Nonparametric Marginal Analysis of Recurrent Events Data under Competing Risks

TL;DR

This paper develops nonparametric methods for analyzing recurrent events with competing risks, focusing on marginal inference of specific event types, and applies these methods to dialysis data to handle dependent censoring.

Contribution

It introduces a nonparametric framework using IPCW for recurrent competing risks data, with theoretical properties and bootstrap inference, applied to real dialysis data.

Findings

Effective adjustment for dependent censoring in recurrent risks

Proposed estimators have desirable large-sample properties

Simulation studies confirm finite-sample performance

Abstract

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.