title: Assessing heterogeneity and correlation of paired failure times with the bivariate frailty model.
abstract: We consider bivariate survival times for heterogeneous populations, where heterogeneity induces deviations in an individual's risk of an event as well as associations between survival times. The heterogeneity is characterized by a bivariate frailty model. We measure the heterogeneity effects through deviations associated with hazard functions and an association function defined through the conditional hazard functions: the cross-ratio function proposed by Oakes. We show how the deviation and association measures are determined by the frailty distribution. A Gibbs sampling method is developed for Bayesian inferences on regression coefficients, frailty parameters and the heterogeneity measures. The method is applied to a mental health care data set.
authors.affiliation: Department of Environmental Medicine, New York University Medical Center, New York 10010-2598, USA. xiaonan.xue@med.nyu.edu
authors.forename: X
authors.initials: X
authors.lastname: Xue
authors.forename: Y
authors.initials: Y
authors.lastname: Ding
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D003625:Data Collection; D006291:Health Policy; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D007345:Insurance Claim Review / Q000706:statistics & numerical data*; D007902:Length of Stay / Q000706:statistics & numerical data; D001523:Mental Disorders / Q000628:therapy*; D008954:Models, Biological; D009010:Monte Carlo Method; D012306:Risk