Sufficient dimension reduction for longitudinally measured predictors

Autores
Pfeiffer, R. M.; Forzani, Liliana Maria; Bura, Efstathia
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We propose a method to combine several predictors (markers) that are measured repeatedly over time into a composite marker score without assuming a model and only requiring a mild condition on the predictor distribution. Assuming that the first and second moments of the predictors can be decomposed into a time and a marker component via a Kronecker product structure that accommodates the longitudinal nature of the predictors, we develop first-moment sufficient dimension reduction techniques to replace the original markers with linear transformations that contain sufficient information for the regression of the predictors on the outcome. These linear combinations can then be combined into a score that has better predictive performance than a score built under a general model that ignores the longitudinal structure of the data. Our methods can be applied to either continuous or categorical outcome measures. In simulations, we focus on binary outcomes and show that our method outperforms existing alternatives by using the AUC, the area under the receiver?operator characteristics (ROC) curve, as a summary measure of the discriminatory ability of a single continuous diagnostic marker for binary disease outcomes. Published 2011. This article is a US Government work and is in the public domain in the USA.
Fil: Pfeiffer, R. M.. National Cancer Institute; Estados Unidos
Fil: Forzani, Liliana Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina
Fil: Bura, Efstathia. George Washington University/department Of Statistics; Estados Unidos
Materia
Auc
Discrimination
Kronecker Product
Sliced Inverse Regression (Sir)
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:11336/84090

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spelling Sufficient dimension reduction for longitudinally measured predictorsPfeiffer, R. M.Forzani, Liliana MariaBura, EfstathiaAucDiscriminationKronecker ProductSliced Inverse Regression (Sir)https://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1We propose a method to combine several predictors (markers) that are measured repeatedly over time into a composite marker score without assuming a model and only requiring a mild condition on the predictor distribution. Assuming that the first and second moments of the predictors can be decomposed into a time and a marker component via a Kronecker product structure that accommodates the longitudinal nature of the predictors, we develop first-moment sufficient dimension reduction techniques to replace the original markers with linear transformations that contain sufficient information for the regression of the predictors on the outcome. These linear combinations can then be combined into a score that has better predictive performance than a score built under a general model that ignores the longitudinal structure of the data. Our methods can be applied to either continuous or categorical outcome measures. In simulations, we focus on binary outcomes and show that our method outperforms existing alternatives by using the AUC, the area under the receiver?operator characteristics (ROC) curve, as a summary measure of the discriminatory ability of a single continuous diagnostic marker for binary disease outcomes. Published 2011. This article is a US Government work and is in the public domain in the USA.Fil: Pfeiffer, R. M.. National Cancer Institute; Estados UnidosFil: Forzani, Liliana Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; ArgentinaFil: Bura, Efstathia. George Washington University/department Of Statistics; Estados UnidosJohn Wiley & Sons Ltd2012info: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/11336/84090Pfeiffer, R. M.; Forzani, Liliana Maria; Bura, Efstathia; Sufficient dimension reduction for longitudinally measured predictors; John Wiley & Sons Ltd; Statistics In Medicine; 31; 22; 2012; 2414-24270277-6715CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://onlinelibrary.wiley.com/doi/10.1002/sim.4437/abstractinfo:eu-repo/semantics/altIdentifier/doi/10.1002/sim.4437/abstractinfo:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5794228/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 Técnicas2025-09-29T09:38:03Zoai:ri.conicet.gov.ar:11336/84090instacron: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 09:38:03.411CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Sufficient dimension reduction for longitudinally measured predictors
title Sufficient dimension reduction for longitudinally measured predictors
spellingShingle Sufficient dimension reduction for longitudinally measured predictors
Pfeiffer, R. M.
