Nonlinear Survival Regression Using Artificial Neural Network
Survival analysis methods deal with a type of data, which is waiting time till occurrence of an event. One common method to analyze this sort of data is Cox regression. Sometimes, the underlying assumptions of the model are not true, such as nonproportionality for the Cox model. In model building, c...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2013-01-01
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| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2013/753930 |
| _version_ | 1798044465100750848 |
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| author | Akbar Biglarian Enayatollah Bakhshi Ahmad Reza Baghestani Mahmood Reza Gohari Mehdi Rahgozar Masoud Karimloo |
| author_facet | Akbar Biglarian Enayatollah Bakhshi Ahmad Reza Baghestani Mahmood Reza Gohari Mehdi Rahgozar Masoud Karimloo |
| author_sort | Akbar Biglarian |
| collection | DOAJ |
| description | Survival analysis methods deal with a type of data, which is waiting time till occurrence of an event. One common method to analyze this sort of data is Cox regression. Sometimes, the underlying assumptions of the model are not true, such as nonproportionality for the Cox model. In model building, choosing an appropriate model depends on complexity and the characteristics of the data that effect the appropriateness of the model. One strategy, which is used nowadays frequently, is artificial neural network (ANN) model which needs a minimal assumption. This study aimed to compare predictions of the ANN and Cox models by simulated data sets, which the average censoring rate were considered 20% to 80% in both simple and complex model. All simulations and comparisons were performed by R 2.14.1. |
| first_indexed | 2024-04-11T23:04:16Z |
| format | Article |
| id | doaj.art-b9a487fac9cb4ecbaba0f8e6f04703b0 |
| institution | Directory Open Access Journal |
| issn | 1687-952X 1687-9538 |
| language | English |
| last_indexed | 2024-04-11T23:04:16Z |
| publishDate | 2013-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | Journal of Probability and Statistics |
| spelling | doaj.art-b9a487fac9cb4ecbaba0f8e6f04703b02022-12-22T03:58:03ZengHindawi LimitedJournal of Probability and Statistics1687-952X1687-95382013-01-01201310.1155/2013/753930753930Nonlinear Survival Regression Using Artificial Neural NetworkAkbar Biglarian0Enayatollah Bakhshi1Ahmad Reza Baghestani2Mahmood Reza Gohari3Mehdi Rahgozar4Masoud Karimloo5Department of Biostatistics, University of Social Welfare and Rehabilitation Sciences (USWRS), Tehran 1985713834, IranDepartment of Biostatistics, University of Social Welfare and Rehabilitation Sciences (USWRS), Tehran 1985713834, IranDepartment of Biostatistics, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran 1971653313, IranHospital Management Research Center, Tehran University of Medical Sciences (TUMS), Tehran 1996713883, IranDepartment of Biostatistics, University of Social Welfare and Rehabilitation Sciences (USWRS), Tehran 1985713834, IranDepartment of Biostatistics, University of Social Welfare and Rehabilitation Sciences (USWRS), Tehran 1985713834, IranSurvival analysis methods deal with a type of data, which is waiting time till occurrence of an event. One common method to analyze this sort of data is Cox regression. Sometimes, the underlying assumptions of the model are not true, such as nonproportionality for the Cox model. In model building, choosing an appropriate model depends on complexity and the characteristics of the data that effect the appropriateness of the model. One strategy, which is used nowadays frequently, is artificial neural network (ANN) model which needs a minimal assumption. This study aimed to compare predictions of the ANN and Cox models by simulated data sets, which the average censoring rate were considered 20% to 80% in both simple and complex model. All simulations and comparisons were performed by R 2.14.1.http://dx.doi.org/10.1155/2013/753930 |
| spellingShingle | Akbar Biglarian Enayatollah Bakhshi Ahmad Reza Baghestani Mahmood Reza Gohari Mehdi Rahgozar Masoud Karimloo Nonlinear Survival Regression Using Artificial Neural Network Journal of Probability and Statistics |
| title | Nonlinear Survival Regression Using Artificial Neural Network |
| title_full | Nonlinear Survival Regression Using Artificial Neural Network |
| title_fullStr | Nonlinear Survival Regression Using Artificial Neural Network |
| title_full_unstemmed | Nonlinear Survival Regression Using Artificial Neural Network |
| title_short | Nonlinear Survival Regression Using Artificial Neural Network |
| title_sort | nonlinear survival regression using artificial neural network |
| url | http://dx.doi.org/10.1155/2013/753930 |
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