Assessing heterogeneity and correlation of paired failure times with the bivariate frailty model

Research output: Contribution to journalArticle

13 Citations (Scopus)

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 languageEnglish (US)
Pages (from-to)907-918
Number of pages12
JournalStatistics in Medicine
Volume18
Issue number8
DOIs
StatePublished - Apr 30 1999
Externally publishedYes

Fingerprint

Frailty Model
Failure Time
Frailty
Deviation
Hazard Function
Survival Time
Population Characteristics
Mental Health
Association Measure
Delivery of Health Care
Cross ratio
Gibbs Sampling
Sampling Methods
Bayesian inference
Regression Coefficient
Healthcare
Datasets

ASJC Scopus subject areas

  • Epidemiology

Cite this

Assessing heterogeneity and correlation of paired failure times with the bivariate frailty model. / Xue, Xiaonan (Nan); Ding, Ye.

In: Statistics in Medicine, Vol. 18, No. 8, 30.04.1999, p. 907-918.

Research output: Contribution to journalArticle

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