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.
Original language | English |
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Pages (from-to) | 132-146 |
Number of pages | 15 |
Journal | Biostatistics |
Volume | 18 |
Issue number | 1 |
DOIs | |
State | Published - 1 Jan 2017 |
Bibliographical note
Publisher Copyright:© 2016 The Author.
Keywords
- Instrumental variables
- Quantile regression
- Survival analysis
- Time-dependent covariates
ASJC Scopus subject areas
- General Medicine