miRecSurv package: Prentice-Williams-Peterson models with multiple imputation of unknown number of previous episodes

Left censoring can occur with relative frequency when analysing recurrent events in epi demiological 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 episod...

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Detalhes bibliográficos
Autores: Moriña, David, Hernández Herrera, Gilma, Navarro Giné, Albert
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Recursos:Universidad de Barcelona
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/183236
Acesso em linha:https://hdl.handle.net/2445/183236
Access Level:acceso abierto
Palavra-chave:Dependència (Estadística)
Imputació múltiple (Estadística)
Ciències de la salut
Investigació mèdica
Dependence (Statistics)
Multiple imputation (Statistics)
Medical sciences
Medicine research
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repository_id_str
spelling miRecSurv package: Prentice-Williams-Peterson models with multiple imputation of unknown number of previous episodesMoriña, DavidHernández Herrera, GilmaNavarro Giné, AlbertDependència (Estadística)Imputació múltiple (Estadística)Ciències de la salutInvestigació mèdicaDependence (Statistics)Multiple imputation (Statistics)Medical sciencesMedicine researchLeft censoring can occur with relative frequency when analysing recurrent events in epi demiological 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-, suband 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 a occupational cohort study and a simulation study.The R Foundation2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2445/183236Articles publicats en revistes (Econometria, Estadística i Economia Aplicada)reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésReproducció del document publicat a: https://journal.r-project.org/archive/2021/RJ-2021-082/index.htmlThe R Journal, 2021, vol. 13, num. 2, p. 419-426cc-by (c) Moriña, David et al., 2021https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/1832362026-05-27T06:46:51Z
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, David
Dependència (Estadística)
Imputació múltiple (Estadística)
Ciències de la salut
Investigació mèdica
Dependence (Statistics)
Multiple imputation (Statistics)
Medical sciences
Medicine research
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, David
Hernández Herrera, Gilma
Navarro Giné, Albert
author Moriña, David
author_facet Moriña, David
Hernández Herrera, Gilma
Navarro Giné, Albert
author_role author
author2 Hernández Herrera, Gilma
Navarro Giné, Albert
author2_role author
author
dc.subject.none.fl_str_mv Dependència (Estadística)
Imputació múltiple (Estadística)
Ciències de la salut
Investigació mèdica
Dependence (Statistics)
Multiple imputation (Statistics)
Medical sciences
Medicine research
topic Dependència (Estadística)
Imputació múltiple (Estadística)
Ciències de la salut
Investigació mèdica
Dependence (Statistics)
Multiple imputation (Statistics)
Medical sciences
Medicine research
description Left censoring can occur with relative frequency when analysing recurrent events in epi demiological 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-, suband 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 a occupational cohort study and a simulation study.
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 https://hdl.handle.net/2445/183236
url https://hdl.handle.net/2445/183236
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://journal.r-project.org/archive/2021/RJ-2021-082/index.html
The R Journal, 2021, vol. 13, num. 2, p. 419-426
dc.rights.none.fl_str_mv cc-by (c) Moriña, David et al., 2021
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Moriña, David et al., 2021
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv The R Foundation
publisher.none.fl_str_mv The R Foundation
dc.source.none.fl_str_mv Articles publicats en revistes (Econometria, Estadística i Economia Aplicada)
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869417829002903552
score 15,300719

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