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

Nonparametric Marginal Analysis of Recurrent Events Data under Competing Risks