2014/04/30 by Malka Gorfine, Gorfine, Malka, Yair Goldberg +3
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Distribution Estimation and Applications #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.1404.7595
openalex publication_date 2014/04/30 · openalex created_date 2025/10/27 · openalex updated_date 2026/07/28
Since survival data occur over time, often important covariates that we wish\nto consider also change over time. Such covariates are referred as\ntime-dependent covariates. Quantile regression offers flexible modeling of\nsurvival data by allowing the covariates to vary with quantiles. This paper\nprovides a novel quantile regression model accommodating time-dependent\ncovariates, for analyzing survival data subject to right censoring. Our simple\nestimation technique assumes the existence of instrumental variables. In\naddition, we present a doubly-robust estimator in the sense of Robins and\nRotnitzky (1992). The asymptotic properties of the estimators are rigorously\nstudied. Finite-sample properties are demonstrated by a simulation study. The\nutility of the proposed methodology is demonstrated using the Stanford heart\ntransplant dataset.\n