title: Bayesian analysis for monotone hazard ratio.
abstract: We propose a Bayesian approach for estimating the hazard functions under the constraint of a monotone hazard ratio. We construct a model for the monotone hazard ratio utilizing the Cox's proportional hazards model with a monotone time-dependent coefficient. To reduce computational complexity, we use a signed gamma process prior for the time-dependent coefficient and the Bayesian bootstrap prior for the baseline hazard function. We develop an efficient MCMC algorithm and illustrate the proposed method on simulated and real data sets.
authors.affiliation: Seoul National University, Seoul, Korea. ydkim0903@gmail.com
authors.forename: Yongdai
authors.initials: Y
authors.lastname: Kim
authors.forename: Jin Kyung
authors.initials: JK
authors.lastname: Park
authors.forename: Gwangsu
authors.initials: G
authors.lastname: Kim
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D005260:Female; D006801:Humans; D007938:Leukemia / Q000188:drug therapy; D008390:Markov Chains; D015122:Mercaptopurine / Q000627:therapeutic use; D009010:Monte Carlo Method; D010051:Ovarian Neoplasms / Q000188:drug therapy; D016016:Proportional Hazards Models; D013269:Stochastic Processes
chemical_list: D015122:Mercaptopurine