Quantile regression methods for censored gap time data

ABSTRACT

In longitudinal studies, subjects may potentially undergo a series of sequentially ordered events. The gap times, which are the times between two serial events, are often the outcome variables of interest. This study considers quantile regression models of gap times for censored serial-event data and adapts a weighted version of the estimating equation for regression coefficients. The resulting estimators are uniformly consistent and asymptotically normal. Extensive simulation studies are presented to evaluate the finite-sample performance of the proposed methods. An analysis of the tumor recurrence data for bladder cancer patients is also provided to illustrate our proposed methods.