Nonparametric covariate hypothesis tests for the cure rate in mixture cure models

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UDC.coleccionInvestigaciónes_ES
UDC.departamentoMatemáticases_ES
UDC.endPage2307es_ES
UDC.grupoInvModelización, Optimización e Inferencia Estatística (MODES)es_ES
UDC.issue7es_ES
UDC.journalTitleStatistics in Medicinees_ES
UDC.startPage2291es_ES
UDC.volume39es_ES
dc.contributor.authorLópez-Cheda, Ana
dc.contributor.authorJácome, M. A.
dc.contributor.authorKeilegom, Ingrid Van
dc.contributor.authorCao, Ricardo
dc.date.accessioned2023-12-22T13:31:41Z
dc.date.available2023-12-22T13:31:41Z
dc.date.issued2020-06
dc.descriptionThis article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.es_ES
dc.description.abstract[Abstract]: In lifetime data, like cancer studies, there may be long term survivors, which lead to heavy censoring at the end of the follow-up period. Since a standard survival model is not appropriate to handle these data, a cure model is needed. In the literature, covariate hypothesis tests for cure models are limited to parametric and semiparametric methods. We fill this important gap by proposing a nonparametric covariate hypothesis test for the probability of cure in mixture cure models. A bootstrap method is proposed to approximate the null distribution of the test statistic. The procedure can be applied to any type of covariate, and could be extended to the multivariate setting. Its efficiency is evaluated in a Monte Carlo simulation study. Finally, the method is applied to a colorectal cancer dataset.es_ES
dc.description.sponsorshipA.L.-C.'s research was sponsored by the Beatriz Galindo Junior Spanish Grant (code BEAGAL18/00143) from MICINN (Ministerio de Ciencia, Innovación y Universidades) with reference BGP18/00154, and by the Spanish FPU (Formación de Profesorado Universitario) Grant FPU13/01371 from MECD (Ministerio de Educación, Cultura y Deporte). All the authors acknowledge partial support by the MINECO (Ministerio de Economía y Competitividad) Grant MTM2014-52876-R (EU ERDF support included) and the MICINN (Ministerio de Ciencia, Innovación y Universidades) Grant MTM2017-82724-R (EU ERDF support included). A.L-C., M.A.J., and R.C. acknowledge partial support of Xunta de Galicia (Centro Singular de Investigación de Galicia accreditation ED431G/01 2016-2019 and Grupos de Referencia Competitiva CN2012/130 and ED431C2016-015) and the European Union (European Regional Development Fund - ERDF). Financial support from the European Research Council (2016-2021, Horizon 2020 / ERC grant agreement No. 694409) for I.V.K. is gratefully acknowledged. The authors are grateful to Dr. S. Pértega and Dr. S. Pita, at the University Hospital of A Coruña, for providing the colorectal cancer dataset, and to two anonymous reviewers whose suggestions were very helpful to improve this article.es_ES
dc.description.sponsorshipXunta de Galicia; ED431G/01es_ES
dc.description.sponsorshipXunta de Galicia; CN2012/130es_ES
dc.description.sponsorshipXunta de Galicia; ED431C-2016-015es_ES
dc.identifier.citationA. López-Cheda, M. A. Jácome, I. Van Keilegom, y R. Cao, «Nonparametric covariate hypothesis tests for the cure rate in mixture cure models», Statistics in Medicine, vol. 39, n.º 17, pp. 2291-2307, 2020, doi: 10.1002/sim.8530.es_ES
dc.identifier.issn1097-0258
dc.identifier.urihttp://hdl.handle.net/2183/34612
dc.language.isoenges_ES
dc.publisherJohn Wiley & Sonses_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/MECD/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/FPU13%2F01371/ES/es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2014-52876-R/ES/INFERENCIA ESTADISTICA COMPLEJA Y DE ALTA DIMENSION: EN GENOMICA, NEUROCIENCIA, ONCOLOGIA, MATERIALES COMPLEJOS, MALHERBOLOGIA, MEDIO AMBIENTE, ENERGIA Y APLICACIONES INDUSTRIALESes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/MTM2017-82724-R/ES/INFERENCIA ESTADISTICA FLEXIBLE PARA DATOS COMPLEJOS DE GRAN VOLUMEN Y DE ALTA DIMENSIONes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/694409es_ES
dc.relation.urihttps://doi.org/10.1002/sim.8530es_ES
dc.rights© 2020 John Wiley & Sons, Ltd.2291es_ES
dc.rights.accessRightsopen accesses_ES
dc.subjectBootstrapes_ES
dc.subjectCensored dataes_ES
dc.subjectCure modelses_ES
dc.subjectHypothesis testses_ES
dc.subjectSurvival analysises_ES
dc.titleNonparametric covariate hypothesis tests for the cure rate in mixture cure modelses_ES
dc.typejournal articlees_ES
dspace.entity.typePublication
relation.isAuthorOfPublication811e9787-a857-4c18-8295-268b4014b4bc
relation.isAuthorOfPublicatione629ebcc-3475-4638-b4e7-bf3e786f997c
relation.isAuthorOfPublication3360aaca-39be-43b4-a458-974e79cdbc6b
relation.isAuthorOfPublication.latestForDiscovery811e9787-a857-4c18-8295-268b4014b4bc

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