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Showing 2 results for Mohammadzadeh
Ameneh Abyar, Mohsen Mohammadzadeh, Kiomars Motarjem,
Volume 21, Issue 1 (9-2016)
Abstract
By existing censor and skewness in survival data, some models such as weibull are used to analyzing survival data.
In addition, parametric and semiparametric models can be obtained from baseline hazard function of Cox model to fit to survival data. However these models are popular because of their simple usage but do not consider unknown risk factors, that's why cannot introduce the best fit to the data necessarily.
In this paper by considering multiple random effects in Cox model, frailty models are introduced. Then using presented models, esophageal cancer data in Golestan were modeled and fitted models were evaluated and compared based on generalized coefficient of determination criterion.
Miss. Kimia Kazemi, Prof. Mohsen Mohammadzadeh,
Volume 25, Issue 2 (3-2021)
Abstract
In conventional methods for spatial survival data modeling, it is often assumed that the coefficients of explanatory variables in different regions have a constant effect on survival time. Usually, the spatial correlation of data through a random effect is also included in the model. But in many practical issues, the factors affecting survival time do not have the same effects in different regions. In this paper, we consider the spatial effects of factors affecting survival time are not the same in the different areas.
For this purpose, spatial regression models and spatial varying coefficient models are introduced. Next, the Bayesian estimates of their parameters are presented. Three models of classical regression, spatial regression and spatial varying coefficient regression are used to analyze Esophageal cancer survival data. The relative risk of various factors is examined and evaluated.
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