Nonparametric Inference in Mixture Cure Models

Proceedings

Jácome, Mª Amalia¹  López-Cheda, Ana²  Cao, Ricardo³  Keilegom, Ingrid Van⁴ 

[1] Author to whom correspondence should be addressed.;Department of Mathematics, University of A Coruña, 15071 A Coruña, Spain;ORSTAT, KU Leuven, 3000 Leuven, Belgium;Presented at the XoveTIC Congress, A Coruña, Spain, 27–28 September 2018.

关键词: b;    width selection;    bootstrap;    censored data;    kernel estimation;    survival analysis;   

DOI  :  10.3390/proceedings2181181

学科分类：社会科学、人文和艺术（综合）

来源: mdpi

【 摘 要 】

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).

【 授权许可】

CC BY