论文标题
混合固化模型与不粘结的协变量的模拟驱除方法
A simulation-extrapolation approach for the mixture cure model with mismeasured covariates
论文作者
论文摘要
我们考虑在缺乏协变量的情况下，来自患有治愈受试者的人群的生存数据。我们使用混合疗法模型来解释永远不会经历事件的个体，同时区分协变量对治疗概率和生存时间的影响。特别是，对于实际应用，似乎很感兴趣的是发病率的后勤形式和延迟的COX比例危害模型。为了纠正测量误差引入的偏差的估计器，我们使用SIMEX算法，这是一种非常一般的基于仿真的方法。从本质上讲，它通过向数据引入额外错误，然后通过推断方法恢复校正后的估计器来估计这种偏见。当已知真实的外推函数时，估计器被证明是一致的，渐近地正态分布。我们通过模拟研究研究了他们的有限样本性能，并应用了提出的方法来分析前列腺特异性抗原（PSA）对前列腺癌患者的影响。
We consider survival data from a population with cured subjects in the presence of mismeasured covariates. We use the mixture cure model to account for the individuals that will never experience the event and at the same time distinguish between the effect of the covariates on the cure probabilities and on survival times. In particular, for practical applications, it seems of interest to assume a logistic form of the incidence and a Cox proportional hazards model for the latency. To correct the estimators for the bias introduced by the measurement error, we use the simex algorithm, which is a very general simulation based method. It essentially estimates this bias by introducing additional error to the data and then recovers bias corrected estimators through an extrapolation approach. The estimators are shown to be consistent and asymptotically normally distributed when the true extrapolation function is known. We investigate their finite sample performance through a simulation study and apply the proposed method to analyse the effect of the prostate specific antigen (PSA) on patients with prostate cancer.
