scholarly journals Extended Cox model for breast cancer survival data using Bayesian approach: A case study

2019 ◽  
Vol 1341 ◽  
pp. 092013
Author(s):  
R Arsyad ◽  
S A Thamrin ◽  
A K Jaya
Keyword(s):  
Breast Cancer ◽  
Bayesian Approach ◽  
Survival Data ◽  
Cancer Survival ◽  
Cox Model ◽  
Breast Cancer Survival ◽  
Cancer Survival Data ◽  
Extended Cox Model

scholarly journals Variable Selection in Frailty Models using FrailtyHL R Package: Breast Cancer Survival Data

2015 ◽  
Vol 28 (5) ◽  
pp. 965-976 ◽  
Author(s):  
Bohyeon Kim ◽  
Il Do Ha ◽  
Maengseok Noh ◽  
Myung Hwan Na ◽  
Ho-Chun Song ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Variable Selection ◽  
Survival Data ◽  
Cancer Survival ◽  
R Package ◽  
Breast Cancer Survival ◽  
Frailty Models ◽  
Cancer Survival Data

Modelling of Breast Cancer Survival Data: A Frailty Model Approach

2011 ◽  
Vol 3 (10) ◽  
pp. 1-3
Author(s):  
Pari Dayal L Pari Dayal L ◽  
◽  
Leo Alexander T Leo Alexander T ◽  
Ponnuraja C Ponnuraja C ◽  
Venkatesan P Venkatesan P
Keyword(s):  
Breast Cancer ◽  
Survival Data ◽  
Cancer Survival ◽  
Frailty Model ◽  
Breast Cancer Survival ◽  
Cancer Survival Data ◽  
Model Approach

scholarly journals Black Hispanic and Black Non-Hispanic Breast Cancer Survival Data Analysis with Half-normal Model Application

2014 ◽  
Vol 15 (21) ◽  
pp. 9453-9458 ◽  
Author(s):  
Hafiz Mohammad Rafiqullah Khan ◽  
Anshul Saxena ◽  
Veronica Vera ◽  
Faheema Abdool-Ghany ◽  
Kemesha Gabbidon ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Data Analysis ◽  
Survival Data ◽  
Cancer Survival ◽  
Breast Cancer Survival ◽  
Normal Model ◽  
Model Application ◽  
Cancer Survival Data

scholarly journals Inferential Statistics from Black Hispanic Breast Cancer Survival Data

2014 ◽  
Vol 2014 ◽  
pp. 1-13
Author(s):  
Hafiz M. R. Khan ◽  
Anshul Saxena ◽  
Elizabeth Ross ◽  
Venkataraghavan Ramamoorthy ◽  
Diana Sheehan
Keyword(s):  
Breast Cancer ◽  
Survival Data ◽  
Cancer Survival ◽  
Breast Cancer Survival ◽  
Information Criteria ◽  
Model Parameters ◽  
Probability Models ◽  
Statistical Probability ◽  
Female Patients ◽  
Cancer Survival Data

In this paper we test the statistical probability models for breast cancer survival data for race and ethnicity. Data was collected from breast cancer patients diagnosed in United States during the years 1973–2009. We selected a stratified random sample of Black Hispanic female patients from the Surveillance Epidemiology and End Results (SEER) database to derive the statistical probability models. We used three common model building criteria which include Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) to measure the goodness of fit tests and it was found that Black Hispanic female patients survival data better fit the exponentiated exponential probability model. A novel Bayesian method was used to derive the posterior density function for the model parameters as well as to derive the predictive inference for future response. We specifically focused on Black Hispanic race. Markov Chain Monte Carlo (MCMC) method was used for obtaining the summary results of posterior parameters. Additionally, we reported predictive intervals for future survival times. These findings would be of great significance in treatment planning and healthcare resource allocation.

scholarly journals Bayesian Method for Modeling Male Breast Cancer Survival Data

2014 ◽  
Vol 15 (2) ◽  
pp. 663-669 ◽  
Author(s):  
Hafiz Mohammad Rafiqullah Khan ◽  
Anshul Saxena ◽  
Sagar Rana ◽  
Nasar Uddin Ahmed
Keyword(s):  
Breast Cancer ◽  
Male Breast Cancer ◽  
Survival Data ◽  
Bayesian Method ◽  
Cancer Survival ◽  
Breast Cancer Survival ◽  
Male Breast ◽  
Cancer Survival Data
Sign in / Sign up

Export Citation Format

Share Document