Bayesian modeling of clustered competing risks survival times with spatial random effects
n some studies, survival data are arranged spatially such as geographical regions. Incorporating spatial association in these data not only can increase the accuracy and efficiency of the parameter estimation, but it also investigates the spatial patterns of survivorship. In this paper, we consider...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Milano University Press
2023-08-01
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| Series: | Epidemiology, Biostatistics and Public Health |
| Subjects: | |
| Online Access: | https://riviste.unimi.it/index.php/ebph/article/view/20866 |
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| Summary: | n some studies, survival data are arranged spatially such as geographical regions. Incorporating spatial association
in these data not only can increase the accuracy and efficiency of the parameter estimation, but it also investigates
the spatial patterns of survivorship. In this paper, we considered a Bayesian hierarchical survival model in the
setting of competing risks for the spatially clustered HIV/AIDS data. In this model, a Weibull Parametric distribution
with the spatial random effects in the form of county-failure type-level was used. A multivariate intrinsic conditional
autoregressive (MCAR) distribution was employed to model the areal spatial random effects. Comparison among
competing models was performed by the deviance information criterion and log pseudo-marginal likelihood. We
illustrated the gains of our model through the simulation studies and application to the HIV/AIDS data.
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| ISSN: | 2282-0930 |