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

Georgios Effraimidis () and Christian Dahl
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Georgios Effraimidis: Department of Business and Economics, Postal: University of Southern Denmark, Campusvej 55, DK-5230 Odense M, Denmark

No 21/2013, Discussion Papers on Economics from University of Southern Denmark, Department of Economics

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 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.

Keywords: Cumulative incidence function; Inverse probability weighting; Kernel estimation; Local linear estimation; Martingale central limit theorem (search for similar items in EconPapers)
JEL-codes: C14 C41 (search for similar items in EconPapers)
Pages: 25 pages
Date: 2013-12-19
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Related works:
Working Paper: Nonparametric Estimation of Cumulative Incidence Functions for Competing Risks Data with Missing Cause of Failure (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:hhs:sdueko:2013_021

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