scholarly journals Bayesian Estimation of Cox Proporional Hazard model under A Piecewise Constant model for Baseline Hazard and the problem of Survival Time Axis Grid

2019 ◽  
Vol 6 (1) ◽  
pp. 551-563
Author(s):  
Chellai Fatih ◽  
Boudrissa Naima
Keyword(s):  
Survival Time ◽  
Bayesian Estimation ◽  
Hazard Model ◽  
Time Axis ◽  
Piecewise Constant ◽  
Baseline Hazard ◽  
Piecewise Constant Model

scholarly journals Cox proportion hazard model for patients with hepatitis disease in Iraq

2009 ◽  
Vol 6 (3) ◽  
pp. 612-617
Author(s):  
Baghdad Science Journal
Keyword(s):  
Regression Model ◽  
Survival Time ◽  
Cox Regression ◽  
Survival Function ◽  
Hazard Model ◽  
Cox Regression Model ◽  
Hepatic Diseases ◽  
Kaplan Meier

Cox regression model have been used to estimate proportion hazard model for patients with hepatitis disease recorded in Gastrointestinal and Hepatic diseases Hospital in Iraq for (2002 -2005). Data consists of (age, gender, survival time terminal stat). A Kaplan-Meier method has been applied to estimate survival function and hazerd function.

scholarly journals Cox Piecewise Constant Hazard Model with Bayesian Method

2021 ◽  
pp. 83-95
Author(s):  
Amanda Putri Tiyas Pratiwi ◽  
Sarini Abdullah ◽  
Ida Fithriani
Keyword(s):  
Bayesian Method ◽  
Survival Models ◽  
Posterior Density ◽  
Hazard Functions ◽  
Piecewise Constant ◽  
Baseline Hazard ◽  
Explanatory Variables ◽  
Time To Event Data ◽  
Dependent Component ◽  
Main Components

Cox PH model is one of the survival models that is widely used for analyzing time-to-event data. Cox PH model consists of two main components, the baseline hazard consisting of time-dependent component; and the exponential function accomodating explanatory variables. The baseline hazard is not estimated in the Cox PH model, thus not accommodating the need for hazard rate estimation. Therefore, in this paper we discuss the estimation of baseline hazard through piecewise constant hazard using Bayesian method. Gamma distribution is assumed for the piecewise constant baseline hazard, and normal distribution is assumed for the regression coefficient. Sampling from the posterior is conducted using Markov chain Monte Carlo through Gibbs sampling. Echocardiogram data containing 106 observations and 6 explanatory variables were used in analysis. The result showed that the baseline hazard functions were estimated and each of parameters in the model is converged as shown by the trace plot and posterior density plot.    

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