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dc.contributor.author
Pfeiffer, R. M.
dc.contributor.author
Forzani, Liliana Maria
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dc.contributor.author
Bura, Efstathia
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dc.date.available
2019-09-23T13:27:44Z
dc.date.issued
2012
dc.identifier.citation
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
dc.identifier.issn
0277-6715
dc.identifier.uri
http://hdl.handle.net/11336/84090
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
John Wiley & Sons Ltd
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Auc
dc.subject
Discrimination
dc.subject
Kronecker Product
dc.subject
Sliced Inverse Regression (Sir)
dc.subject.classification
Matemática Pura
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dc.subject.classification
Matemáticas
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
Sufficient dimension reduction for longitudinally measured predictors
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2019-09-20T14:16:01Z
dc.journal.volume
31
dc.journal.number
22
dc.journal.pagination
2414-2427
dc.journal.pais
Reino Unido
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dc.journal.ciudad
LOndres
dc.description.fil
Fil: Pfeiffer, R. M.. National Cancer Institute; Estados Unidos
dc.description.fil
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
dc.description.fil
Fil: Bura, Efstathia. George Washington University/department Of Statistics; Estados Unidos
dc.journal.title
Statistics In Medicine
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://onlinelibrary.wiley.com/doi/10.1002/sim.4437/abstract
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1002/sim.4437/abstract
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5794228/
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