In survival analysis, deviations from proportional hazards may sometimes be explained by unaccounted random heterogeneity, or frailty. This paper recalls the literature on omitted covariates in survival analysis and shows in a case study how unstably frailty models might behave when asked to account
Assessing heterogeneity and correlation of paired failure times with the bivariate frailty model
β Scribed by Xiaonan Xue; Ye Ding
- Publisher
- John Wiley and Sons
- Year
- 1999
- Tongue
- English
- Weight
- 125 KB
- Volume
- 18
- Category
- Article
- ISSN
- 0277-6715
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β¦ Synopsis
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.
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