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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/84090
Ver los metadatos del registro completo
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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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