Number of words: 2795 (excluding summary, references and tables) 2 SUMMARY This paper discusses techniques to generate survival times for simulation studies regarding Cox proportional hazards models. In linear regression models, the response variable is directly connected with the considered covariates, the regression coefficients and the simulated random errors. Thus, the response variable can...

introduction: the majority of leukemia patients are acute leukemia patients, so that about 70.8% lymphoblastic leukemia were acute lymphoblastic leukemia (all) patients and 66.4 % of myeloid leukemia patients were acute myeloid leukemia (aml) in tehran metropolitan. during the last two decades, intensification of therapy by the use of high-dose cytarabine allogeneic stem cell transplantation in...

The semi-parametric Cox proportional hazards (PH) regression model was developed by Sir David Cox (Cox 1972) and is by far the most popular model for survival analysis. The model defines a hazard function, which is the rate of an event occurring at any given time, given the observation is still at risk, as a function of the observed covariates. When data consist of independent and identically d...

background: although the cox proportional hazard regression is the most popular model for analyzing the prognostic factors on survival of cancer patients, under certain circumstances, parametric models estimate the parameter more efficiently than the cox model.  the aim of this study was to compare the cox regression model  with parametric models in patients with gastric cancer who registered a...

background: the aim of this study is to evaluate the association between different treatments and survival time of breast cancer patients using either standard cox model or stratified cox model. methods: the study was conducted on 15830 women diagnosed with breast cancer in british columbia, canada.  they were divided into eight groups according to patients’ ages and stage of disease either cox...