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].
Highlights
Quantile regression analysis has received increasing attentions in the recent literature of survival analysis
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
Dependent censoring is handled by assuming that the joint distribution of the two event times follows a parametric copula model with unspecified marginal distributions
Summary
Quantile regression analysis has received increasing attentions in the recent literature of survival analysis. Compared with conventional regression models such as the proportional hazards (PH) model or the accelerated failure time (AFT) model, quantile regression models provide direct assessment of the covariate effect on different quantiles of the failure time variable. This model allows covariates to affect both location and shape of the distribution. We will adopt a semi-parametric copula assumption to model their joint distribution and apply the technique of inverse probability weighting (IPW) to correct the bias due to dependent censoring in the estimation procedure.
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