The Estimation Of Accelerated Failure Time Cure Model With Current Status Data

There has been considerable progress in the development of an accelerated failure time models for regression analysis of time-to-event data.However,most of the current work focuses on rightcensored data,especially when the population contains a non-ignorable cured subgroup.But this paper work focuses on accelerated failure time cure model with current status data.A cured model is a useful approach for analysing failure time data in which some subjects could eventually experience and others never experience the event of interest.All subjects in the test belong to one of the two groups: the susceptible group and the non-susceptible group.This thesis attempts to conduct the research from two kinds of accelerated failure time cure model with current status data: One model has a constant cure rate and the other cure rate model is a logistic function of covariate,An accelerated failure time regression model is proposed for the event time when the subject is in the non-susceptible group.An EM algorithm is used to maximize the log-likelihood of the observed data.Simulation results show that the proposed method can get efficient estimations.