Sufficient dimension reduction for censored predictors

Autores
Tomassi, Diego Rodolfo; Forzani, Liliana Maria; Bura, Efstathia; Pfeiffer, Ruth
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Motivated by a study conducted to evaluate the associations of 51 inflammatory markers and lung cancer risk, we propose several approaches of varying computational complexity for analyzing multiple correlated markers that are also censored due to lower and/or upper limits of detection, using likelihood-based sufficient dimension reduction (SDR) methods. We extend the theory and the likelihood-based SDR framework in two ways: (i) we accommodate censored predictors directly in the likelihood, and (ii) we incorporate variable selection. We find linear combinations that contain all the information that the correlated markers have on an outcome variable (i.e., are sufficient for modeling and prediction of the outcome) while accounting for censoring of the markers. These methods yield efficient estimators and can be applied to any type of outcome, including continuous and categorical. We illustrate and compare all methods using data from the motivating study and in simulations. We find that explicitly accounting for the censoring in the likelihood of the SDR methods can lead to appreciable gains in efficiency and prediction accuracy, and also outperformed multiple imputations combined with standard SDR.
Fil: Tomassi, Diego Rodolfo. Universidad Nacional del Litoral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: Forzani, Liliana Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Universidad Nacional del Litoral; Argentina
Fil: Bura, Efstathia. The George Washington University; Estados Unidos
Fil: Pfeiffer, Ruth. National Cancer Institute; Estados Unidos
Materia
Informative Missingness
Limits of Detection
Missing Data
Penalized Likelihood
Shrinkage
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/71171
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Sufficient dimension reduction for censored predictorsTomassi, Diego RodolfoForzani, Liliana MariaBura, EfstathiaPfeiffer, RuthInformative MissingnessLimits of DetectionMissing DataPenalized LikelihoodShrinkagehttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1Motivated by a study conducted to evaluate the associations of 51 inflammatory markers and lung cancer risk, we propose several approaches of varying computational complexity for analyzing multiple correlated markers that are also censored due to lower and/or upper limits of detection, using likelihood-based sufficient dimension reduction (SDR) methods. We extend the theory and the likelihood-based SDR framework in two ways: (i) we accommodate censored predictors directly in the likelihood, and (ii) we incorporate variable selection. We find linear combinations that contain all the information that the correlated markers have on an outcome variable (i.e., are sufficient for modeling and prediction of the outcome) while accounting for censoring of the markers. These methods yield efficient estimators and can be applied to any type of outcome, including continuous and categorical. We illustrate and compare all methods using data from the motivating study and in simulations. We find that explicitly accounting for the censoring in the likelihood of the SDR methods can lead to appreciable gains in efficiency and prediction accuracy, and also outperformed multiple imputations combined with standard SDR.Fil: Tomassi, Diego Rodolfo. Universidad Nacional del Litoral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Forzani, Liliana Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Universidad Nacional del Litoral; ArgentinaFil: Bura, Efstathia. The George Washington University; Estados UnidosFil: Pfeiffer, Ruth. National Cancer Institute; Estados UnidosWiley Blackwell Publishing, Inc2017-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/71171Tomassi, Diego Rodolfo; Forzani, Liliana Maria; Bura, Efstathia; Pfeiffer, Ruth; Sufficient dimension reduction for censored predictors; Wiley Blackwell Publishing, Inc; Biometrics; 73; 1; 3-2017; 220-2310006-341XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1111/biom.12556info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.12556info:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6543825/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:38:53Zoai:ri.conicet.gov.ar:11336/71171instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:38:53.653CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Sufficient dimension reduction for censored predictors
title Sufficient dimension reduction for censored predictors
spellingShingle Sufficient dimension reduction for censored predictors
Tomassi, Diego Rodolfo
Informative Missingness
Limits of Detection
Missing Data
Penalized Likelihood
