Assessing heterogeneity and correlation of paired failure times with the bivariate frailty model.
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.
Xue
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Department of Environmental Medicine, New York University Medical Center, New York 10010-2598, USA. xiaonan.xue@med.nyu.edu
Ding
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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