RT - Journal Article
T1 - Use of Frailty Propertional Risk Rate Model in Real Data Analysis
JF - Andishe-_ye-Amari
YR - 2021
JO - Andishe-_ye-Amari
VO - 25
IS - 1
UR - http://andisheyeamari.irstat.ir/article-1-790-en.html
SP - 25
EP - 31
K1 - Likelihood Maximum Estimator
K1 - Baseline Distribution
K1 - Unconditional Survival Functions
K1 - Frailty Model .
AB - Many populations encountered in survival analysis are often not homogeneous. Individuals are flexible in their susceptibility to causes of death, response to treatment, and influence of various risk factors. Ignoring this heterogeneity can result in misleading conclusions. To deal with these problems, the proportional hazard frailty model was introduced. In this paper, the frailty model is explained as the product of the frailty random variable and baseline hazard rate. We examine the fit of the frailty model to the right-censored data from in the presence of explanatory variables (observable variables) and use it as a practical example to fit the frailty model to the data by considering the Weibull basis distribution and exponential in the likelihood functions. It is used to estimate the model parameters and compare the fit of the models with different criteria.
LA eng
UL http://andisheyeamari.irstat.ir/article-1-790-en.html
M3
ER -