جستجوی مقالات مرتبط با کلیدواژه "مدل بقای فضایی" در نشریات گروه "ریاضی"

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

One of the most widely used models for fitting survival data is Cox proportional hazards model that is based on homogeneity, independence and equi-distributed of survival data. But in many cases hazards of statistical units are different and the assumption of population homogeneity is not established. One of the reasons for such deference is the unknown or unobserved risk factors which may lead to some misleading models if there is no concern for them or some models such as Cox proportional hazard models have to be implemented. In such cases, regarding the unknown risk factors, frailty models are used. In this paper the performances of the Cox and frailty models for survival and spatial survival data with unknown risk factors are considered. The efficiency of these models whilst the source of unknown risk factors is the spatial correlation of survival data is also examined.