Nonparametric Inference in Mixture Cure Models
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Nonparametric Inference in Mixture Cure ModelsData
2018-09Cita bibliográfica
A. López-Cheda, R. Cao, M. A. Jácome, y I. V. Keilegom, «Nonparametric Inference in Mixture Cure Models», Proceedings, vol. 2, n.º 18, Art. n.º 18, 2018, doi: 10.3390/proceedings2181181.
Resumo
[Abstract]: A completely nonparametric method for the estimation of mixture cure models is proposed. Nonparametric estimators for the cure probability (incidence) and for the survival function of the uncured population (latency) are introduced. In addition, a bootstrap bandwidth selection method for each nonparametric estimator is considered. The methodology is applied to a dataset of colorectal cancer patients from the University Hospital of A Coruña (CHUAC). Furthermore, a nonparametric covariate significance test for the incidence is proposed. The test is extended to non-continuous covariates: binary, discrete and qualitative, and also to contexts with a large number of covariates. The method is applied to a sarcomas dataset from the University Hospital of Santiago (CHUS).
Palabras chave
Bandwidth selection
Bootstrap
Censored data
Kernel estimation
Survival analysis
Bootstrap
Censored data
Kernel estimation
Survival analysis
Descrición
Presented at the XoveTIC Congress, A Coruña, Spain, 27–28 September 2018.
Versión do editor
Dereitos
Atribución 3.0 España
ISSN
2504-3900