Comparison of Different Parametric Modeling for Time-to-Event Data Among Cancer Patients

K. Srividhya¹ , A. Radhika²

Section:Research Paper, Product Type: Isroset-Journal
Vol.6 , Issue.1 , pp.187-192, Feb-2019

CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v6i1.187192

Online published on Feb 28, 2019

Copyright © K. Srividhya, A. Radhika . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Parametric models are widely used in the modelling of survival data under various diseases. These parametric models were applied to the data of 350 patients of uterus cancer. The main objective of this paper is to compare the results of survival analysis of uterus cancer patients by using different parametric models like Exponential distribution, Weibull distribution, Gompertz distribution, Log - Normal distribution, Log - Logistic distribution and Generalized Gamma distribution by two approaches, the one by Deviance method and the other by Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values. As a result except Exponential and Gompertz distribution all the other distributions gives approximately relative results by these two methods. The model selection of the data is carried out by using Statistical Software STATA 12.

Key-Words / Index Term :
Survival analysis, Deviance, AIC and BIC values, Uterus Cancer, Parametric Models

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