The Use of Split Exponential and Split Weibull Analyse Survival Data With Long Term Survivors

The split population model is a flexible way of extending the standard survival analytical methods to failure time data in which susceptibles and long-term survivors coexist. Susceptibles would develop the event with certainty if complete follow-up were possible, but the long-term survivors would...

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Main Author: Rahmatina, Desi
Format: Thesis
Language:English
English
Published: 2005
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Online Access:http://psasir.upm.edu.my/id/eprint/6213/1/FS_2005_12.pdf
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spelling my-upm-ir.62132023-10-16T03:26:30Z The Use of Split Exponential and Split Weibull Analyse Survival Data With Long Term Survivors 2005-12 Rahmatina, Desi The split population model is a flexible way of extending the standard survival analytical methods to failure time data in which susceptibles and long-term survivors coexist. Susceptibles would develop the event with certainty if complete follow-up were possible, but the long-term survivors would never experience the event. A study was conducted to allow the effects of covariates on the probability that an individual is immune, and the immune probability vary from individual to individual. In effect, we are associating with each individual a distinct probability of being immune, which depends on the covariate information specific to that individual. And then fitted a few models using the maximum likelihood estimation to determine whether the covariates are significant or not. Several popular distributions on the survival data analysis as endorsed by graphical techniques were used. We applied the split exponential and the split Weibull models together with deviance test, a parametric test for the presence of immunes, and a test for outlier, to test for sufficient follow-up in the samples where there may or may not be immunes presences. We presented the probability of eventual immune for the ith individual as the logit model and logistic model. We will work with two data sets, firstly a Clinical Trial in the Treatment of Carcinoma of the Oropharynx and secondly Stanford Heart Transplant data. The results from the data analyses for a Clinical Trial in the Treatment of Carcinoma of the Oropharynx data show that the simple exponential model produces a fit not significantly worse than the simple Weibull model and the simple split Weibull model no better than the simple split exponential model, also shown that no evidence of immune population and all covariates are not significant. The results from the data analyses for Stanford Heart Transplant data show that the simple Weibull model is significantly better than the simple exponential model, and the simple split Weibull model is better than the simple split exponential model. We have calculated the maximum log-likelihood function value for both the logit exponential and logistic exponential models. They are exactly similar for both the Clinical Trial in the Treatment of Carcinoma of the Oropharynx and Stanford Heart Transplant data. So, we suggest that both the logit exponential and logistic exponential models are equally superior. Survival analysis (Biometry) - Case studies 2005-12 Thesis http://psasir.upm.edu.my/id/eprint/6213/ http://psasir.upm.edu.my/id/eprint/6213/1/FS_2005_12.pdf text en public masters Universiti Putra Malaysia Survival analysis (Biometry) - Case studies Science Abu Bakar, Mohd Rizam English
institution Universiti Putra Malaysia
collection PSAS Institutional Repository
language English
English
advisor Abu Bakar, Mohd Rizam
topic Survival analysis (Biometry) - Case studies


spellingShingle Survival analysis (Biometry) - Case studies


Rahmatina, Desi
The Use of Split Exponential and Split Weibull Analyse Survival Data With Long Term Survivors
description The split population model is a flexible way of extending the standard survival analytical methods to failure time data in which susceptibles and long-term survivors coexist. Susceptibles would develop the event with certainty if complete follow-up were possible, but the long-term survivors would never experience the event. A study was conducted to allow the effects of covariates on the probability that an individual is immune, and the immune probability vary from individual to individual. In effect, we are associating with each individual a distinct probability of being immune, which depends on the covariate information specific to that individual. And then fitted a few models using the maximum likelihood estimation to determine whether the covariates are significant or not. Several popular distributions on the survival data analysis as endorsed by graphical techniques were used. We applied the split exponential and the split Weibull models together with deviance test, a parametric test for the presence of immunes, and a test for outlier, to test for sufficient follow-up in the samples where there may or may not be immunes presences. We presented the probability of eventual immune for the ith individual as the logit model and logistic model. We will work with two data sets, firstly a Clinical Trial in the Treatment of Carcinoma of the Oropharynx and secondly Stanford Heart Transplant data. The results from the data analyses for a Clinical Trial in the Treatment of Carcinoma of the Oropharynx data show that the simple exponential model produces a fit not significantly worse than the simple Weibull model and the simple split Weibull model no better than the simple split exponential model, also shown that no evidence of immune population and all covariates are not significant. The results from the data analyses for Stanford Heart Transplant data show that the simple Weibull model is significantly better than the simple exponential model, and the simple split Weibull model is better than the simple split exponential model. We have calculated the maximum log-likelihood function value for both the logit exponential and logistic exponential models. They are exactly similar for both the Clinical Trial in the Treatment of Carcinoma of the Oropharynx and Stanford Heart Transplant data. So, we suggest that both the logit exponential and logistic exponential models are equally superior.
format Thesis
qualification_level Master's degree
author Rahmatina, Desi
author_facet Rahmatina, Desi
author_sort Rahmatina, Desi
title The Use of Split Exponential and Split Weibull Analyse Survival Data With Long Term Survivors
title_short The Use of Split Exponential and Split Weibull Analyse Survival Data With Long Term Survivors
title_full The Use of Split Exponential and Split Weibull Analyse Survival Data With Long Term Survivors
title_fullStr The Use of Split Exponential and Split Weibull Analyse Survival Data With Long Term Survivors
title_full_unstemmed The Use of Split Exponential and Split Weibull Analyse Survival Data With Long Term Survivors
title_sort use of split exponential and split weibull analyse survival data with long term survivors
granting_institution Universiti Putra Malaysia
granting_department Science
publishDate 2005
url http://psasir.upm.edu.my/id/eprint/6213/1/FS_2005_12.pdf
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