  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.
