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Bayesian transformation cure frailty models with multivariate failure time data
Author(s) -
Yin Guosheng
Publication year - 2008
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3371
Subject(s) - deviance information criterion , multivariate statistics , gibbs sampling , computer science , statistics , bayesian probability , statistic , posterior probability , econometrics , mathematics , bayesian inference
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. Copyright © 2008 John Wiley & Sons, Ltd.