Quantile Regression Based on Semi-Competing Risks Data

Jin-Jian Hsieh, A. Adam Ding, Weijing Wang, Yu-Lin Chi

Department of Mathematics, National Chung Cheng University, Chia-Yi, Chinese Taipei.
Department of Mathematics, Northeastern University, Boston, USA..
Institute of Statistics, National Chiao-Tung University, Hsin-Chu, Chinese Taipei.

DOI: 10.4236/ojs.2013.31003                

Abstract

This paper considers quantile regression analysis based on semi-competing risks data in which a non-terminal event may be dependently censored by a terminal event. The major interest is the covariate effects on the quantile of the non-terminal event time. Dependent censoring is handled by assuming that the joint distribution of the two event times follows a parametric copula model with unspecified marginal distributions. The technique of inverse probability weighting (IPW) is adopted to adjust for the selection bias. Large-sample properties of the proposed estimator are derived and a model diagnostic procedure is developed to check the adequacy of the model assumption. Simulation results show that the proposed estimator performs well. For illustrative purposes, our method is applied to analyze the bone marrow transplant data in [1].

Keywords

Copula Model; Dependent Censoring; Quantile Regression; Semi-Competing Risks Data

Conflicts of Interest

The authors declare no conflicts of interest.

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