Marshall–Olkin frailty survival models for bivariate right-censored failure time data
| Autor: | Andrea Giussani, Marco Bonetti |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
021103 operations research 0211 other engineering and technologies FRAILTY MODELS 02 engineering and technology Bivariate analysis Time data CACHE COUNTY STUDY FRAILTY MODELS MARSHALL–OLKIN DISTRIBUTION SURVIVAL ANALYSIS 01 natural sciences Multivariate survival 010104 statistics & probability MARSHALL–OLKIN DISTRIBUTION Statistics 0101 mathematics Statistics Probability and Uncertainty SURVIVAL ANALYSIS Survival analysis CACHE COUNTY STUDY Mathematics Parametric statistics |
| Popis: | The aim of this paper is to explore multivariate survival techniques for the analysis of bivariate right-censoring failure time data. In particular, a new family of parametric bivariate frailty models is investigated. To take into account the correlation between two survival times, the Marshall���Olkin Bivariate Exponential Distribution (MOBVE) is exploited to model the joint distribution of two frailties. The reason is twofold: on the one hand, it allows one to model shocks that affect individual-specific frailties; on the other hand, the parameter underlying the Poisson process characterizing the common shock is used to capture the dependence between two lifetimes. The proposed methodology is applied to the investigation of association in death on different-sex couples followed within the Cache County Study on Memory Health and Aging (CCSMHA). A cure rate extension of the model is also described. |
| Databáze: | OpenAIRE |
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