Bayesian analysis for monotone hazard ratio.
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.
Kim
Yongdai
Y
Seoul National University, Seoul, Korea. ydkim0903@gmail.com
Park
Jin Kyung
JK
Kim
Gwangsu
G
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
D015122:Mercaptopurine