Nonparametric Estimation of Cumulative Incidence Functions for Competing Risks Data with Missing Cause of Failure

Publikation: Working paperForskning

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  • Georgios Effraimidis, Syddansk Universitet, Danmark
  • Christian Møller Dahl, Danmark
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 details how to implement our proposed nonparametric estimator. Secondly, 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.
OriginalsprogEngelsk
UdgivelsesstedAarhus
UdgiverInstitut for Økonomi, Aarhus Universitet
Antal sider25
StatusUdgivet - 19 dec. 2013
SerietitelCREATES Research Papers
Nummer2013-50

    Forskningsområder

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

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