文章基本信息

标题：A simulation-extrapolation approach for the mixture cure model with mismeasured covariates
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作者：Eni Musta ; Ingrid Van Keilegom
期刊名称：Electronic Journal of Statistics
印刷版ISSN：1935-7524
出版年度：2021
卷号：15
期号：2
页码：3708-3742
DOI：10.1214/21-EJS1874
语种：English
出版社：Institute of Mathematical Statistics
摘要：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.
关键词：62N02; cure models; logistic model; measurement error; simex algorithm; Survival analysis