A two‐step estimation procedure for semiparametric mixture cure models
针对小样本下混合治愈模型的最大似然估计不理想的问题，提出先构造非参数估计再投影到参数类的两步法，在逻辑斯蒂-考克斯模型中表现更优。
Abstract In survival analysis, cure models have been developed to account for the presence of cured subjects that will never experience the event of interest. Mixture cure models with a parametric model for the incidence and a semiparametric model for the survival of the susceptibles are particularly common in practice. Because of the latent cure status, maximum likelihood estimation is performed via the iterative EM algorithm. Here, we focus on the cure probabilities and propose a two‐step procedure to improve upon the maximum likelihood estimator when the sample size is not large. The new method is based on presmoothing by first constructing a nonparametric estimator and then projecting it on the desired parametric class. We investigate the theoretical properties of the resulting estimator and show through an extensive simulation study for the logistic‐Cox model that it outperforms the existing method. Practical use of the method is illustrated through two melanoma datasets.