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Showing 4 results for Survival Analysis
Amal Saki Malehi, Ebrahim Hajizadeh, Kambiz Ahmadi,
Volume 6, Issue 1 (8-2012)
Abstract
The survival analysis methods are usually conducted based on assumption that the population is homogeneity. However, generally, this assumption in most cases is unrealistic, because of unobserved risk factors or subject specific random effect. Disregarding the heterogeneity leads to unbiased results. So frailty model as a mixed model was used to adjust for uncertainty that cannot be explained by observed factors in survival analysis. In this paper, family of power variance function distributions that includes gamma and inverse Gaussian distribution were introduced and evaluated for frailty effects. Finally the proportional hazard frailty models with Weibull baseline hazard as a parametric model used for analyzing survival data of the colorectal cancer patients.
Azam Rastin, Mohammadreza Faridrohani,
Volume 13, Issue 2 (2-2020)
Abstract
The methodology of sufficient dimension reduction has offered an effective means to facilitate regression analysis of high-dimensional data. When the response is censored, most existing estimators cannot be applied, or require some restrictive conditions. In this article modification of sliced inverse, regression-II have proposed for dimension reduction for non-linear censored regression data. The proposed method requires no model specification, it retains full regression information, and it provides a usually small set of composite variables upon which subsequent model formulation and prediction can be based. Finally, the performance of the method is compared based on the simulation studies and some real data set include primary biliary cirrhosis data. We also compare with the sliced inverse regression-I estimator.
Mr Milad Pakdel, Dr Kiomars Motarjem,
Volume 18, Issue 1 (8-2024)
Abstract
In some instances, the occurrence of an event can be influenced by its spatial location, giving rise to spatial survival data. The accurate and precise estimation of parameters in a spatial survival model poses a challenge due to the complexity of the likelihood function, highlighting the significance of employing a Bayesian approach in survival analysis. In a Bayesian spatial survival model, the spatial correlation between event times is elucidated using a geostatistical model. This article presents a simulation study to estimate the parameters of classical and spatial survival models, evaluating the performance of each model in fitting simulated survival data. Ultimately, it is demonstrated that the spatial survival model exhibits superior efficacy in analyzing blood cancer data compared to conventional models.
Mehrnoosh Madadi, Kiomars Motarjem,
Volume 18, Issue 2 (2-2025)
Abstract
Due to the volume and complexity of emerging data in survival analysis, it is necessary to use statistical learning methods in this field. These methods can estimate the probability of survival and the effect of various factors on the survival of patients. In this article, the performance of the Cox model as a common model in survival analysis is compared with compensation-based methods such as Cox Ridge and Cox Lasso, as well as statistical learning methods such as random survival forests and neural networks. The simulation results show that in linear conditions, the performance of the models mentioned above is similar to the Cox model. In non-linear conditions, methods such as Cox lasso, random survival forest, and neural networks perform better. Then, these models were evaluated in the analysis of the data of patients with atheromatous, and the results showed that when faced with data with a large number of explanatory variables, statistical learning approaches generally perform better than the classical survival model.
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