ABSTRACT Survival analysis involves the modeling of time-to-event data whereby death or failure is considered an "event". The graphic presentation of Cox proportional hazards model using SAS PHREG is a significant tool which facilitates effective data exploration in survival analysis. The SAS PROC PHREG can generate some of the useful survival analysis plots using the ODS graphics option in ver...

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...

We have developed a prognostic index model for survival data based on an ensemble of artificial neural networks that optimizes directly on the concordance index. Approximations of the c-index are avoided with the use of a genetic algorithm, which does not require gradient information. The model is compared with Cox proportional hazards (COX) and three support vector machine (SVM) models by Van ...

Page Title 1 Overview − hierarchical models 2 Calf mortality data 3 Hierarchical regression models 4 Hierarchical survival models: general approaches 5 Calf mortality data: multi-level Cox model 6 Methods for hierarchical Cox models 7 Pig lameness data 8 Pig data: hypothesis and first results 9 Pig data: Cox random slope models 10 Pig data: parametric models 11 Proportional hazards revisited 12...

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...

Although Cox proportional hazards regression is the default analysis for time to event data, there is typically uncertainty about whether the effects of a predictor are more appropriately characterized by a multiplicative or additive model. To accommodate this uncertainty, we place a model selection prior on the coefficients in an additive-multiplicative hazards model. This prior assigns positi...

The classical Cox proportional hazards model is a benchmark approach to analyze continuous survival times in the presence of covariate information. In a number of applications, there is a need to relax one or more of its inherent assumptions, such as linearity of the predictor or the proportional hazards property. Also, one is often interested in jointly estimating the baseline hazard together ...