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.
|Number of pages||15|
|State||Published - 1 Jan 2017|
Bibliographical noteFunding Information:
NIH grant (P01CA53996) to M.G.; ISF grant (1308/12) toY.G.; ISF grant (1770/15) toY.R.
© 2016 The Author.
- Instrumental variables
- Quantile regression
- Survival analysis
- Time-dependent covariates
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty