Open AccessAnalisis Survival dengan Model Regresi pada Data Tersensor Berdistribusi Log-Logistik
Author(s) -
Gatri Eka Kusumawardhani,
Vera Maya Santi,
Suyono Suyono
Publication year - 2018
Publication title -
jurnal statistika dan aplikasinya
Language(s) - English
Resource type - Journals
ISSN - 2620-8369
DOI - 10.21009/jsa.02204
Subject(s) - logistic regression , statistics , regression analysis , factor regression model , mathematics , survival analysis , log logistic distribution , logistic model tree , proper linear model , polynomial regression , probability distribution , distribution fitting
Survival analysis is an analysis used to determine the length of time required by an object in order to survive. That time is sometimes influenced by several factors called independent variables. One way to know relationship is through a regression model. The dependent variable in this regression model is a survival time which is log-logistic distributed. The data used in this study were right censored survival data. Log-logistic regression models for survival data can be expressed by transformation Y=lnT= θ0+θ1xi1+...+θixij+σԑ. The parameter of the log-logistic regression models for right censored survival data are estimated with the maximum likelihood method. In this study, the application of log-logistic regression model for survival data is in data of lung cancer patients. Based on the data already performed, best log-logistic regression model is obtained yi=1.92458+0.0242393 xi1+0.639037ԑi.