Parametric Models in Survival Analysis for Lung Cancer Patients

The aim of this study is to estimate the survival function for the data of lung cancer patients, using parametric methods (Weibull, Gumbel, exponential and log-logistic). Comparisons between the proposed estimation method have been performed using statistical indicator Akaike information Criterio...

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Main Author: Layla A. Ahmed
Format: Article
Language:English
Published: University of Baghdad 2021-04-01
Series:Ibn Al-Haitham Journal for Pure and Applied Sciences
Subjects:
Online Access:https://jih.uobaghdad.edu.iq/index.php/j/article/view/2617
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author Layla A. Ahmed
author_facet Layla A. Ahmed
author_sort Layla A. Ahmed
collection DOAJ
description The aim of this study is to estimate the survival function for the data of lung cancer patients, using parametric methods (Weibull, Gumbel, exponential and log-logistic). Comparisons between the proposed estimation method have been performed using statistical indicator Akaike information Criterion, Akaike information criterion corrected and Bayesian information Criterion, concluding that the survival function for the lung cancer by using Gumbel distribution model is the best. The expected values of the survival function of all estimation methods that are proposed in this study have been decreasing gradually with increasing failure times for lung cancer patients, which means that there is an opposite relationship failure times and survival function.
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spelling doaj.art-41f57f8c5e774c569978c78bf204fbac2022-12-22T02:54:07ZengUniversity of BaghdadIbn Al-Haitham Journal for Pure and Applied Sciences1609-40422521-34072021-04-0134210811810.30526/34.2.26172522Parametric Models in Survival Analysis for Lung Cancer PatientsLayla A. AhmedThe aim of this study is to estimate the survival function for the data of lung cancer patients, using parametric methods (Weibull, Gumbel, exponential and log-logistic). Comparisons between the proposed estimation method have been performed using statistical indicator Akaike information Criterion, Akaike information criterion corrected and Bayesian information Criterion, concluding that the survival function for the lung cancer by using Gumbel distribution model is the best. The expected values of the survival function of all estimation methods that are proposed in this study have been decreasing gradually with increasing failure times for lung cancer patients, which means that there is an opposite relationship failure times and survival function.https://jih.uobaghdad.edu.iq/index.php/j/article/view/2617survival analysis, weibull distribution, gumbel distribution, exponential distribution, log-logistic distribution
spellingShingle Layla A. Ahmed
Parametric Models in Survival Analysis for Lung Cancer Patients
Ibn Al-Haitham Journal for Pure and Applied Sciences
survival analysis, weibull distribution, gumbel distribution, exponential distribution, log-logistic distribution
title Parametric Models in Survival Analysis for Lung Cancer Patients
title_full Parametric Models in Survival Analysis for Lung Cancer Patients
title_fullStr Parametric Models in Survival Analysis for Lung Cancer Patients
title_full_unstemmed Parametric Models in Survival Analysis for Lung Cancer Patients
title_short Parametric Models in Survival Analysis for Lung Cancer Patients
title_sort parametric models in survival analysis for lung cancer patients
topic survival analysis, weibull distribution, gumbel distribution, exponential distribution, log-logistic distribution
url https://jih.uobaghdad.edu.iq/index.php/j/article/view/2617
work_keys_str_mv AT laylaaahmed parametricmodelsinsurvivalanalysisforlungcancerpatients