Using the accelerated failure time model to analyze current status data with misclassified covariates

Current status data arise commonly in applications when there is only one feasible observation time to check if the failure has occurred, but exact remains unknown. To accommodate covariate effect on time, accelerated (AFT) model been widely used analyze current with distribution of assumed be specified or unspecified. In this paper, we consider a logistic regression misclassfied from scheme. A semiparametric AFT was built eliminate bias caused by misclassification. This also robust misspecification compared parametric model, as assume an unknown proposed model. Furthermore, incorporating increases flexibility Finally, adapt Expectation-Maximization algorithm for estimation, which guarantees convergence estimate. Both theory and empirical studies show consistency estimator.