A Quantile Regression Model for Failure-Time Data with Time-Dependent Covariates
Abstract
Since survival data occur over time, often important covariates that we wish to consider also change over time. Such covariates are referred as time-dependent covariates. Quantile regression offers flexible modeling of survival data by allowing the covariates to vary with quantiles. This paper provides a novel quantile regression model accommodating time-dependent covariates, for analyzing survival data subject to right censoring. Our simple estimation technique assumes the existence of instrumental variables. In addition, we present a doubly-robust estimator in the sense of Robins and Rotnitzky (1992). The asymptotic properties of the estimators are rigorously studied. Finite-sample properties are demonstrated by a simulation study. The utility of the proposed methodology is demonstrated using the Stanford heart transplant dataset.
Cite
@article{arxiv.1404.7595,
title = {A Quantile Regression Model for Failure-Time Data with Time-Dependent Covariates},
author = {Malka Gorfine and Yair Goldberg and Yaacov Ritov},
journal= {arXiv preprint arXiv:1404.7595},
year = {2014}
}