Nonparametric estimation of cumulative incidence functions for competing risks data with missing cause of failure |
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Institution: | 1. Department of Urology, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon, Republic of Korea;2. Biostatistic Consulting, Soonchunhyang University College of Medicine, Bucheon, Republic of Korea;3. Department of Urology, Soonchunhyang University Hospital, Soonchunhyang University College of Medicine, Seoul, Republic of Korea |
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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. |
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Keywords: | Cumulative incidence function Inverse probability weighting Kernel estimation Local linear estimation Martingale central limit theorem |
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