Nonparametric Marginal Analysis of Recurrent Events Data under Competing Risks
This work addresses a statistical challenge in medical research for dialysis patients, but it is incremental as it applies known techniques to a specific domain.
The researchers tackled the problem of analyzing recurrent events data under competing risks, specifically for dialysis patients experiencing two types of shunt thrombosis, by developing nonparametric estimators for cumulative incidence and cause-specific hazard functions using inverse probability of censoring weighting and bootstrap methods, with simulations showing finite-sample performance and application to real data.
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.