background : gastric cancer is the one of the most prevalent reason of cancer-related death in the world. survival of patients after surgery involves identifying risk factors. there are various models to detect the effect of risk factors on patients’ survival. the present study aims at evaluating these models. methods : data from 330 gastric cancer patients diagnosed at the iran cancer institut...

The aim of fitting a Cox model to time-to-event data is to estimate the effect of covariates on the baseline hazard function. The baseline hazard function, not itself estimated within the model, is the hazard function obtained when all covariate are set to zero. In several applications, it is important to have an explicit, preferably smooth, estimate of the baseline hazard function, or more gen...

OBJECTIVE The Cox model is the dominant tool in clinical trials to compare treatment options. This model does not specify any specific form to the hazard function. On the other hand, parametric models allow the researcher to consider an appropriate shape of hazard function for the event of interest. The aim of this article is to compare performance of Cox and parametric models. METHODS We use...

background : the aim of this study was to predict the survival rate of iranian gastric cancer patients using the cox proportional hazard and artificial neural network models as well as comparing the ability of these approaches in predicting the survival of these patients. methods: in this historical cohort study, the data gathered from 436 registered gastric cancer patients who have had surgery...

The Cox proportional hazards regression model has been widely used in the analysis of survival/duration data. It is semiparametric because the model includes a baseline hazard function that is completely unspecified. We study here the statistical inference of the Cox model where some information about the baseline hazard function is available, but it still remains as an infinite dimensional nui...

Cox proportional hazards model is a commonly used model in providing hazard ratio to compare survival times of two population groups. The exponentiated linear regression part of the model describes the effects of explanatory variables on hazard ratio. PROC PHREG is a SAS procedure that implements the Cox model and provides the hazard ratio estimate. The estimate is interpreted as the percent ch...

The Cox proportional hazards (PH) model usually assumes linearity of the covariates on the log hazard function, which may be violated because linearity cannot always be guaranteed. We propose a partially linear single-index proportional hazards regression model, which can model both linear and nonlinear covariate effects on the log hazard in the proportional hazards model. We adopt a polynomial...