Auc
Discrimination
Kronecker Product
Sliced Inverse Regression (Sir)
title_short Sufficient dimension reduction for longitudinally measured predictors
title_full Sufficient dimension reduction for longitudinally measured predictors
title_fullStr Sufficient dimension reduction for longitudinally measured predictors
title_full_unstemmed Sufficient dimension reduction for longitudinally measured predictors
title_sort Sufficient dimension reduction for longitudinally measured predictors
dc.creator.none.fl_str_mv Pfeiffer, R. M.
Forzani, Liliana Maria
Bura, Efstathia
author Pfeiffer, R. M.
author_facet Pfeiffer, R. M.
Forzani, Liliana Maria
Bura, Efstathia
author_role author
author2 Forzani, Liliana Maria
Bura, Efstathia
author2_role author
author
dc.subject.none.fl_str_mv Auc
Discrimination
Kronecker Product
Sliced Inverse Regression (Sir)
topic Auc
Discrimination
Kronecker Product
Sliced Inverse Regression (Sir)
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We propose a method to combine several predictors (markers) that are measured repeatedly over time into a composite marker score without assuming a model and only requiring a mild condition on the predictor distribution. Assuming that the first and second moments of the predictors can be decomposed into a time and a marker component via a Kronecker product structure that accommodates the longitudinal nature of the predictors, we develop first-moment sufficient dimension reduction techniques to replace the original markers with linear transformations that contain sufficient information for the regression of the predictors on the outcome. These linear combinations can then be combined into a score that has better predictive performance than a score built under a general model that ignores the longitudinal structure of the data. Our methods can be applied to either continuous or categorical outcome measures. In simulations, we focus on binary outcomes and show that our method outperforms existing alternatives by using the AUC, the area under the receiver?operator characteristics (ROC) curve, as a summary measure of the discriminatory ability of a single continuous diagnostic marker for binary disease outcomes. Published 2011. This article is a US Government work and is in the public domain in the USA.
Fil: Pfeiffer, R. M.. National Cancer Institute; Estados Unidos
Fil: Forzani, Liliana Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina
Fil: Bura, Efstathia. George Washington University/department Of Statistics; Estados Unidos
description We propose a method to combine several predictors (markers) that are measured repeatedly over time into a composite marker score without assuming a model and only requiring a mild condition on the predictor distribution. Assuming that the first and second moments of the predictors can be decomposed into a time and a marker component via a Kronecker product structure that accommodates the longitudinal nature of the predictors, we develop first-moment sufficient dimension reduction techniques to replace the original markers with linear transformations that contain sufficient information for the regression of the predictors on the outcome. These linear combinations can then be combined into a score that has better predictive performance than a score built under a general model that ignores the longitudinal structure of the data. Our methods can be applied to either continuous or categorical outcome measures. In simulations, we focus on binary outcomes and show that our method outperforms existing alternatives by using the AUC, the area under the receiver?operator characteristics (ROC) curve, as a summary measure of the discriminatory ability of a single continuous diagnostic marker for binary disease outcomes. Published 2011. This article is a US Government work and is in the public domain in the USA.
publishDate 2012
dc.date.none.fl_str_mv 2012
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/11336/84090
Pfeiffer, R. M.; Forzani, Liliana Maria; Bura, Efstathia; Sufficient dimension reduction for longitudinally measured predictors; John Wiley & Sons Ltd; Statistics In Medicine; 31; 22; 2012; 2414-2427
0277-6715
CONICET Digital
CONICET
url http://hdl.handle.net/11336/84090
identifier_str_mv Pfeiffer, R. M.; Forzani, Liliana Maria; Bura, Efstathia; Sufficient dimension reduction for longitudinally measured predictors; John Wiley & Sons Ltd; Statistics In Medicine; 31; 22; 2012; 2414-2427
0277-6715
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://onlinelibrary.wiley.com/doi/10.1002/sim.4437/abstract
info:eu-repo/semantics/altIdentifier/doi/10.1002/sim.4437/abstract
info:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5794228/
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 John Wiley & Sons Ltd
publisher.none.fl_str_mv John Wiley & Sons Ltd
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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