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...

BACKGROUND Transplantation is the only treatment for patients with liver failure. Since the therapy imposes high expenses to the patients and community, identification of effective factors on survival of such patients after transplantation is valuable. OBJECTIVES The current study attempted to model the survival of patients (two years old and above) after liver transplantation using neural ne...

Semiparametric random censorship (SRC) models (Dikta, 1998), derive their rationale from their ability to gainfully utilize parametric ideas within the random censorship environment. An extension of this approach is developed for Cox regression, producing new estimators of the regression parameter and baseline cumulative hazard function. Under correct parametric specification, the proposed esti...

MOTIVATION Univariate Cox regression (COX) is often used to select genes possibly linked to survival. With non-proportional hazards (NPH), COX could lead to under- or over-estimation of effects. The effect size measure c=P(T(1)<T(0)), i.e. the probability that a person randomly chosen from group G(1) dies earlier than a person from G(0), is independent of the proportional hazards (PH) assumptio...

The Proportional Hazards (PH) model is an important type of failure time regression model which relates the occurrence probability of critical failures to influential factors. However, little research work has been done on detecting changes in the PH models fitted based on different sets of reliability data. This paper develops the methods for change detection in the Cox PH models, also known a...