This work studies a proportional hazards model for survival data with ＂long-term survivors＂, in which covariates are subject to linear measurement error. It is well known that the naive estimators from both partial ...This work studies a proportional hazards model for survival data with ＂long-term survivors＂, in which covariates are subject to linear measurement error. It is well known that the naive estimators from both partial and full likelihood methods are inconsistent under this measurement error model. For measurement error models, methods of unbiased estimating function and corrected likelihood have been proposed in the literature. In this paper, we apply the corrected partial and full likelihood approaches to estimate the model and obtain statistical inference from survival data with long-term survivors. The asymptotic properties of the estimators are established. Simulation results illustrate that the proposed approaches provide useful tools for the models considered.展开更多
基金supported by the National Nature Science Foundation of China under Grant No.10871084Macquarie University Safety Net grant
文摘This work studies a proportional hazards model for survival data with ＂long-term survivors＂, in which covariates are subject to linear measurement error. It is well known that the naive estimators from both partial and full likelihood methods are inconsistent under this measurement error model. For measurement error models, methods of unbiased estimating function and corrected likelihood have been proposed in the literature. In this paper, we apply the corrected partial and full likelihood approaches to estimate the model and obtain statistical inference from survival data with long-term survivors. The asymptotic properties of the estimators are established. Simulation results illustrate that the proposed approaches provide useful tools for the models considered.
