A Nonparametric Mixture Model for Cure Rate Estimation
Article first published online: 25 MAY 2004
Volume 56, Issue 1, pages 237–243, March 2000
How to Cite
Peng, Y. and Dear, K. B. G. (2000), A Nonparametric Mixture Model for Cure Rate Estimation. Biometrics, 56: 237–243. doi: 10.1111/j.0006-341X.2000.00237.x
- Issue published online: 25 MAY 2004
- Article first published online: 25 MAY 2004
- Received December 1997. Revised June 1999. Accepted June 1999.
- Breast cancer;
- Censored data;
- EM algorithm;
- Logistic regression;
- Marginal likelihood;
- Proportional hazards assumption;
- Survival data
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.