Construction of a survival tree for dependent censoring.

In this study, we examined the problem of constructing a model for time-to-event data considering dependent censoring. Our goal was to construct a set of subgroups of covariate space, wherein each element had the same failure model considering the dependency of failure and censoring times. As such, a model was constructed based on the parametric form from the identifiability problem of censoring. We used the copula to represent the dependency between failure and censoring times. Under the assumption of parametric models for failure and censoring times and a copula function, which have unknown parameters, we proposed a method for constructing the tree-structured model through the test statistics. We subsequently evaluated the performance of the splitting rule and tree obtained using the proposed method and compared it with the general method that assumes independent censoring through simulation studies. We also present the analysis results for AIDS clinical trial research to show the utility of the method.