Quantile Regression Based on Semi-Competing Risks Data
2013
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:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
24
References
6
Citations
NaN
KQI