Bayesian cure rate frailty models with application to a root canal therapy study.
Due to natural or artificial clustering, multivariate survival data often arise in biomedical studies, for example, a dental study involving multiple teeth from each subject. A certain proportion of subjects in the population who are not expected to experience the event of interest are considered to be "cured" or insusceptible. To model correlated or clustered failure time data incorporating a surviving fraction, we propose two forms of cure rate frailty models. One model naturally introduces frailty based on biological considerations while the other is motivated from the Cox proportional hazards frailty model. We formulate the likelihood functions based on piecewise constant hazards and derive the full conditional distributions for Gibbs sampling in the Bayesian paradigm. As opposed to the Cox frailty model, the proposed methods demonstrate great potential in modeling multivariate survival data with a cure fraction. We illustrate the cure rate frailty models with a root canal therapy data set.
Yin
Guosheng
G
Department of Biostatistics and Applied Mathematics, M. D. Anderson Cancer Center, The University of Texas, Houston, Texas 77030, USA. gyin@odin.mdacc.tmc.edu
D001499:Bayes Theorem; D001699:Biometry / Q000379:methods; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D015999:Multivariate Analysis; D016016:Proportional Hazards Models; D012390:Root Canal Therapy / Q000379:methods*; D012680:Sensitivity and Specificity; D013223:Statistics as Topic / Q000379:methods*; D018709:Statistics, Nonparametric; D013997:Time Factors; D014081:Tooth Extraction; D016896:Treatment Outcome