Quantile regression based on counting process approach under semi-competing risks data

Jin-Jian Hsieh () and Hong-Rui Wang
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Jin-Jian Hsieh: National Chung Cheng University
Hong-Rui Wang: National Chung Cheng University

Annals of the Institute of Statistical Mathematics, 2018, vol. 70, issue 2, No 10, 395-419

Abstract: Abstract In this paper, we investigate the quantile regression analysis for semi-competing risks data in which a non-terminal event may be dependently censored by a terminal event. The estimation of quantile regression parameters for the non-terminal event is complicated. We cannot make inference on the non-terminal event without extra assumptions. Thus, we handle this problem by assuming that the joint distribution of the terminal event and the non-terminal event follows a parametric copula model with unspecified marginal distributions. We use the stochastic property of the martingale method to estimate the quantile regression parameters under semi-competing risks data. We also prove the large sample properties of the proposed estimator, and introduce a model diagnostic approach to check model adequacy. From simulation results, it shows that the proposed estimator performs well. For illustration, we apply our proposed approach to analyze a real data.

Keywords: Copula model; Dependent censoring; Quantile regression; Semi-competing risks data (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10463-016-0593-6

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