2020/09/13 by Eni Musta, Musta, Eni, Ingrid Van Keilegom +1
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Distribution Estimation and Applications #Statistical Methods and Bayesian Inference
paper · pdf · doi:10.48550/arxiv.2009.06094
openalex publication_date 2020/09/13 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
We consider survival data from a population with cured subjects in the\npresence of mismeasured covariates. We use the mixture cure model to account\nfor the individuals that will never experience the event and at the same time\ndistinguish between the effect of the covariates on the cure probabilities and\non survival times. In particular, for practical applications, it seems of\ninterest to assume a logistic form of the incidence and a Cox proportional\nhazards model for the latency. To correct the estimators for the bias\nintroduced by the measurement error, we use the simex algorithm, which is a\nvery general simulation based method. It essentially estimates this bias by\nintroducing additional error to the data and then recovers bias corrected\nestimators through an extrapolation approach. The estimators are shown to be\nconsistent and asymptotically normally distributed when the true extrapolation\nfunction is known. We investigate their finite sample performance through a\nsimulation study and apply the proposed method to analyse the effect of the\nprostate specific antigen (PSA) on patients with prostate cancer.\n