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A Nonparametric Mixture Model for Cure Rate Estimation
Author(s) -
Peng Yingwei,
Dear Keith B. G.
Publication year - 2000
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2000.00237.x
Subject(s) - covariate , nonparametric statistics , proportional hazards model , parametric statistics , econometrics , statistics , parametric model , nonparametric regression , semiparametric model , estimation , semiparametric regression , accelerated failure time model , regression analysis , mixture model , computer science , mathematics , engineering , systems engineering
Summary. Nonparametric methods have attracted less attention than their parametric counterparts for cure rate analysis. In this paper, we study a general nonparametric mixture model. The proportional hazards assumption is employed in modeling the effect of covariates on the failure time of patients who are not cured. The EM algorithm, the marginal likelihood approach, and multiple imputations are employed to estimate parameters of interest in the model. This model extends models and improves estimation methods proposed by other researchers. It also extends Cox's proportional hazards regression model by allowing a proportion of event‐free patients and investigating covariate effects on that proportion. The model and its estimation method are investigated by simulations. An application to breast cancer data, including comparisons with previous analyses using a parametric model and an existing nonparametric model by other researchers, confirms the conclusions from the parametric model but not those from the existing nonparametric model.