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

Author

Listed:
  • Jin-Jian Hsieh

    (National Chung Cheng University)

  • Hong-Rui Wang

    (National Chung Cheng University)

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.

Suggested Citation

  • Jin-Jian Hsieh & Hong-Rui Wang, 2018. "Quantile regression based on counting process approach under semi-competing risks data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(2), pages 395-419, April.
    • Handle: RePEc:spr:aistmt:v:70:y:2018:i:2:d:10.1007_s10463-016-0593-6
    DOI: 10.1007/s10463-016-0593-6
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    References listed on IDEAS

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