Estimating the survival function based on the semi-Markov model for dependent censoring

Ziqiang Zhao (), Ming Zheng () and Zhezhen Jin ()
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Ziqiang Zhao: Fudan University
Ming Zheng: Fudan University
Zhezhen Jin: Columbia University

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2016, vol. 22, issue 2, No 1, 190 pages

Abstract: Abstract In this paper, we study a nonparametric maximum likelihood estimator (NPMLE) of the survival function based on a semi-Markov model under dependent censoring. We show that the NPMLE is asymptotically normal and achieves asymptotic nonparametric efficiency. We also provide a uniformly consistent estimator of the corresponding asymptotic covariance function based on an information operator. The finite-sample performance of the proposed NPMLE is examined with simulation studies, which show that the NPMLE has smaller mean squared error than the existing estimators and its corresponding pointwise confidence intervals have reasonable coverages. A real example is also presented.

Keywords: Semi-Markov model; Dependent censoring; NPMLE; Survival function (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s10985-015-9325-0

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