Smoothed quantile regression analysis of competing risks.

ABSTRACT: 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 mul

SUBMITTER: Choi S 

PROVIDER: S-EPMC6156950 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature