In this paper, survival data analysis is realized by applying Generalized Entropy Optimization Methods (GEOM

Objective: Entropy Optimization Methods (EOM) have important applications, especially in statistics, economy, engineering, survival data analysis and etc. There are several examples in the literature that known statistical data do not conform to theoretical distributions, however do conform the entropy optimization distributions well. In the present study, survival data of male patients with localized cancer of a rectum diagnosed in Connecticut from 1935 to 1944 is analyzed by using Generalized Entropy Optimization Methods (GEOM) in the form of the MinMinxEnt and the MaxMinxEnt methods. Material and Methods: The MinMinxEnt and the MaxMinxEnt methods have suggested distributions in the form of the MinMinxEnt, the MaxMinxEnt distributions which are closest to statistical data and furthest from statistical data in the sense of Kullback-Leibler measure, respectively. Results: The results are acquired by using statistical software MATLAB. The performances of MinMixEnt and MaxMinxEnt methods are established by Chi-Square, Root Mean Square Error (RMSE) and Kullback-Leibler criteria. It is shown that is better than distribution in the sense of Kullback-Leibler measure to mentioned data. Furthermore, in the sense of RMSE criteria distribution is more suitable for statistical data than distribution. These results are also corroborated by graphical representation. Conclusion: In this study, it is shown that and distributions more successfully represent Survival Data. Our investigation indicates that GEOM in survival data analysis yields reasonable results.