysis
Statistics
Estadística
Aplicacions (Matemàtica)
Classificació AMS::62 Statistics::62N Survival analysis and censored data
Classificació AMS::62 Statistics::62P Applications
topic Survival Analysis
Statistics
Estadística
Aplicacions (Matemàtica)
Classificació AMS::62 Statistics::62N Survival analysis and censored data
Classificació AMS::62 Statistics::62P Applications
description In this paper we describe the so-called “indirect” method of inference, originally developed from the econometric literature, and apply it to survival analyses of two data sets with repeated events. This method is often more convenient computationally than maximum likelihood estimation when handling such model complexities as random effects and measurement error, for example; and it can also serve as a basis for robust inference with less stringent assumptions on the data generating mechanism. The first data set concerns recurrence times of mammary tumors in rats and is modeled using a Poisson process model with covariates and frailties. The second data set involves times of recurrences of skin tumors in individual patients in a clinical trial. The methodology is applied in both parametric and semi-parametric regression analyses to accommodate random effects and covariate measurement error.
publishDate 2003
dc.date.none.fl_str_mv 2003
2003-01-01
2007
2007-11-12
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2099/3730
url https://hdl.handle.net/2099/3730
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 2.5 Spain
http://creativecommons.org/licenses/by-nc-nd/2.5/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 2.5 Spain
http://creativecommons.org/licenses/by-nc-nd/2.5/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Institut d'Estadística de Catalunya
publisher.none.fl_str_mv Institut d'Estadística de Catalunya
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.301603

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