alexa Confidence Intervals Estimation for Survival Function i
ISSN: 2155-6180

Journal of Biometrics & Biostatistics
Open Access

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Research Article

Confidence Intervals Estimation for Survival Function in Log-Logistic Distribution and Proportional Odds Regression Based on Censored Survival Time Data

Kamil ALAKUŞ*, Necati Alp ERİLLİ

Ondokuz Mayıs University, Faculty of science and Arts, Department of Statistics, 55139 Turkey

*Corresponding Author:
Ondokuz Mayıs University
Faculty of science and Arts
Department of Statistics
55139 Turkey
E-mail: [email protected]

Received date: December 21, 2010; Accepted date: June 06, 2011; Published date: September 25, 2011

Citation: ALAKUS K, ERILLI NA (2011) Confidence Intervals Estimation for Survival Function in Log-Logistic Distribution and Proportional Odds Regression Based on Censored Survival Time Data. J Biomet Biostat 2:116. doi: 10.4172/2155-6180.1000116

Copyright: © 2011 ALAKUS K, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.



Log-logistic and Weibull distributions have both accelerated survival time property. The log-logistic distribution has also proportional odds property. Log-logistic distribution has unimodal hazard curve which changes direction. Link [6,7] presented a confidence interval estimate of survival function using Cox\'s proportional hazard model with covariates. Her idea more recently extended by [1] to the exponential distribution and [2] to exponential proportional hazard model, respectively. The same idea has been extended to the Weibull proportional hazard regression model by [3]. In this study, it is formed on confidence interval for log-logistic distribution survival function for any values of the time provided that the survival times have a log-logistic distributed random variable. It is also extended the same results to the proportional odds regression. A Real time data and a simulation data examples are also considered in the study for illustration the discussed confidence interval.


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