Quantile Regression Adjusting for Dependent Censoring from Semi-Competing Risks.

ABSTRACT: In this work, we study quantile regression when the response is an event time subject to potentially dependent censoring. We consider the semi-competing risks setting, where time to censoring remains observable after the occurrence of the event of interest. While such a scenario frequently arises in biomedical studies, most of current quantile regression methods for censored data are not applicable because they generally require the censoring time and the event time be independent. By imposing rather mild assumptions on the association structure between the time-to-event response and the censoring time variable, we propose quantile regression procedures, which allow us to garner a comprehensive view of the covariate effects on the event time outcome as well as to examine the informativenes

SUBMITTER: Li R 

PROVIDER: S-EPMC4283952 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature