Bayesian transformation cure frailty models with multivariate failure time data.
We propose a class of transformation cure frailty models to accommodate a survival fraction in multivariate failure time data. Established through a general power transformation, this family of cure frailty models includes the proportional hazards and the proportional odds modeling structures as two special cases. Within the Bayesian paradigm, we obtain the joint posterior distribution and the corresponding full conditional distributions of the model parameters for the implementation of Gibbs sampling. Model selection is based on the conditional predictive ordinate statistic and deviance information criterion. As an illustration, we apply the proposed method to a real data set from dentistry.
Yin
Guosheng
G
Department of Biostatistics, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA. gsyin@mdanderson.org
D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D003813:Dentistry / Q000706:statistics & numerical data; D018572:Disease-Free Survival; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D015999:Multivariate Analysis; D016016:Proportional Hazards Models; D012390:Root Canal Therapy / Q000379:methods; D016896:Treatment Outcome