Nonparametric estimation of cumulative incidence functions for competing risks data with missing cause of failure

Georgios Effraimidis, Christian M. Dahl

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

In this paper, we develop a fully nonparametric approach for the estimation of the cumulative incidence function with Missing At Random right-censored competing risks data. We obtain results on the pointwise asymptotic normality as well as the uniform convergence rate of the proposed nonparametric estimator. A simulation study that serves two purposes is provided. First, it illustrates in detail how to implement our proposed nonparametric estimator. Second, it facilitates a comparison of the nonparametric estimator to a parametric counterpart based on the estimator of Lu and Liang (2008). The simulation results are generally very encouraging.

Original languageEnglish
JournalStatistics & Probability Letters
Volume89
Issue numberJune
Pages (from-to)1-7
ISSN0167-7152
DOIs
Publication statusPublished - 1. Jun 2014

Keywords

  • Cumulative incidence function
  • Inverse probability weighting
  • Kernel estimation
  • Local linear estimation
  • Martingale central limit theorem

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