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.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Xue X,Ding Y

doi

10.1002/(sici)1097-0258(19990430)18:8<907::aid-sim

subject

Has Abstract

pub_date

1999-04-30 00:00:00

pages

907-18

issue

8

eissn

0277-6715

issn

1097-0258

pii

10.1002/(SICI)1097-0258(19990430)18:8<907::AID-SIM

journal_volume

18

pub_type

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