Show simple item record

dc.contributor.authorDe la Cruz, R.
dc.contributor.authorFuentes, C.
dc.contributor.authorMeza, C.
dc.contributor.authorLee, D.-J. 
dc.contributor.authorArribas-Gil, A.
dc.dateinfo:eu-repo/date/embargoEnd/2018-02-06en_US
dc.date.accessioned2017-02-09T16:16:17Z
dc.date.available2017-02-09T16:16:17Z
dc.date.issued2017-02-09
dc.identifier.issn0277-6715
dc.identifier.urihttp://hdl.handle.net/20.500.11824/361
dc.description.abstractWe propose a semiparametric mixed–effects model (SNMM) using penalized splines to clas- sify longitudinal data and improve the prediction of a binary outcome. The work is motivated by a study in which different hormone levels were measured during the early stages of preg- nancy, and the challenge is using this information to predict normal versus abnormal pregnancy outcomes. The aim of this paper is to compare models and estimation strategies based on alternative formulations of SNMMs depending on the characteristics of the data set under con- sideration. For our motivating example, we address the classification problem using a particular case of the SNMM in which the parameter space has a finite dimensional component (fixed effects and variance components) and an infinite dimensional component (unknown function) that need to be estimated. The nonparametric component of the model is estimated using pe- nalized splines. For the parametric component, we compare the advantages of using random effects versus direct modeling of the correlation structure of the errors. Numerical studies show that our approach improves over other existing methods for the analysis of this type of data. Furthermore, the results obtained using our method support the idea that explicit modeling of the serial correlation of the error term improves the prediction accuracy with respect to a model with random effects, but independent errors.en_US
dc.description.sponsorshipMTM2014-52184-Pen_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectClassificacion modelsen_US
dc.subjectCorrelated observationsen_US
dc.subjectLongitudinal dataen_US
dc.subjectMixed-effects modelsen_US
dc.subjectP-splinesen_US
dc.titlePredicting Pregnancy Outcomes Using Longitudinal Information: A Penalized Splines Mixed–Effects Model Approachen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.identifier.doi10.1002/sim.7256
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/644202en_US
dc.relation.projectIDES/1PE/SEV-2013-0323en_US
dc.relation.projectIDEUS/BERC/BERC.2014-2017en_US
dc.relation.projectIDEUS/ELKARTEKen_US
dc.rights.accessRightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersionen_US
dc.journal.titleStatistics in Medicineen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España