title: Bayesian quantile regression for censored data.
abstract: In this paper we propose a semiparametric quantile regression model for censored survival data. Quantile regression permits covariates to affect survival differently at different stages in the follow-up period, thus providing a comprehensive study of the survival distribution. We take a semiparametric approach, representing the quantile process as a linear combination of basis functions. The basis functions are chosen so that the prior for the quantile process is centered on a simple location-scale model, but flexible enough to accommodate a wide range of quantile processes. We show in a simulation study that this approach is competitive with existing methods. The method is illustrated using data from a drug treatment study, where we find that the Bayesian model often gives smaller measures of uncertainty than its competitors, and thus identifies more significant effects.
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695, U.S.A.
authors.forename: Brian J
authors.initials: BJ
authors.lastname: Reich
authors.forename: Luke B
authors.initials: LB
authors.lastname: Smith
mesh_terms: D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D012044:Regression Analysis; D016019:Survival Analysis; D035501:Uncertainty
keywords: Accelerated failure time model; Markov chain Monte Carlo; Quantile regression; Survival data