Spatial Modeling of Censored Survival Data
An important issue in survival data analysis is the identification of risk factors. Some of these factors are identifiable and explainable by presence of some covariates in the Cox proportional hazard model, while the others are unidentifiable or even immeasurable. Spatial correlation of censored su...
| Main Authors: | , , |
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| Format: | Article |
| Language: | fas |
| Published: |
Kharazmi University
2015-12-01
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| Series: | پژوهشهای ریاضی |
| Subjects: | |
| Online Access: | http://mmr.khu.ac.ir/article-1-2538-en.html |
| Summary: | An important issue in survival data analysis is the identification of risk factors. Some of these factors are identifiable and explainable by presence of some covariates in the Cox proportional hazard model, while the others are unidentifiable or even immeasurable. Spatial correlation of censored survival data is one of these sources that are rarely considered in the literatures. In this paper, a spatial survival model is introduced to analyze such kinds of data. Then a simulation method is introduced to study the performance of Cox, frailty and spatial survival models for modeling spatially correlated survival data. Next, the proposed spatial survival model is used to model the time disease of Cercosporiose in olive trees. Finally, results and discussion are presented |
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| ISSN: | 2588-2546 2588-2554 |