Additive risk survival model with microarray data

Additive risk models for survival data with high-dimensional covariates.

As a useful alternative to Cox's proportional hazard model, the additive risk model assumes that the hazard function is the sum of the baseline hazard function and the regression function of covariates. This article is concerned with estimation and prediction for the additive risk models with right censored survival data, especially when the dimension of the covariates is comparable to or large...

Predicting the Survival Time for Bladder Cancer Using an Additive Hazards Model in Microarray Data

BACKGROUND One substantial part of microarray studies is to predict patients' survival based on their gene expression profile. Variable selection techniques are powerful tools to handle high dimensionality in analysis of microarray data. However, these techniques have not been investigated in competing risks setting. This study aimed to investigate the performance of four sparse variable select...

cDNA Microarray Data Normalization

predicting the survival time for bladder cancer using an addi-tive hazards model in microarray data

background: one substantial part of microarray studies is to predict patients’ survival based on their gene expression profile. variable selection techniques are powerful tools to handle high dimensionality in analysis of microarray data. however, these techniques have not been investigated in competing risks setting. this study aimed to investigate the performance of four sparse variable selec...

Survival of Dialysis Patients Using Random Survival Forest Model in Low-Dimensional Data with Few-Events

Background:Dialysis is a process for eliminating extra uremic fluids of patients with chronic renal failure. The present study aimed to determine the variables that influence the survival of dialysis patients using random survival forest model (RSFM) in low-dimensional data with low events per variable (EPV). Methods:In this historical cohort study, infor...