Analisis Survival dengan Model Regresi pada Data Tersensor Berdistribusi Log-Logistik

Abstract

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+θ₁x_i1+...+θ_ix_ij+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 y_i=1.92458+0.0242393 x_i1+0.639037Ô‘_i.