Shrinkage
title_short Sufficient dimension reduction for censored predictors
title_full Sufficient dimension reduction for censored predictors
title_fullStr Sufficient dimension reduction for censored predictors
title_full_unstemmed Sufficient dimension reduction for censored predictors
title_sort Sufficient dimension reduction for censored predictors
dc.creator.none.fl_str_mv Tomassi, Diego Rodolfo
Forzani, Liliana Maria
Bura, Efstathia
Pfeiffer, Ruth
author Tomassi, Diego Rodolfo
author_facet Tomassi, Diego Rodolfo
Forzani, Liliana Maria
Bura, Efstathia
Pfeiffer, Ruth
author_role author
author2 Forzani, Liliana Maria
Bura, Efstathia
Pfeiffer, Ruth
author2_role author
author
author
dc.subject.none.fl_str_mv Informative Missingness
Limits of Detection
Missing Data
Penalized Likelihood
Shrinkage
topic Informative Missingness
Limits of Detection
Missing Data
Penalized Likelihood
Shrinkage
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Motivated by a study conducted to evaluate the associations of 51 inflammatory markers and lung cancer risk, we propose several approaches of varying computational complexity for analyzing multiple correlated markers that are also censored due to lower and/or upper limits of detection, using likelihood-based sufficient dimension reduction (SDR) methods. We extend the theory and the likelihood-based SDR framework in two ways: (i) we accommodate censored predictors directly in the likelihood, and (ii) we incorporate variable selection. We find linear combinations that contain all the information that the correlated markers have on an outcome variable (i.e., are sufficient for modeling and prediction of the outcome) while accounting for censoring of the markers. These methods yield efficient estimators and can be applied to any type of outcome, including continuous and categorical. We illustrate and compare all methods using data from the motivating study and in simulations. We find that explicitly accounting for the censoring in the likelihood of the SDR methods can lead to appreciable gains in efficiency and prediction accuracy, and also outperformed multiple imputations combined with standard SDR.
Fil: Tomassi, Diego Rodolfo. Universidad Nacional del Litoral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: Forzani, Liliana Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Universidad Nacional del Litoral; Argentina
Fil: Bura, Efstathia. The George Washington University; Estados Unidos
Fil: Pfeiffer, Ruth. National Cancer Institute; Estados Unidos
description Motivated by a study conducted to evaluate the associations of 51 inflammatory markers and lung cancer risk, we propose several approaches of varying computational complexity for analyzing multiple correlated markers that are also censored due to lower and/or upper limits of detection, using likelihood-based sufficient dimension reduction (SDR) methods. We extend the theory and the likelihood-based SDR framework in two ways: (i) we accommodate censored predictors directly in the likelihood, and (ii) we incorporate variable selection. We find linear combinations that contain all the information that the correlated markers have on an outcome variable (i.e., are sufficient for modeling and prediction of the outcome) while accounting for censoring of the markers. These methods yield efficient estimators and can be applied to any type of outcome, including continuous and categorical. We illustrate and compare all methods using data from the motivating study and in simulations. We find that explicitly accounting for the censoring in the likelihood of the SDR methods can lead to appreciable gains in efficiency and prediction accuracy, and also outperformed multiple imputations combined with standard SDR.
publishDate 2017
dc.date.none.fl_str_mv 2017-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/71171
Tomassi, Diego Rodolfo; Forzani, Liliana Maria; Bura, Efstathia; Pfeiffer, Ruth; Sufficient dimension reduction for censored predictors; Wiley Blackwell Publishing, Inc; Biometrics; 73; 1; 3-2017; 220-231
0006-341X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/71171
identifier_str_mv Tomassi, Diego Rodolfo; Forzani, Liliana Maria; Bura, Efstathia; Pfeiffer, Ruth; Sufficient dimension reduction for censored predictors; Wiley Blackwell Publishing, Inc; Biometrics; 73; 1; 3-2017; 220-231
0006-341X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1111/biom.12556
info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.12556
info:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6543825/
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Wiley Blackwell Publishing, Inc
publisher.none.fl_str_mv Wiley Blackwell Publishing, Inc
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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