Multiple imputation method for the semiparametric accelerated failure time mixture cure model

There are few discussions on the semiparametric accelerated failure time mixture cure model due to its complexity in estimation. In this paper, we propose a multiple imputation method for the semiparametric accelerated failure time mixture cure model based on the rank estimation method and the profile likelihood method. Both approaches can be easily implemented in R environment. However, the computation time for the rank estimation method is longer than that from the profile likelihood method. Simulation studies demonstrate that the performances of estimated parameters from the proposed methods are comparable to those from the expectation maximization (EM) algorithm, and the estimated variances are comparable to those from the empirical approach. For illustration, we apply the proposed method to a data set of failure times from the bone marrow transplantation.