  This project was motivated by a dialysis study in northern Taiwan. Dialysis
patients, after shunt implantation, may experience two types ("acute" or
"non-acute") of shunt thrombosis, both of which may recur. We formulate the
problem under the framework of recurrent events data in the presence of
competing risks. In particular we focus on marginal inference for the gap time
variable of specific type. The functions of interest are the cumulative
incidence function and cause-specific hazard function. The major challenge of
nonparametric inference is the problem of induced dependent censoring. We apply
the technique of inverse probability of censoring weighting (IPCW) to adjust
for the selection bias. Besides point estimation, we apply the bootstrap
re-sampling method for further inference. Large sample properties of the
proposed estimators are derived. Simulations are performed to examine the
finite-sample performances of the proposed methods. Finally we apply the
proposed methodology to analyze the dialysis data.
