Censored Quantile Regression with Time Varying Covariates

About this event

We propose a class of quantile regression models for right censored failure time data with time varying covariates that requires only standard conditional independence. Using a quantile based transformation, a system of functional estimating equations for the quantile parameters is derived upon the martingale structure. The proposed formulation extends the existing censored quantile regression model so that only the covariate history up to the observed event time is required as in the Cox proportional hazards model for time varying covariates. A recursive algorithm is developed to evaluate the estimator numerically. Asymptotic properties including uniform consistency and weak convergence of the proposed estimator as a process of the quantile level is established. Monte Carlo simulations and numerical studies on the clinical trial data of the AIDS Clinical Trials Group is presented to illustrate the numerical performance of the proposed estimator.