DeepWei-Cu: A Deep Weibull Network for Cure Fraction Models
Introduction: Survival analysis including cure fraction subgroups is heavily used in different fields like economics, engineering and medicine. The main core of the analysis is to understand the relationship between the covariates and the survival function taking into consideration censoring and lo...
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| Format: | Article |
| Language: | English |
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Tehran University of Medical Sciences
2024-12-01
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| Series: | Journal of Biostatistics and Epidemiology |
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| Online Access: | https://jbe.tums.ac.ir/index.php/jbe/article/view/1425 |
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| author | Ola Abuelamayem |
| author_facet | Ola Abuelamayem |
| author_sort | Ola Abuelamayem |
| collection | DOAJ |
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Introduction: Survival analysis including cure fraction subgroups is heavily used in different fields like economics, engineering and medicine. The main core of the analysis is to understand the relationship between the covariates and the survival function taking into consideration censoring and long-term survival. The analysis can be performed using traditional statistical models or neural networks. Recently, neural network has attracted attention in analyzing lifetime data due to its ability of efficiently estimating the survival function under the existence of complex covariates. To the best of our knowledge, this is the first time a parametric neural network is introduced to analyze mixture cure fraction models.
Methods: In this paper, we introduce a novel neural network based on mixture cure fraction Weibull loss function.
Results: Alzheimer disease dataset as long as synthetic dataset are used to study the efficiency of the model. We compared the results using goodness of fit methods in both datasets with Weibull regression.
Conclusion: The proposed neural network has the flexibility of analyzing continuous data without discretization. Also, it has the advantage of using Weibull distribution properties. For example, it can analyze data with different hazard rates (monotonically decreasing, monotonically increasing and constant). comparing the results with Weibull regression, the proposed neural network performed better.
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| format | Article |
| id | doaj-art-3def6c0f8263498487d35d58c67edb29 |
| institution | Kabale University |
| issn | 2383-4196 2383-420X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Tehran University of Medical Sciences |
| record_format | Article |
| series | Journal of Biostatistics and Epidemiology |
| spelling | doaj-art-3def6c0f8263498487d35d58c67edb292025-01-06T08:40:29ZengTehran University of Medical SciencesJournal of Biostatistics and Epidemiology2383-41962383-420X2024-12-0110110.18502/jbe.v10i1.17153DeepWei-Cu: A Deep Weibull Network for Cure Fraction ModelsOla Abuelamayem0Dr Introduction: Survival analysis including cure fraction subgroups is heavily used in different fields like economics, engineering and medicine. The main core of the analysis is to understand the relationship between the covariates and the survival function taking into consideration censoring and long-term survival. The analysis can be performed using traditional statistical models or neural networks. Recently, neural network has attracted attention in analyzing lifetime data due to its ability of efficiently estimating the survival function under the existence of complex covariates. To the best of our knowledge, this is the first time a parametric neural network is introduced to analyze mixture cure fraction models. Methods: In this paper, we introduce a novel neural network based on mixture cure fraction Weibull loss function. Results: Alzheimer disease dataset as long as synthetic dataset are used to study the efficiency of the model. We compared the results using goodness of fit methods in both datasets with Weibull regression. Conclusion: The proposed neural network has the flexibility of analyzing continuous data without discretization. Also, it has the advantage of using Weibull distribution properties. For example, it can analyze data with different hazard rates (monotonically decreasing, monotonically increasing and constant). comparing the results with Weibull regression, the proposed neural network performed better. https://jbe.tums.ac.ir/index.php/jbe/article/view/1425Cure fractionWeibull distributionDeep learningNeural NetworkRandom Censoring. |
| spellingShingle | Ola Abuelamayem DeepWei-Cu: A Deep Weibull Network for Cure Fraction Models Journal of Biostatistics and Epidemiology Cure fraction Weibull distribution Deep learning Neural Network Random Censoring. |
| title | DeepWei-Cu: A Deep Weibull Network for Cure Fraction Models |
| title_full | DeepWei-Cu: A Deep Weibull Network for Cure Fraction Models |
| title_fullStr | DeepWei-Cu: A Deep Weibull Network for Cure Fraction Models |
| title_full_unstemmed | DeepWei-Cu: A Deep Weibull Network for Cure Fraction Models |
| title_short | DeepWei-Cu: A Deep Weibull Network for Cure Fraction Models |
| title_sort | deepwei cu a deep weibull network for cure fraction models |
| topic | Cure fraction Weibull distribution Deep learning Neural Network Random Censoring. |
| url | https://jbe.tums.ac.ir/index.php/jbe/article/view/1425 |
| work_keys_str_mv | AT olaabuelamayem deepweicuadeepweibullnetworkforcurefractionmodels |