Censored quantile regression models, which offer great flexibility in assessing covariate effects on event times, have attracted considerable research interest. In this study, we consider flexible estimation and inference procedures for competing risks quantile regression, which not only provides meaningful interpretations by using cumulative incidence quantiles but also extends the conventional accelerated failure time model by relaxing some of the stringent model assumptions, such as global linearity and unconditional independence. Current method for censored quantile regressions often involves the minimization of the L1-type convex function or solving the nonsmoothed estimating equations. This approach could lead to multiple roots in practical settings, particularly with multiple covariates. Moreover, variance estimation involves an unknown error distribution and most methods rely on computationally intensive resampling techniques such as bootstrapping. We consider the induced smoothing procedure for censored quantile regressions to the competing risks setting. The proposed procedure permits the fast and accurate computation of quantile regression parameter estimates and standard variances by using conventional numerical methods such as the Newton–Raphson algorithm. Numerical studies show that the proposed estimators perform well and the resulting inference is reliable in practical settings. The method is finally applied to data from a soft tissue sarcoma study.
Bibliographical noteFunding Information:
National Research Foundation of Korea, Grant/Award Numbers: 2017R1A2B4005818, 2017R1C1B1004817; National Institutes of Health, Grant/Award Numbers: 5P50 CA100632, U01 CA152958, U54 CA096300; Korea University, Grant/Award Number: K1607341; National Science Foundation, Grant/Award Number: DMS 1612965
Dr. Choi was supported by grants from Korea University (K1607341) and National Research Foundation (NRF) of Korea (2017R1C1B1004817). Dr. Kang was supported by grant from NRF of Korea (2017R1A2B4005818). The research of Dr. Huang was supported in part by USA NSF grant (DMS 1612965) and NIH grants (U54 CA096300, U01 CA152958, and 5P50 CA100632).
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Statistics, Probability and Uncertainty