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Nonparametric Kernel Estimation of the Probability of Cure in a Mixture Cure Model when the Cure Status is Partially Observed
dc.contributor.author | Safari, Wende Clarence | |
dc.contributor.author | López-de-Ullibarri, Ignacio | |
dc.contributor.author | Jácome, M. A. | |
dc.date.accessioned | 2023-12-26T09:45:05Z | |
dc.date.available | 2023-12-26T09:45:05Z | |
dc.date.issued | 2022-08-01 | |
dc.identifier.citation | Safari WC, López-de-Ullibarri I, Jácome MA. Nonparametric kernel estimation of the probability of cure in a mixture cure model when the cure status is partially observed. Statistical Methods in Medical Research. 2022;31(11):2164-2188. doi:10.1177/09622802221115880 | es_ES |
dc.identifier.issn | 0962-2802 | |
dc.identifier.issn | 1477-0334 | |
dc.identifier.uri | http://hdl.handle.net/2183/34624 | |
dc.description.abstract | [Abstract] Cure models are a class of time-to-event models where a proportion of individuals will never experience the event of interest. The lifetimes of these so-called cured individuals are always censored. It is usually assumed that one never knows which censored observation is cured and which is uncured, so the cure status is unknown for censored times. In this paper, we develop a method to estimate the probability of cure in the mixture cure model when some censored individuals are known to be cured. A cure probability estimator that incorporates the cure status information is introduced. This estimator is shown to be strongly consistent and asymptotically normally distributed. Two alternative estimators are also presented. The first one considers a competing risks approach with two types of competing events, the event of interest and the cure. The second alternative estimator is based on the fact that the probability of cure can be written as the conditional mean of the cure status. Hence, nonparametric regression methods can be applied to estimate this conditional mean. However, the cure status remains unknown for some censored individuals. Consequently, the application of regression methods in this context requires handling missing data in the response variable (cure status). Simulations are performed to evaluate the finite sample performance of the estimators, and we apply them to the analysis of two datasets related to survival of breast cancer patients and length of hospital stay of COVID-19 patients requiring intensive care. | es_ES |
dc.description.sponsorship | The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research/work has been supported by MICINN grant PID2020-113578RB-I00, and by the Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2020/14 and Centro de Investigación del Sistema universitario de Galicia ED431G 2019/01), all of them through the ERDF | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C-2020/14 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Sage | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113578RB-I00/ES/METODOS ESTADISTICOS FLEXIBLES EN CIENCIA DE DATOS PARA DATOS COMPLEJOS Y DE GRAN VOLUMEN: TEORIA Y APLICACIONES | es_ES |
dc.relation.uri | https://doi.org/10.1177/09622802221115880 | es_ES |
dc.subject | Bootstrap bandwidth | es_ES |
dc.subject | Censoring | es_ES |
dc.subject | Cure model | es_ES |
dc.subject | Cure status | es_ES |
dc.subject | Kernel estimators | es_ES |
dc.title | Nonparametric Kernel Estimation of the Probability of Cure in a Mixture Cure Model when the Cure Status is Partially Observed | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Statistical Methods in Medical Research | es_ES |
UDC.volume | 31 | es_ES |
UDC.issue | 11 | es_ES |
UDC.startPage | 2164 | es_ES |
UDC.endPage | 2188 | es_ES |
dc.identifier.doi | 10.1177/09622802221115880 |
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