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. Findings on the association between posttransplant anemia (PTA) and mortality in posttransplant patients are scarce. This study explored whether PTA shortly after kidney transplantation (KT) predicts mortality at up to 10 years' follow-up, stratified for chronic kidney disease (CKD) stages. Methods. PTA was divided into 3 categories according to the hemoglobin (Hb) value: severe (Hb...

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...

RESEARCHDESIGNANDMETHODSdUsing claims data from a nationwide statutory health insurance fund (Gmünder ErsatzKasse), we assessed all deaths in a cohort of 5,757 patients with a first stroke between 2005 and 2007 (69.3% male, mean age 68.1 years, 32.2% with diabetes) up to 2009. By use of Cox regression, we estimated time-dependent hazard ratios (HRs) to compare patients with and without diabetes...

We state and prove a limit theorem for estimators of a general, possibly infinite dimensional parameter based on unbiased estimating equations containing estimated nuisance parameters. The theorem corrects a gap in the proof of one of the assertions of our paper 'Weighted likelihood for semiparametric models and two-phase stratified samples, with application to Cox regression'.

Summary In this article, we propose a positive stable shared frailty Cox model for clustered failure time data where the frailty distribution varies with cluster-level covariates. The proposed model accounts for covariate-dependent intracluster correlation and permits both conditional and marginal inferences. We obtain marginal inference directly from a marginal model, then use a stratified Cox...

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...

conclusions the present study detected more accurate results for ann method compared to those of cox ph model to analyze the survival of patients with liver transplantation. furthermore, the order of effective factors in patients’ survival after transplantation was clinically more acceptable. the large dataset with a few missing data was the advantage of this study, the fact which makes the res...