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

Discussion Papers on Business and Economics, University of Southern Denmark, 21/2013

26 Pages Posted: 21 Dec 2013

See all articles by Georgios Effraimidis

Georgios Effraimidis

Qualcomm, Inc.

Christian M. Dahl

Department of Business and Economics

Date Written: December 19, 2013

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

JEL Classification: C14, C41

Suggested Citation

Effraimidis, Georgios and Dahl, Christian M., Nonparametric Estimation of Cumulative Incidence Functions for Competing Risks Data with Missing Cause of Failure (December 19, 2013). Discussion Papers on Business and Economics, University of Southern Denmark, 21/2013. Available at SSRN: https://ssrn.com/abstract=2369708 or http://dx.doi.org/10.2139/ssrn.2369708

Georgios Effraimidis (Contact Author)

Qualcomm, Inc. ( email )

5775 Morehouse Dr
San Diego, CA 92121
United States

Christian M. Dahl

Department of Business and Economics ( email )

Campusvej 55
DK-5230 Odense M
Denmark
29125486 (Phone)

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