Analysis of survival in breast cancer patients by using different parametric models
In biomedical applications or clinical trials, right censoring was often arising when studying the time to event data. In this case, some individuals are still alive at the end of the study or lost to follow up at a certain time. It is an important issue to handle the censoring data in order to prev...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2017
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| Online Access: | http://eprints.uthm.edu.my/5168/ http://eprints.uthm.edu.my/5168/1/AJ%202017%20%28298%29%20Analysis%20of%20survival%20in%20breast%20cancer%20patients.pdf |
| _version_ | 1848888483056189440 |
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| author | Amran, Syahila Enera Abdullah, M Asrul Afendi Kek, Sie Long Muhamad Jamil, Siti Afiqah |
| author_facet | Amran, Syahila Enera Abdullah, M Asrul Afendi Kek, Sie Long Muhamad Jamil, Siti Afiqah |
| author_sort | Amran, Syahila Enera |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | In biomedical applications or clinical trials, right censoring was often arising when studying the time to event data. In this case, some individuals are still alive at the end of the study or lost to follow up at a certain time. It is an important issue to handle the censoring data in order to prevent any bias information in the analysis. Therefore, this study was carried out to analyze the right censoring data with three different parametric models; exponential model, Weibull model and log-logistic models. Data of breast cancer patients from Hospital Sultan Ismail, Johor Bahru from 30 December 2008 until 15 February 2017 was used in this study to illustrate the right censoring data. Besides, the covariates included in this study are the time of breast cancer infection patients survive t, age of each patients X1 and treatment given to the patients X2. In order to determine the best parametric models in analysing survival of breast cancer patients, the performance of each model was compare based on Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and log-likelihood value using statistical software R. When analysing the breast cancer data, all three distributions were shown consistency of data with the line graph of cumulative hazard function resembles a straight line going through the origin. As the result, log-logistic model was the best fitted parametric model compared with exponential and Weibull model since it has the smallest value in AIC and BIC, also the biggest value in log-likelihood. |
| first_indexed | 2025-11-15T20:11:00Z |
| format | Article |
| id | uthm-5168 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:11:00Z |
| publishDate | 2017 |
| publisher | IOP Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-51682022-01-06T03:45:56Z http://eprints.uthm.edu.my/5168/ Analysis of survival in breast cancer patients by using different parametric models Amran, Syahila Enera Abdullah, M Asrul Afendi Kek, Sie Long Muhamad Jamil, Siti Afiqah R855-855.5 Medical technology In biomedical applications or clinical trials, right censoring was often arising when studying the time to event data. In this case, some individuals are still alive at the end of the study or lost to follow up at a certain time. It is an important issue to handle the censoring data in order to prevent any bias information in the analysis. Therefore, this study was carried out to analyze the right censoring data with three different parametric models; exponential model, Weibull model and log-logistic models. Data of breast cancer patients from Hospital Sultan Ismail, Johor Bahru from 30 December 2008 until 15 February 2017 was used in this study to illustrate the right censoring data. Besides, the covariates included in this study are the time of breast cancer infection patients survive t, age of each patients X1 and treatment given to the patients X2. In order to determine the best parametric models in analysing survival of breast cancer patients, the performance of each model was compare based on Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and log-likelihood value using statistical software R. When analysing the breast cancer data, all three distributions were shown consistency of data with the line graph of cumulative hazard function resembles a straight line going through the origin. As the result, log-logistic model was the best fitted parametric model compared with exponential and Weibull model since it has the smallest value in AIC and BIC, also the biggest value in log-likelihood. IOP Publishing 2017 Article PeerReviewed text en http://eprints.uthm.edu.my/5168/1/AJ%202017%20%28298%29%20Analysis%20of%20survival%20in%20breast%20cancer%20patients.pdf Amran, Syahila Enera and Abdullah, M Asrul Afendi and Kek, Sie Long and Muhamad Jamil, Siti Afiqah (2017) Analysis of survival in breast cancer patients by using different parametric models. Journal of Physics: Conference Series, 890 (012169). pp. 1-8. ISSN 1742-6588 http://dx.doi.org/10.1088/1742-6596/890/1/012169 |
| spellingShingle | R855-855.5 Medical technology Amran, Syahila Enera Abdullah, M Asrul Afendi Kek, Sie Long Muhamad Jamil, Siti Afiqah Analysis of survival in breast cancer patients by using different parametric models |
| title | Analysis of survival in breast cancer patients by using different parametric models |
| title_full | Analysis of survival in breast cancer patients by using different parametric models |
| title_fullStr | Analysis of survival in breast cancer patients by using different parametric models |
| title_full_unstemmed | Analysis of survival in breast cancer patients by using different parametric models |
| title_short | Analysis of survival in breast cancer patients by using different parametric models |
| title_sort | analysis of survival in breast cancer patients by using different parametric models |
| topic | R855-855.5 Medical technology |
| url | http://eprints.uthm.edu.my/5168/ http://eprints.uthm.edu.my/5168/ http://eprints.uthm.edu.my/5168/1/AJ%202017%20%28298%29%20Analysis%20of%20survival%20in%20breast%20cancer%20patients.pdf |