title: 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 article 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, Recovery of information and adjustment for dependent censoring using surrogate markers. In: Jewell, N. P., Dietz, K. and Farewell, V. T. (editors), AIDS Epidemiology. Boston: Birkhaäuser, pp. 297-331.). 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.
authors.affiliation: Department of Statistics and Operation Research, Tel Aviv University, Ramat Aviv, 6997801 Tel Aviv, Israel gorfinem@post.tau.ac.il.
authors.forename: Malka
authors.initials: M
authors.lastname: Gorfine
authors.affiliation: Department of Statistics, University of Haifa, Mount Carmel, 31905 Haifa, Israel.
authors.forename: Yair
authors.initials: Y
authors.lastname: Goldberg
authors.affiliation: Department of Statistics, The Hebrew University of Jerusalem, Mount Scopus, 91905 Jerusalem, Israel and Department of Statistics, University of Michigan, Ann Arbor, MI 48194, USA.
authors.forename: Ya'acov
authors.initials: Y
authors.lastname: Ritov
mesh_terms: D003198:Computer Simulation; D016027:Heart Transplantation / Q000706:statistics & numerical data; D006801:Humans; D015233:Models, Statistical; D012044:Regression Analysis; D016019:Survival Analysis
keywords: Instrumental variables; Quantile regression; Survival analysis; Time-dependent covariates