background: kidney transplantation is the best alternative treatment for end-stage renal disease. several studies have been devoted to investigate predisposing factors of graft rejection. however, there is inconsistency between the results. the objective of the present study was to utilize an intuitive and robust approach for variable selection, random survival forests (rsf), and to identify im...

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...

conclusions the use of smoothing methods helps us to eliminate non-linear effects but it is more appropriate to use cox proportional hazards model in medical data because of its’ ease of interpretation and capability of modeling both continuous and discrete covariates. also, cox proportional hazards model and smoothing methods analysis identified that age at diagnosis and tumor size were indepe...

survival analysis is a set of methods used for analysis of the data which exist until the occurrence of an event. this study aimed to compare the results of the use of the semi-parametric cox model with parametric models to determine the factors influencing the length of stay of patients in the inpatient units of women hospital in tehran, iran. in this historical cohort study all 3421 charts of...

MOTIVATION There has been an increasing interest in expressing a survival phenotype (e.g. time to cancer recurrence or death) or its distribution in terms of a subset of the expression data of a subset of genes. Due to high dimensionality of gene expression data, however, there is a serious problem of collinearity in fitting a prediction model, e.g. Cox's proportional hazards model. To avoid th...