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 : 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...

Cox regression model serves as a statistical method for analyzing the survival data, which requires some options such as hazard proportionality. In recent decades, artificial neural network model has been increasingly applied to predict survival data. This research was conducted to compare Cox regression and artificial neural network models in prediction of kidney transplant survival. The prese...

Cox proportional hazards regression analysis of survival data and conditional logistic regression analysis of matched case-control data are methods that are widely used by epidemiologists. Standard statistical software packages accommodate only log-linear model forms, which imply exponential exposure-response functions and multiplicative interactions. In this paper, the authors describe methods...

BACKGROUND Case-cohort studies have become common in epidemiological studies of rare disease, with Cox regression models the principal method used in their analysis. However, no appropriate procedures to assess the assumption of proportional hazards of case-cohort Cox models have been proposed. METHODS We extended the correlation test based on Schoenfeld residuals, an approach used to evaluat...

Cox proportional hazards (PH) regression is a well-known model for analyzing survival data and its strengths are widely recognized. Threshold regression (TR) is a relatively new methodology but one that is receiving greater attention and being used successfully by researchers in different fields, including biopharmaceutical statistics. In threshold regression, event times are modeled by a stoch...

Cox proportional hazards regression analysis of survival data and conditional logistic regression analysis of matched case-control data are methods that are widely used by epidemiologists. Standard statistical software packages accommodate only log-linear model forms, which imply exponential exposure-response functions and multiplicative interactions. In this paper, the authors describe methods...

Semiparametric hazard function regression models are among the well studied risk models in survival analysis. The Cox proportional hazards model has been a popular choice in modelling data from epidemiological settings. The Cox-Aalen model is one of the tools for handling the problem of non-proportional effects in the Cox model. We show an application on Piedmont cancer registry data. We initia...