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.
Original language | English (US) |
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Pages (from-to) | 907-918 |
Number of pages | 12 |
Journal | Statistics in Medicine |
Volume | 18 |
Issue number | 8 |
DOIs | |
State | Published - Apr 30 1999 |
Externally published | Yes |
ASJC Scopus subject areas
- Epidemiology
- Statistics and Probability