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Title |
Markov Chain Monte Carlo ? Based Approaches for Modeling the Spatial Survival with Conditional Autoregressive (CAR) Frailty |
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Author |
Nur Iriawan, Suci Astutik, Dedy Dwi Prastyo |
| Citation |
Vol. 10 No. 12 pp. 211-216 |
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Abstract |
Survival Model is widely used in medical field and biostatistics. This model can be used to identify the risk factors of an event and can handle the situation when risk factors change with time. Timing of an event frequently depends on the location (spatial) called as spatial survival model. In the development, survival modeling also included random effects models (frailty) to overcome the heterogeneity / sources of unexplained variance in the model. Bayesian approach couple with Markov Chain Monte Carlo (MCMC) was developed in this paper to estimate the spatial parameters of survival models with Conditional Autoregressive (CAR) frailty. The purpose of this study is to assess and implement the MCMC algorithm for modeling survival by using software WinBUGS CAR frailty that can be used to overcome the heterogeneity / sources of unexplained variance in the model because of the influence of the location. |
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Keywords |
MCMC, Bayesian, Spatial survival, CAR frailty |
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