miRecSurv Package: Prentice-Williams-Peterson Models with Multiple Imputation of Unknown Number of Previous Episodes

Left censoring can occur with relative frequency when analyzing recurrent events in epidemiological studies, especially observational ones. Concretely, the inclusion of individuals that were already at risk before the effective initiation in a cohort study may cause the unawareness of prior episodes...

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Detalles Bibliográficos
Autores: Moriña, D., Hernández-Herrera, G., Navarro, A.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2072/531301
Acceso en línea:http://hdl.handle.net/2072/531301
Access Level:acceso abierto
Palabra clave:Informàtica - Paquet R
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spelling miRecSurv Package: Prentice-Williams-Peterson Models with Multiple Imputation of Unknown Number of Previous EpisodesMoriña, D.Hernández-Herrera, G.Navarro, A.Informàtica - Paquet R004Left censoring can occur with relative frequency when analyzing recurrent events in epidemiological studies, especially observational ones. Concretely, the inclusion of individuals that were already at risk before the effective initiation in a cohort study may cause the unawareness of prior episodes that have already been experienced, and this will easily lead to biased and inefficient estimates. The miRecSurv package is based on the use of models with specific baseline hazard, with multiple imputation of the number of prior episodes when unknown by means of the COMPoisson distribution, a very flexible count distribution that can handle over, sub, and equidispersion, with a stratified model depending on whether the individual had or had not previously been at risk, and the use of a frailty term. The usage of the package is illustrated by means of a real data example based on an occupational cohort study and a simulation study. © 2021, R Journal. All Rights Reserved.Technische Universitaet Wien2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion8 p.application/pdfhttp://hdl.handle.net/2072/531301RECERCAT (Dipòsit de la Recerca de Catalunya)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésR JournalL'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons: https://creativecommons.org/licenses/by/4.0/L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons: https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:2072/5313012026-05-29T05:05:01Z
dc.title.none.fl_str_mv miRecSurv Package: Prentice-Williams-Peterson Models with Multiple Imputation of Unknown Number of Previous Episodes
title miRecSurv Package: Prentice-Williams-Peterson Models with Multiple Imputation of Unknown Number of Previous Episodes
spellingShingle miRecSurv Package: Prentice-Williams-Peterson Models with Multiple Imputation of Unknown Number of Previous Episodes
Moriña, D.
Informàtica - Paquet R
004
title_short miRecSurv Package: Prentice-Williams-Peterson Models with Multiple Imputation of Unknown Number of Previous Episodes
title_full miRecSurv Package: Prentice-Williams-Peterson Models with Multiple Imputation of Unknown Number of Previous Episodes
title_fullStr miRecSurv Package: Prentice-Williams-Peterson Models with Multiple Imputation of Unknown Number of Previous Episodes
title_full_unstemmed miRecSurv Package: Prentice-Williams-Peterson Models with Multiple Imputation of Unknown Number of Previous Episodes
title_sort miRecSurv Package: Prentice-Williams-Peterson Models with Multiple Imputation of Unknown Number of Previous Episodes
dc.creator.none.fl_str_mv Moriña, D.
Hernández-Herrera, G.
Navarro, A.
author Moriña, D.
author_facet Moriña, D.
Hernández-Herrera, G.
Navarro, A.
author_role author
author2 Hernández-Herrera, G.
Navarro, A.
author2_role author
author
dc.subject.none.fl_str_mv Informàtica - Paquet R
004
topic Informàtica - Paquet R
004
description Left censoring can occur with relative frequency when analyzing recurrent events in epidemiological studies, especially observational ones. Concretely, the inclusion of individuals that were already at risk before the effective initiation in a cohort study may cause the unawareness of prior episodes that have already been experienced, and this will easily lead to biased and inefficient estimates. The miRecSurv package is based on the use of models with specific baseline hazard, with multiple imputation of the number of prior episodes when unknown by means of the COMPoisson distribution, a very flexible count distribution that can handle over, sub, and equidispersion, with a stratified model depending on whether the individual had or had not previously been at risk, and the use of a frailty term. The usage of the package is illustrated by means of a real data example based on an occupational cohort study and a simulation study. © 2021, R Journal. All Rights Reserved.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/2072/531301
url http://hdl.handle.net/2072/531301
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv R Journal
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 8 p.
application/pdf
dc.publisher.none.fl_str_mv Technische Universitaet Wien
publisher.none.fl_str_mv Technische Universitaet Wien
dc.source.none.fl_str_mv RECERCAT (Dipòsit de la Recerca de Catalunya)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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